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IGFL BioInfo Old Shack

DESeq2: tips and tricks

package version: 1.36.0

by Gilquin Laurent 106 min read

Introduction

This document presents tips and tricks for the use of the R package DESeq2(Love et al., 2014) through a case study. The objective is to help the user acquire a better understanding of the package, in particular:

  • its standard workflow,
  • what each function does,
  • how to visualize and interpret the results.

The document contains encapsulated block codes used to produce figures and results. This code can be directly copied and pasted into the console of your choice (R or Rstudio).

The package \(\bf{\texttt{DESeq2}}\) provides methods to analyse counts data from high-throughput sequencing assays such as: RNA-seq, ChIP-Seq, HiC, shRNA screening, mass spectrometry and more broadly NGS metagenomics or any comparison of count tables. The package builds on statistical inference and hypothesis testing to detect differentially expressed genes.

The whole methodology can perform well with as few as two or three biological replicates by pooling information across genes. However, the robustness of the statistical inference greatly depends on the overall number of genes at hand. \(\bf{\texttt{DESeq2}}\) heavily relies on the negative Binomial distribution which offers a more realistic model for the variability of counts data than the Poisson distribution(Di et al., 2011).

Installation

To run the block codes of this note, you can download the R project at this address and follow the installation instructions.

Aside from \(\bf{\texttt{DESeq2}}\), two additional packages are installed through the package manager Bioconductor:

  • \(\bf{\texttt{EnhancedVolcano}}\): an easy customization of Volcano plots to visualize the results of differential expression analyses,
  • \(\bf{\texttt{vsn}}\): a variance stabilizing data transformation.

Other CRAN dependencies are installed, some of them are used to produce this note, such as:

  • \(\bf{\texttt{bslib}}\): customize bootstrap Sass themes for shiny and Rmarkdown,
  • \(\bf{\texttt{dplyr}}\): manipulate and transform data frame like objects,
  • \(\bf{\texttt{glmpca}}\): perform dimension reduction of non-normally distributed data,
  • \(\bf{\texttt{ggridges}}\): create ridgeline plots in ggplot2,
  • \(\bf{\texttt{gridExtra}}\): arrange and combine ggplot2 graphs,
  • \(\bf{\texttt{kableExtra}}\): customize HTML tables created with knitr::kable,
  • \(\bf{\texttt{knitr}}\): an engine for dynamic report generation,
  • \(\bf{\texttt{import}}\): use external functionality in R scripts differently,
  • \(\bf{\texttt{latex2exp}}\): include laTeX formulas in graphics,
  • \(\bf{\texttt{magrittr}}\): provide a forward-pipe operator for R,
  • \(\bf{\texttt{pheatmap}}\): draw clustered heatmaps in ggplot2,
  • \(\bf{\texttt{rmarkdown}}\): weave together narrative text and code to produce formatted outputs,
  • \(\bf{\texttt{showtext}}\): import and use fonts more easily in graphics,
  • \(\bf{\texttt{tidyr}}\): reshape and transform data frame like objects.

Resources

In-depth guides of \(\bf{\texttt{DESeq2}}\) and \(\bf{\texttt{EnhancedVolcano}}\) packages are available in the form of R vignettes:

An end-to-end walkthrough of RNA-seq differential expression analysis is also proposed in the following R vignette:

The latter notably deals with the construction of the count matrix, a subject that is not addressed by the present document.

Teaching materials from a differential gene expression (DGE) analysis workshop on RNA-Seq data can be found here:

Loading the packages

Warning 

Before loading the packages, the current working directory must be set to the parent directory of the present file (either interactively or manually with the setwd function).

Packages and functions are loaded with the import package. This package is an alternative to the native library function. import provides an easier management of the environment, in particular, functions can be imported by name to avoid cluttering the environment:

# libraries for running the DESeq2 analysis
import::from(DESeq2, .all=TRUE)
import::from(magrittr, "%>%", "%<>%")
import::from("matrixStats", "rowProds", .character_only=TRUE)
import::from("SummarizedExperiment", c("assay", "assays"), .character_only=TRUE)
import::from("vsn", "meanSdPlot", .character_only=TRUE)

# libraries for data visualization
import::from("EnhancedVolcano", "EnhancedVolcano", .character_only=TRUE)
import::from(ggplot2, .all=TRUE)
import::from("glmpca", "glmpca", .character_only=TRUE)
import::from("ggridges", "geom_density_ridges", .character_only=TRUE)
import::from("gridExtra", "grid.arrange", .character_only=TRUE)
import::from("latex2exp", "TeX", .character_only=TRUE)
import::from("pheatmap", "pheatmap", .character_only=TRUE)
import::from("RColorBrewer", "brewer.pal", .character_only=TRUE)
import::from("showtext", "showtext_auto", .character_only=TRUE)

# local import
scripts_path <- "scripts"
source(file.path(scripts_path, "ggtheme.R")) ## ggplot2 custom theme
# load custom function for volcano plots (interactive or static)
use_bokeh <- TRUE
if (use_bokeh){
  volcano <- reticulate::import_from_path(
    "volcanoPlot", path=file.path(scripts_path, "Python/"), delay_load = TRUE
    )
  volcanoPlot <- volcano$volcanoPlot
} else {
    import::from(
      file.path(scripts_path, "volcanoPlot.R"),
      "volcanoPlot",
      .character_only=TRUE
      )
}

Case study: example

The present case study focuses on finding genes that are differentially expressed based on the number of protein-protein interactions they generate. An approach based on Open Reading Frames (ORFs) is used to identify the protein partners, referred to as ‘prey’-proteins, that interact with a bait-protein of interest. The number of counts for a given gene corresponds to the number of interactions formed between the bait-protein and its partners.

In the following, the variable condition will refer to a bait-protein investigated and the batch variable will refer to a biological replicate. Note that the terminologies condition and batch are quite universal, for instance in a medical study conditions would refer to treatments while batches would refer to patients. The bait-proteins of interest are the following:

  • Ctrl a bait-protein that acts as the control condition,
  • NoDrug, Drug1, Drug2, Drug3 that correspond to the bait-protein of interest with or without drug addition,
  • Alt a second bait-protein known to be independent of the others.

For the remainder of this document, a sample will refer to a pair (condition, batch).

Formatting the data : The DESeqDataSet object

Count matrix and sample information data

\(\bf{\texttt{DESeq2}}\) uses a custom class object called DESeqDataSet that stores the data required to perform the differential expression analysis, namely:

  • the file containing the count matrix (counts for all samples), here the first 6 rows of the matrix are shown. The value in the \(i\)-th row and the \(j\)-th column of the matrix tells how many reads/fragments can be assigned to gene \(i\) in sample \(j\). For other types of assays, the rows might refer either to binding regions for ChIP-Seq or to peptide sequences for quantitative mass spectrometry.

      cts <- read.table("data/cts.txt", header=T, row.names=1, sep="\t")
    Drug1.1 Drug1.2 Drug1.3 Drug2.1 Drug2.2 Drug2.3 Drug3.1 Drug3.2 Drug3.3 Alt.1 Alt.2 Alt.3 Ctrl.1 Ctrl.2 Ctrl.3 NoDrug.1 NoDrug.2 NoDrug.3
    ABHD12 253 416 449 374 383 362 219 96 217 410 386 453 326 307 436 304 239 515
    ABI2 0 0 0 0 0 0 25 0 5 0 0 0 0 0 0 0 0 0
    ABLIM1 186 192 103 191 158 136 150 149 210 376 388 239 233 144 276 187 129 270
    ABO 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 8 0 0
    ACAA2 39 56 77 39 101 31 93 23 30 34 23 46 9 9 130 16 44 42
    ACP1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

  • the file containing the table with the information about the samples, also called table of sample information.

      coldata <- read.table("data/coldata.txt", header=T, row.names=1, sep="\t")
    condition batch
    Drug1.1 Drug1 1
    Drug1.2 Drug1 2
    Drug1.3 Drug1 3
    Drug2.1 Drug2 1
    Drug2.2 Drug2 2
    Drug2.3 Drug2 3
    Drug3.1 Drug3 1
    Drug3.2 Drug3 2
    Drug3.3 Drug3 3
    Alt.1 Alt 1
    Alt.2 Alt 2
    Alt.3 Alt 3
    Ctrl.1 Ctrl 1
    Ctrl.2 Ctrl 2
    Ctrl.3 Ctrl 3
    NoDrug.1 NoDrug 1
    NoDrug.2 NoDrug 2
    NoDrug.3 NoDrug 3

Tip 

Tables can be interactively explored in RStudio with the View function.

Remarks

Warning 

If the data are not properly formatted, all subsequent analyses will be incorrect.

  1. Values in the count matrix must be un-normalized raw counts or estimated counts of sequencing reads/fragments.

  2. The columns of the count matrix must be a subset of the rows of the sample information table. This requirement can be checked with the following lines:

     stop_str <- "One or multiple rows of the sample information data 
     are missing from the count matrix columns."
 stopifnot(stop_str=all(rownames(coldata) %in% colnames(cts)))

    Furthermore, \(\bf{\texttt{DESeq2}}\) will not make guesses as to which column of the count matrix identifies to which row of the sample information table. It is up to the user to order the sample names in the count matrix and in the sample information table identically. Nonetheless, this ordering can still be enforced like so:

     if (nrow(coldata) >= ncol(cts)){
   coldata <- coldata[colnames(cts),]
 } else {
   cts <- cts[,rownames(coldata)]
 }

    A typical bad ordering would look like this, where there is a shift due to the wrong first three entries:

    table
    samples
    cts Drug1.1 Drug1.2 Drug1.3 Drug2.1 Drug2.2 Drug2.3 Drug3.1 Drug3.2 Drug3.3 Alt.1 Alt.2 Alt.3 Ctrl.1 Ctrl.2 Ctrl.3 NoDrug.1 NoDrug.2 NoDrug.3
    coldata NoDrug.1 NoDrug.2 NoDrug.3 Drug1.1 Drug1.2 Drug1.3 Drug2.1 Drug2.2 Drug2.3 Drug3.1 Drug3.2 Drug3.3 Alt.1 Alt.2 Alt.3 Ctrl.1 Ctrl.2 Ctrl.3

  3. Columns of the sample information table are expected to be categorical variables, e.g. variables that can take a finite number of values (characters or integers). This transformation can be performed as follows:

     for (idx in 1:length(names(coldata))){
   coldata[[idx]] <- factor(coldata[[idx]])
 }

Design

The DESeqDataSet has an associated design formula that tells which columns of the sample information table should be used to construct the experimental design. In the proposed case study, we want to measure the effect of the condition, controlling for batch differences:

dds <- DESeqDataSetFromMatrix(
  countData=cts, colData=coldata, design=~ batch + condition
  )

The order of the variables of the design (i.e. batch, condition) does not matter so long as the user specifies the comparison to allow for building of the results table. This is further discussed in Section 4.3.

The design matrix can be printed with the following commands:

designMat <- model.matrix(
  as.formula(paste(Reduce(paste, deparse(dds@design)))), dds@colData
  )
designMat
batches
conditions
(Intercept) 2 3 Crl Drug1 Drug2 Drug3 NoDrug
Drug1.1 1 0 0 0 1 0 0 0
Drug1.2 1 1 0 0 1 0 0 0
Drug1.3 1 0 1 0 1 0 0 0
Drug2.1 1 0 0 0 0 1 0 0
Drug2.2 1 1 0 0 0 1 0 0
Drug2.3 1 0 1 0 0 1 0 0
Drug3.1 1 0 0 0 0 0 1 0
Drug3.2 1 1 0 0 0 0 1 0
Drug3.3 1 0 1 0 0 0 1 0
Alt.1 1 0 0 0 0 0 0 0
Alt.2 1 1 0 0 0 0 0 0
Alt.3 1 0 1 0 0 0 0 0
Ctrl.1 1 0 0 1 0 0 0 0
Ctrl.2 1 1 0 1 0 0 0 0
Ctrl.3 1 0 1 1 0 0 0 0
NoDrug.1 1 0 0 0 0 0 0 1
NoDrug.2 1 1 0 0 0 0 0 1
NoDrug.3 1 0 1 0 0 0 0 1

The first batch and the condition Alt do not appear in the design columns because they can be inferred from the other batches and conditions values.

Exploratory analysis and visualization

Prior to running the differential expression analysis, it is often worth exploring sample relationships through data visualization methods. These methods are also practical tools to detect and remove samples (or even a whole condition) that would be detrimental if kept in the analysis.

All the methods introduced in this section are applied “blindly” without making use of the design formula (they might also be referred as unsupervised methods).

Pre-filtering

Pre-filtering low count genes consists of removing rows of the count matrix that have zero or only a few counts across all samples. This offers two benefits for studies involving a lot of genes:

  • it reduces the overall size of the count matrix (rows deletion),

  • it reduces the overall computational time of the differential expression analysis.

The most basic filtering rule consists of removing rows that have close to no counts across all samples (\(<10\) counts here):

rowDel <- rowSums(counts(dds))<10
print(paste(sum(rowDel),"rows out of", nrow(dds), "have been removed."))

## [1] "195 rows out of 1166 have been removed."

dds <- dds[!rowDel,]

Warning 

Here a threshold of 10 counts is chosen but the pre-filtering rule should be adapted according to the dataset and/or the biological study case.

Plotting counts

\(\bf{\texttt{DESeq2}}\) offers a utility function, called plotCounts to quickly visualize the number of counts for a given gene over groups of variables (here condition and batch) in intgroup. The function plotCounts normalizes counts by a set of coefficients called size factors (see Section 4.2.1) and as such required these coefficients to be estimated. This estimation of the size factors is performed by calling the estimateSizeFactors function of \(\bf{\texttt{DESeq2}}\).

The gene of interest (here the first one) can be specified either by its name (a string) or by its index (the row’s number). The function also provides a way to extract the results without plotting via the argument returnData.

dds_norm <- estimateSizeFactors(dds)
geneCounts <- plotCounts(dds_norm, gene=1, intgroup=c("batch", "condition"),
                         returnData=TRUE)
p <- ggplot(geneCounts, aes(x=condition, y=count, color=batch, group=batch)) +
  scale_y_log10() + geom_point(size=3) + geom_line(lty="longdash") +
  theme_custom()
p

**Normalized counts with lines connecting counts from the same batch**. A log scale is used for the y-axis.

Normalized counts with lines connecting counts from the same batch. A log scale is used for the y-axis.

Tip 

The function plotCounts is useful to highlight experimental effects (here batch effect) as DESeq2 can take into account such effects when testing for differential expression.

Violin plots and ridge plots

Violin and ridge plots are non-parametric statistical tools useful to compare raw counts per sample, across all genes. A violin plot is a mix between a boxplot and a density plot:

  • it can provide common summary statistics: median, interquartile range,

  • the outer shape of the violin displays the kernel density estimate fitted on the values (i.e. a smooth version of the values histogram).

Violin plots can be drawn with the geom_violin function of the \(\bf{\texttt{ggplot2}}\) package.

# format the data
sampleCounts <- data.frame(ID=rownames(dds),
                           count=c(assay(dds)),
                           batch=rep(coldata$batch, each=nrow(dds)),
                           condition=rep(coldata$condition, each=nrow(dds)))
# draw the violin
pos <- position_dodge(width=0.8)
ggplot(sampleCounts %>% dplyr::filter(count>0),
       aes(x=condition, y=count, fill=batch)) + 
  geom_violin(position=pos, width=0.7) + 
  geom_dotplot(binaxis='y', stackdir='center', position=pos, dotsize=1/2, binwidth=1/50) + 
  stat_summary(geom="point", fun="median", size=1.5, color="goldenrod1", position=pos) +
  scale_y_log10() + theme_custom()

**Violin plots grouped by condition with highlighted batch effect**. Medians are marked by golden points. Black points represent sets of raw counts per sample. A log scale is used for the y-axis.

Violin plots grouped by condition with highlighted batch effect. Medians are marked by golden points. Black points represent sets of raw counts per sample. A log scale is used for the y-axis.

A ridge plot is an alternative representation where kernel density estimates are stacked horizontally creating the impression of a mountain range. They are particularly useful to assess discrepancies between data distributions.

Ridge line plots can be produced with the geom_density_ridges function of the \(\bf{\texttt{ggridges}}\) package.

ggplot(sampleCounts %>% dplyr::filter(count>0),
       aes(x=count, y=condition, fill=batch)) + 
  geom_density_ridges(quantile_lines=TRUE, quantiles=2, vline_color="darkred",
                      scale=0.9, alpha=0.5) +
  guides(scale="none") + scale_x_continuous(trans="log10") + theme_custom()

**Ridge plots grouped by condition with highlighted batch effect**. Red vertical lines represent medians. A log scale is used for the x-axis.

Ridge plots grouped by condition with highlighted batch effect. Red vertical lines represent medians. A log scale is used for the x-axis.

An example of worrisome ridge plot profiles is given below for a condition where the second replicate stands far apart and the third one is not normally distributed,

**Worrisome ridge plot profiles**. The second replicate is isolated while the third replicate follows a different distribution.

Worrisome ridge plot profiles. The second replicate is isolated while the third replicate follows a different distribution.

Tip 

Violin and ridge plots are useful to detect abnormal samples. Samples showing an unusual distribution profile (non-normal or uneven count) require further investigation and should eventually be discarded.

Counts data transformation

Some exploratory methods such as clustering analysis or dimension reduction (for instance principal components analysis) rely on distance or covariance matrix that are heavily sensitive to the scale of the variables. As such, a prerequisite is to have homoskedastic data, to scale variables so that their variance lies in a close range for a given mean value.

In this perspective, the \(\bf{\texttt{DESeq2}}\) package comes with two possible transformation functions that aim to remove the dependence of the variance on the mean: the variance stabilizing transformation or vst, and the regularized logarithm or rlog. When choosing the method, rlog should be preferred when the number of samples is small (range of \(10\) to \(100\)). Both functions have a blind boolean argument that dictate whether the design formula should be used (blind=FALSE) or not (blind=TRUE) to estimate the global amount of variability in the counts.

To highlight the variance stabilization effect, these two transformations are compared against a classic logarithmic shift that doesn’t act specifically on the variance. The logarithmic shift is applied with the normTransform function of \(\bf{\texttt{DESeq2}}\). Here, the blind argument is set to True to perform the transformations with no a priori on the design.

ntd <- normTransform(dds)
vsd <- varianceStabilizingTransformation(dds, blind=TRUE)
rld <- rlog(dds, blind=TRUE)

p1 <- meanSdPlot(assay(ntd), plot=FALSE)$gg +
  labs(subtitle="(a). logarithmic shift") + theme_custom()
p2 <- meanSdPlot(assay(vsd), plot=FALSE)$gg +
  labs(subtitle="(b). vst") + theme_custom()
p3 <- meanSdPlot(assay(rld), plot=FALSE)$gg +
  labs(subtitle="(c). rlog") + theme_custom()
blankPlot <- ggplot() + geom_blank(aes(1,1)) + theme_void()

grid.arrange(p1, blankPlot, p2, p3, ncol=2, nrow=2)

**Plot of standard deviation against the mean for three transformations.** (a) logarithmic shift, (b) vst, (c) rlog. The blue colormap indicates the density of counts. The red line denotes the standard deviation running median.

Plot of standard deviation against the mean for three transformations. (a) logarithmic shift, (b) vst, (c) rlog. The blue colormap indicates the density of counts. The red line denotes the standard deviation running median.

If there is no variance-mean dependency, then the red line should be approximately horizontal. That is clearly not the case for the logarithmic shift transformation. vst gives the best results and will be used for clustering analysis and PCA. The corresponding transformed data will be referred as VST data.

Tip 

If the red line for the logarithmic shift is approximately horizontal, then there is no need to apply the vst or rlog transformations.

Clustering analysis

Another useful tool to assess similarities between samples is to draw a heatmap of the sample-to-sample distance matrix. This matrix contains the Euclidean distance values of pairs of samples. The distance is evaluated on the VST data. Note that to calculate the distance matrix sample-wise, the counts matrix must be transposed.

The heatmap figure is drawn with the pheatmap function from the \(\bf{\texttt{pheatmap}}\) package. The colormap of the figure is set with the brewer.pal from the \(\bf{\texttt{RColorBrewer}}\) package.

# calculation of the sample-to-sample matrix
sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix(sampleDists)
# labels formatting
rownames(sampleDistMatrix) <- paste(vsd$condition, vsd$batch, sep=".")
colnames(sampleDistMatrix) <- NULL
# heatmap plot
colors <- colorRampPalette( rev(brewer.pal(9, "BuPu")) )(255)
p <- pheatmap(sampleDistMatrix,
              clustering_distance_rows=sampleDists,
              clustering_distance_cols=sampleDists,
              col=colors, fontsize = 14, silent=TRUE)
# custom text color and font
p$gtable$grobs[[4]]$gp=grid::gpar(
  col="#23373b", fontsize=14, fontfamily="CMU Bright")
p$gtable$grobs[[5]]$gp=grid::gpar(
  col="#23373b", fontsize=14, fontfamily="CMU Bright")
p

**Heatmap of the sample-to-sample distance matrix.** The darker the color the closer the samples. Three main clusters can be observed.

Heatmap of the sample-to-sample distance matrix. The darker the color the closer the samples. Three main clusters can be observed.

From the result, three main clusters stand out :

  • one made of the three drugs conditions: Drug1, Drug2, Drug3,

  • one made of the Alt conditions,

  • the last regroups both the Ctrl and NoDrug conditions.

PCA

Principal components analysis(Jolliffe, 2002) is a dimension reduction technique that projects a dataset on a subspace of lower dimension described by an orthogonal basis. The vectors of the basis are called principal components that maximize the variance of the projected data: the first component explains the most variance, the second component explains the most variance once the contribution of the first component is removed, and so on.

The plotPCA function of the \(\bf{\texttt{DESeq2}}\) package can be used to project the VST data onto the 2D-plane formed by the first two principal components, namely PC1 and PC2. The percentage on each axis label is the fraction of total variance explained by each principal component. The function only returns the first two principal components. The package PCATools retrieves more principal components and also uses additional features (see the package documentation for further details).

The intgroup argument can be used to label the samples, here according to their batch and condition. Just like for the plotCounts function, the argument returnData allow extraction of the results without plotting. A custom plot is then produced with the \(\bf{\texttt{ggplot2}}\) package.

# builds the PCA
pcaData <- plotPCA(vsd, intgroup=c("condition", "batch"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
# custom ggplot2 figure
ggplot(pcaData, aes(PC1, PC2, color=condition, shape=batch)) +
  geom_point(size=3) +
  xlab(paste0("PC1: ",percentVar[1], "% variance")) +
  ylab(paste0("PC2: ",percentVar[2], "% variance")) + 
  coord_fixed() + scale_color_brewer(palette="Dark2") +
  theme_custom()

**PCA plot of the vst data.** Colors and markers respectively label sample condition and batch index.

PCA plot of the vst data. Colors and markers respectively label sample condition and batch index.

One can observe that the first two components explain around \(50\%\) of the total variance. The reminder is explained by the other components although each of these remaining dimensions will explain less than the first two. The conclusions are the same than those drawn from the clustering analysis. Additionally, one can observe that there is no batch effect: there is no group of points with a unique identifier (circle, triangle or square).

Warning 

PCA implicitly assumes the data to be jointly normally distributed. This might not always be the case when overspread raw counts are observed for some samples, for instance the ridge plot example of Section 3.3.

In such cases, it is preferable to perform a generalized PCA(Townes et al., 2019). This can be done with the glmpca function from the \(\bf{\texttt{glmpca}}\) package. The function takes as inputs:

  • the raw counts,

  • the number of latent variables to fit, L, which is the equivalent of the number of principal components in PCA.

# set random generator seed for reproducibility
set.seed(404)
# builds the generalized PCA
gpca <- glmpca(counts(dds), L=2)
gpca.dat <- gpca$factors
gpca.dat$condition <- dds$condition
gpca.dat$batch <- dds$batch
# reset seed to default
set.seed(NULL)

# custom ggplot2 figure
ggplot(gpca.dat, aes(x=dim1, y=dim2, color=condition, shape=batch)) +
  geom_point(size=3) +
  xlab(TeX(paste0("PC1: ", "$\\sigma_1$", "=", round(sd(gpca.dat$dim1),2)))) +
  ylab(TeX(paste0("PC2: ", "$\\sigma_2$", "=", round(sd(gpca.dat$dim2),2)))) + 
  coord_fixed() + scale_color_brewer(palette="Dark2") +
  theme_custom()

**Generalized PCA plot of the raw count data.** Colors and markers respectively label sample condition and batch index.

Generalized PCA plot of the raw count data. Colors and markers respectively label sample condition and batch index.

For the present case study, the results are quite similar to those of PCA. One caveat of generalized PCA is the lack of interpretability: the percent of total variance explained by each component is unknown1. However, the components can still be ranked based on the value of their variance: \(\sigma_1^2 > \sigma_2^2 > \dots\).

Warning 

An important drawback of PCA is that it can only captures linear correlations between the features. For instance, PCA will fail if the dataset lies in a nonlinear manifold (e.g. if it has hidden nonlinear patterns).

Several alternative approaches dealing with nonlinear dimension reduction have already been introduced, popular ones for high-throughput sequencing data are t-SNE(van der Maaten and Hinton, 2008) and UMAP(McInnes et al., 2018).

Conclusion

The exploratory analysis conducted on the present study case has revealed that the condition \(\text{Alt}\) stands afar from the rest of the conditions. This observation was expected given the intrinsic biological representation of the condition. This condition is removed before conducting the differential expression analysis as it would introduce an additional detrimental source of noise. The reduced DESeqDataSet is obtained as follows:

# reduced model without Alt condition
colDel <- paste("Alt", seq(3), sep=".")
dds <- DESeqDataSetFromMatrix(countData=cts[,!(row.names(coldata) %in% colDel)],
                              colData=coldata[!(row.names(coldata) %in% colDel),],
                              design=~ batch + condition)
dds <- dds[!rowDel,]

Differential expression analysis

Section 4.1 provides the theoretical background of \(\bf{\texttt{DESeq2}}\) and can be entirely skipped without drawbacks.

Statistical background

While the previous exploratory analysis involves transformation of the counts, it is critical that the hypothesis testings performed during the differential expression analysis are applied on un-normalized raw counts.

The basis of the differential expression analysis is to model each gene count by a statistical model called generalized linear model(Dunn and Smyth, 2018) or more simply GLM. The general form of a GLM is the following:

\[\begin{array}{lcccl} g\left(\mathbb{E}[Y\vert\bm{X}]\right) & = & g\left(\mu\right) & = & g\left( \bm{X}\bm{\beta} \right) \ , \\ \mathbb{Var}[Y\vert\bm{X}] & = & \sigma^2 & = & \mathbb{Var}\left( g^{-1}\left( \bm{X}\bm{\beta} \right) \right) \ . \end{array}\]

where \(\mathbb{E}[Y\vert\bm{X}]\) and \(\mathbb{Var}[Y\vert\bm{X}]\) denote respectively the mean and variance of the outcome \(Y\). The GLM components are the following:

  • a particular distribution taken from the exponential family that models the outcome \(Y\),

  • a linear predictor for the mean: \(\bm{X}\bm{\beta}\),

  • a link function \(g\).

In \(\bf{\texttt{DESeq2}}\), the outcome \(Y\) corresponds to the number of counts for gene \(i\) and sample \(j\), denoted by \(K_{i,j}\). The number of counts is modeled by a Negative Binomial distribution (NB) with parameterization \(\Theta=(\mu_{i,j},\alpha_i)\) and probability mass function defined as:

\[\mathbb{P}\left( K_{i,j} = k \right) = \dfrac{\Gamma\left(r_i + k \right)}{k! \Gamma\left(r_i\right)} \left(\dfrac{r_i}{r_i + \mu_{i,j}}\right)^{r_i}\left(\dfrac{\mu_{i,j}}{r_i + \mu_{i,j}}\right)^{k} \ ,\]

where \(r_i = \alpha_i^{-1}\) and \(\Gamma\) is the Gamma function. The mean is composed of two parameters:

\[\mathbb{E}[K_{i,j}\vert\bm{X}_j]=\mu_{i,j}=s_jq_{i,j} \ ,\]

\(q_{i,j}\) is a parameter proportional to the expected true concentration of fragments for sample \(j\) and \(s_j\) is a sample-specific size factor that acts as a normalization factor. \(\bm{X}_j\) is the row of the design matrix (see Section 2.3) corresponding to sample \(j\).

The choice of a negative binomial distribution can be motivated by looking at its variance:

\[\mathbb{Var}[K_{i,j}\vert\bm{X}_j]=\mu_{i,j}\left( 1 + \alpha_i \mu_{i,j}\right) \ ,\]

as the gene-wise dispersion parameter \(\alpha_i\) helps to better capture biological variability.

The link function \(g\) is the binary logarithm such that:

\[\log_2\left(q_{i,j}\right) = \bm{X}_j \bm{\beta}_i \ ,\]

or equivalently,

\[\log_2\left(\mu_{i,j}\right) = \log_2\left(s_j\right) + \bm{X}_j \bm{\beta}_i \ ,\]

where \(\bm{\beta}_i\) are the coefficients giving the log2 fold changes for gene \(i\).

Workflow

From the previous equations there are three categories of parameters to estimate:

  • the size factor \(s_j\), common to all GLM’s,

  • the dispersion coefficient \(\alpha_i\), also common to all GLM’s,

  • the log2 fold change coefficients \(\bm{\beta}_i\) for each GLM.

The \(\bf{\texttt{DESeq2}}\) package offers a workflow to estimate all the parameters. The workflow can be run with a single call to the function DESeq. This call will print out a message for each step it performs.

dds <- DESeq(dds)

## estimating size factors.
## estimating dispersions.
## gene-wise dispersion estimates.
## mean-dispersion relationship.
## final dispersion estimates.
## fitting model and testing.

The following graph summarizes the steps performed by the DESeq call as well as their associated R function call.

**Worflow of the `DESeq` function.** The three main steps consist in estimating in order: the size factors $$s_j$$, the dispersions coefficients $$\alpha_i$$ and the log2 fold change coefficients $$\bm{\beta}_i$$.

Worflow of the DESeq function. The three main steps consist in estimating in order: the size factors \(s_j\), the dispersions coefficients \(\alpha_i\) and the log2 fold change coefficients \(\bm{\beta}_i\).

Size factors

By default, the normalization constants are considered constant within a sample, that is \(s_{i,j} = {s_j}\), and are estimated with the median-of-ratios method,

\[s_j = \operatorname*{\text{median}}_{i, K_i^R \neq 0} \dfrac{K_{i,j}}{K_i^R} \ , \quad K_i^R=\left(\prod_{j=1}^m K_{i,j}\right)^{1/m} \ ,\]

where \(m\) denotes the number of samples. This method is robust to imbalance in up- or down-regulation and to large number of differentially expressed genes. Values of the size factors can be accessed through the sizeFactors function.

sizeFactors(dds)

The figure below shows the median value for the distribution of all gene ratios for each sample.

**histogram of the set of gene ratios for each sample**. Gold vertical lines mark the median values which corresponds to the size factors.

histogram of the set of gene ratios for each sample. Gold vertical lines mark the median values which corresponds to the size factors.

size factors
Drug1.1 Drug1.2 Drug1.3 Drug2.1 Drug2.2 Drug2.3 Drug3.1 Drug3.2 Drug3.3 Ctrl.1 Ctrl.2 Ctrl.3 NoDrug.1 NoDrug.2 NoDrug.3
1.113555 1.088804 1.196878 1.111516 1.111082 1.03039 1.370907 0.920712 1.011378 1.0364 0.986884 1.029594 0.9068247 0.6774022 1.192338

The matrix containing the count data normalized by the size factors can be computed with the counts function:

counts(dds, normalized=TRUE)

Tip 

Usually the size factors fluctuate around 1. If large variations between samples are observed it may indicate the presence of either multiple outliers or zero counts. In those cases, the user can try the other types of normalization proposed by DESeq2 (see the documentation of the function
estimateSizeFactors).

Dispersion coefficients

Recall that the dispersion coefficient \(\alpha_i\) reads,

\[\alpha_i = \dfrac{\mathbb{Var}[K_{i,j}\vert\bm{X}_j]-\mu_{i,j}}{\mu_{i,j}^2} \ .\]

Three main observations can be drawn from this formula. The dispersion coefficient:

  • is directly related to the variance,

  • is higher the fewer the mean counts and lower the more the mean counts,

  • reflects the variance in gene expression for a given mean value (dispersion estimates for two genes with the same mean will only differ based on their gene count variance)

For low mean counts, the variance estimates have a much larger spread; therefore, the dispersion estimates will differ much more between genes with small means. This can be illustrated by plotting mean against variance in counts data.

# recover mean and variance for each gene over conditions
sampleCounts %<>% dplyr::group_by(ID, condition) %>%
  dplyr::summarise(mean=mean(count), var=var(count))
# plot the histogram
ggplot(sampleCounts, aes(x=mean, y=var)) + geom_point(color="#23373b") +
  scale_y_log10() + scale_x_log10() +
  geom_abline(intercept = 0, slope=1, color="purple") +
  labs(x="mean", y="variance") +
  theme_custom()

**Plot of counts mean against counts variance for each gene.** Mean and variance are computed for each condition over all batches (replicates). The purple line is the identity function. A log scale is used for each axis.

Plot of counts mean against counts variance for each gene. Mean and variance are computed for each condition over all batches (replicates). The purple line is the identity function. A log scale is used for each axis.

In the present case study, the variance spread is not obvious due to the short mean range. A more explicit example is proposed at this link.

Given the few available replicates, to generate accurate estimates of variation between replicates of the same condition, \(\bf{\texttt{DESeq2}}\) relies on a method called shrinkage by sharing information across genes. This is done in three inner steps:

  • first, dispersion coefficients are obtained for each gene separately using a maximum likelihood estimation (MLE),

  • second, a curve is fitted over the set of MLE values obtained at the first step,

  • finally, the MLE values are shrunk towards the curve obtained at the second step.

The shrunk values are obtained by a Bayesian approach(Gelman et al., 2013) and as such are often called Maximum A Posteriori (MAP) estimates. Values of the MAP estimates can be accessed through the dispersion function.

dispersions(dds)

The figure below shows the set of dispersion estimates obtained at each step. The same figure can be obtained by calling the plotdispEsts function of \(\bf{\texttt{DESeq2}}\). Here, a custom ggplot2 version is used for illustration purpose.

# store the dispersion coefficients in a DataFrame
dispValues <- data.frame(
  mean=dds@rowRanges@elementMetadata@listData$baseMean,
  MLE=dds@rowRanges@elementMetadata@listData$dispGeneEst,
  fitted=dds@rowRanges@elementMetadata@listData$dispFit,
  MAP=dds@rowRanges@elementMetadata@listData$dispMAP,
  outliers=dds@rowRanges@elementMetadata@listData$dispOutlier
)
# custom ggplot
colors <- c("c1"="#23373b", "c2"="red", "c3"="dodgerblue", "c4"="purple")
shapes <- c("c1"=19, "c2"=19, "c3"=19, "c4"=21)
ggplot(dispValues, aes(x=mean)) + geom_point(aes(y=MLE, color="c1", shape="c1")) +
  geom_point(data=dispValues[dispValues$outliers,], aes(x=mean, y=MLE),
             pch=21, fill=NA, size=3, stroke=1, color="purple") +
  geom_point(aes(y=MAP, color="c3", shape="c3")) +
  geom_point(aes(y=fitted, color="c2", shape="c2")) +
  labs(x="mean of normalized counts", y="dispersion") +
  scale_color_manual(name="coefficients",
                     breaks=c("c1", "c2", "c3", "c4"),
                     values=colors,
                     labels=c("MLE", "fitted", "MAP", "outliers")) +
  scale_shape_manual(name="coefficients",
                     breaks=c("c1", "c2", "c3", "c4"),
                     values=shapes,
                     labels=c("MLE", "fitted", "MAP", "outliers")) +
  scale_y_log10() + scale_x_log10() + theme_custom()

**Plot of dispersion estimates.** Black dots are the MLE values, the red curve is the one fitted at step two, the blue dots are the shrunk (MAP) estimates. A log scale is used for each axis.

Plot of dispersion estimates. Black dots are the MLE values, the red curve is the one fitted at step two, the blue dots are the shrunk (MAP) estimates. A log scale is used for each axis.

Both genes with either low dispersion estimates or slightly “above the curve” estimates are shrunk towards the fitted estimates. The outliers, circled in purple in the figure above, are genes with extremely high dispersion values. Such values could mean that the corresponding genes violate the modeling assumptions due to biological effects not captured by the GLM. As a consequence, these outliers are not shrunk toward the curve to avoid false positives.

Tip 

As a simple check, the data should be scattered around the fitted curve, the fitted dispersion decreasing with increasing mean level.

Log2 fold changes

Similarly to the dispersion coefficients, log2 fold changes are also estimated within a Bayesian framework:

  • a first set of estimates is obtained by MLE,

  • then a zero-centered prior normal distribution is fitted based on the empirical distribution of the MLE estimates,

  • the final log2 fold change estimates, i.e. the MAP estimates, are obtained by solving a standard MAP formulation.

The MLE estimates values can be accessed by calling the coef function. This function returns a DataFrame storing by row the log2 fold changes estimates \(\hat{\beta}^{\text{MLE}}_i\) for each gene \(i\).

coef(dds)

Tip 

MLE estimates are sufficient to highlight the set of significant genes. The MAP estimates can help better discriminate the ranking of significant genes.

MAP estimates can be obtained after producing an actual result table, as explained in the following results section.

Results table

Hypothesis testing and results table are produced through calling the function results. Without additional arguments, the results function will select the last variable in the design formula as the reference factor (here condition) and the comparison for the differential expression will be between the last level of this variable (here NoDrug) and the reference level chosen based on the alphabetical order of the levels (here Ctrl):

results(dds)

The comparison performed relies (by default) on the Wald statistical test. For this test, the null hypothesis is that there is no differential expression across the two sample groups. The p-value of the test is then computed from the observed data to check if there is evidence that the null hypothesis is violated, or in others words, that the gene is differentially expressed across the two sample groups. Details about the Wald test are given in the next section.

To specify the two sample groups to be compared, one can use the contrast argument of the results function. The syntax is the following:

# DO NOT RUN: it's a syntaxic example
contrast <- c("factor", "A", "B")
results(dds, contrast=constrast)

The constrast argument is a vector composed of three elements:

  • “factor” is the name of a factor in the design formula,

  • “A” is the name of the level to be compared against the reference level,

  • “B” is the name of the reference level.

The default comparison corresponds to the following contrast:

contrast <- c("condition", "NoDrug", "Ctrl")
resMLE <- results(dds, contrast=contrast)
log2 fold change (MLE): condition NoDrug vs Ctrl
baseMean log2FoldChange lfcSE stat pvalue padj
ABHD12 310.9719028 0.0992761 0.2406201 0.4125846 0.6799110 0.9633554
ABI2 1.5453307 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
ABLIM1 174.4158662 -0.0283841 0.3449217 -0.0822915 0.9344149 1.0000000
ABO 1.4663182 -0.3621200 4.6296993 -0.0782167 0.9376557 1.0000000
ACAA2 46.0824707 -0.0452983 0.8938168 -0.0506796 0.9595808 1.0000000
ACP5 46.3211428 -0.2159979 0.6759546 -0.3195450 0.7493133 0.9644026
ACVR1 74.6916526 -1.7185533 0.6010804 -2.8591072 0.0042484 0.0801480
ADA 490.8209066 -0.0021692 0.2727733 -0.0079526 0.9936548 1.0000000
ADAM17 80.5679219 -0.8669257 0.6641498 -1.3053164 0.1917851 0.6182408
ADAMTS12 54.4463986 0.3740965 0.6640764 0.5633335 0.5732078 0.9061304
ADCYAP1 33.7390346 -1.6638696 0.9816517 -1.6949694 0.0900812 0.4603472
ADIPOQ 77.8239090 -0.8658804 0.7582193 -1.1419920 0.2534574 0.6853581
ADORA1 71.1025285 -1.1739514 0.8916019 -1.3166766 0.1879471 0.6135111
ADORA2A 15.9369812 0.4804360 1.5783695 0.3043875 0.7608327 0.9671256
ADORA2B 56.9670207 0.6558843 0.8130464 0.8066997 0.4198395 0.8197691
ADRB3 1.9743938 3.8909206 3.7271227 1.0439476 0.2965096 0.7280014
ADRBK1 3.9748573 -2.4215316 3.5159323 -0.6887310 0.4909926 0.8666517
AEN 73.3790876 -0.1591530 0.5003809 -0.3180638 0.7504366 0.9644026
AENbis 83.8109172 -0.1822879 0.4859615 -0.3751078 0.7075803 0.9636086
AES 0.0557007 0.0000000 4.8566061 0.0000000 1.0000000 NA
AGER 31.2161675 2.0704197 1.1616818 1.7822606 0.0747068 0.4299714
AGT 26.2541762 2.6049027 1.5466228 1.6842521 0.0921330 0.4665771
AGTR1 28.4888007 -1.3246235 1.1402159 -1.1617304 0.2453450 0.6807668
AGTR1bis 37.9888588 0.9663346 0.8889520 1.0870492 0.2770151 0.7125520
AHCY 132.5387580 -0.5944308 0.3800081 -1.5642581 0.1177570 0.5225185
AHNAK 699.9748623 0.0080788 0.1510136 0.0534971 0.9573358 1.0000000
AHNAKbis 405.2041864 0.1270705 0.2117932 0.5999745 0.5485232 0.9001688
AHNAK2 93.2009034 -0.2481034 0.5287468 -0.4692291 0.6389059 0.9498334
AHSG 47.2848703 0.3180518 0.8249366 0.3855470 0.6998322 0.9636086
AIF1 113.8829316 0.7062955 0.4724499 1.4949638 0.1349239 0.5415128
AIFM1 17.7343569 0.6723623 1.4306807 0.4699597 0.6383838 0.9498334
AIFM1bis 17.2154638 0.4991518 1.6910840 0.2951667 0.7678665 0.9721518
AKT1 86.3225242 -0.9457110 0.5328145 -1.7749349 0.0759086 0.4330451
ALOX15 14.8489248 -1.9669954 1.2028106 -1.6353326 0.1019793 0.4859820
ALOX5 28.6382975 -1.6090859 1.1010063 -1.4614683 0.1438870 0.5429116
ALOX5AP 45.2483630 -0.8618185 1.0665948 -0.8080092 0.4190853 0.8197691
AMPH 140.3655278 0.0728459 0.3894364 0.1870548 0.8516177 0.9839667
ANKRD2 119.4247595 0.4839305 0.3892765 1.2431536 0.2138112 0.6393638
ANO6 5.1865240 0.5892188 2.9707842 0.1983378 0.8427808 0.9839667
ANXA1 187.2626815 -0.1436024 0.3850050 -0.3729885 0.7091570 0.9636086
ANXA1bis 251.5187815 -0.3773135 0.2581809 -1.4614304 0.1438974 0.5429116
ANXA5 265.4528888 0.2873602 0.2954941 0.9724735 0.3308151 0.7526465
ANXA5bis 14.1177118 0.9665165 1.0340997 0.9346453 0.3499711 0.7761655
AOAH 27.3552166 -1.4870016 1.0171893 -1.4618730 0.1437760 0.5429116
APBB1 37.3418291 -0.9537746 0.7512540 -1.2695769 0.2042354 0.6334051
APCS 27.4414622 1.4739775 1.4818009 0.9947204 0.3198723 0.7474380
APOA1 43.9786548 -0.4431802 0.7647122 -0.5795385 0.5622258 0.9011983
APOA2 32.4161352 0.0294539 1.4451994 0.0203805 0.9837398 1.0000000
APOD 348.2616908 -0.4044929 0.2610492 -1.5494892 0.1212642 0.5225185
APOPT1 79.8866136 -0.1352048 0.5440354 -0.2485221 0.8037304 0.9751363
APP 32.9247970 0.8087750 0.9601532 0.8423396 0.3995979 0.8189469
APPL1 53.7366502 -0.7149678 0.7735637 -0.9242521 0.3553551 0.7771323
APPL2 162.3873602 0.1550034 0.3807554 0.4070945 0.6839386 0.9636086
ARHGAP26 27.9089834 -0.2673152 0.7861530 -0.3400295 0.7338343 0.9644026
ARHGEF2 31.7571263 -0.2405671 0.7514189 -0.3201505 0.7488543 0.9644026
ARL6IP5 97.8575226 0.4357004 0.5916164 0.7364575 0.4614523 0.8571639
ARRB1 163.6295125 -0.0445514 0.3063074 -0.1454468 0.8843581 0.9880121
ARRB2 104.0870034 0.0040839 0.3848379 0.0106119 0.9915331 1.0000000
ASAH2 20.2410362 -2.3542373 1.5175732 -1.5513171 0.1208257 0.5225185
ASB2 39.1525422 -0.4099931 0.9733568 -0.4212156 0.6735976 0.9633554
ASB4 8.5243797 -3.9294167 2.6524946 -1.4814042 0.1384989 0.5424108
ASS1 187.2297776 -0.4922234 0.3342041 -1.4728228 0.1407988 0.5426034
ASS1bis 165.4515253 -0.5782720 0.3022306 -1.9133470 0.0557036 0.3696922
ATF2 169.8378643 2.0785949 0.4544713 4.5736545 0.0000048 0.0006034
ATF2bis 96.1886316 0.0852245 0.5163658 0.1650468 0.8689072 0.9866368
ATF4 58.9712823 1.3147580 0.9055564 1.4518787 0.1465353 0.5442474
ATG2B 13.7238503 -4.8761569 2.0525666 -2.3756389 0.0175186 0.2100014
ATM 24.2111835 3.2771634 1.7136376 1.9124017 0.0558247 0.3696922
ATP6V1G1 2.7089157 -3.8311898 3.8408785 -0.9974775 0.3185328 0.7474380
ATPIF1 62.9647397 -0.4077694 0.8883901 -0.4589982 0.6462355 0.9532477
AVP 38.3494878 -0.2976374 0.7685509 -0.3872709 0.6985557 0.9636086
AZU1 133.7629280 0.1505487 0.4225245 0.3563075 0.7216103 0.9644026
B4GALT1 69.9696991 0.4659645 0.6399174 0.7281635 0.4665135 0.8571639
BAD 835.6076430 0.0055413 0.1851706 0.0299256 0.9761264 1.0000000
BAG5 66.2036602 -0.2451500 0.6160533 -0.3979363 0.6906771 0.9636086
BAK1 154.3227100 -0.8808182 0.3738693 -2.3559525 0.0184753 0.2160011
BANP 266.1907259 -1.1527063 0.2938775 -3.9224039 0.0000877 0.0042067
BAP1 46.3077313 -0.2697250 0.6924240 -0.3895373 0.6968788 0.9636086
BCAP31 78.5306397 0.4177709 0.5572425 0.7497111 0.4534287 0.8511742
BCL10 191.0730675 0.2544407 0.3522763 0.7222758 0.4701249 0.8571639
BCL2L1 115.2901085 0.7960056 0.3746929 2.1244217 0.0336349 0.2843950
BCL2L10 72.6515552 -0.9394467 0.6333561 -1.4832835 0.1379991 0.5424108
BCL2L11 286.7354783 0.6339231 0.2742092 2.3118227 0.0207875 0.2211879
BCL2L12 90.1931690 0.6035394 0.4393873 1.3735934 0.1695680 0.5750708
BCL2L12bis 72.5327073 -0.0547749 0.5425787 -0.1009528 0.9195879 0.9963956
BCL2L14 258.8622201 -0.4393538 0.2975536 -1.4765537 0.1397953 0.5425663
BCL2L2 378.5707399 -0.2944819 0.2226992 -1.3223306 0.1860581 0.6101283
BCL6 9.3683868 2.9666422 2.2042386 1.3458807 0.1783410 0.5946793
BCL6B 41.0712108 0.2811335 0.7154318 0.3929564 0.6943517 0.9636086
BCL7C 330.6323489 -0.3033127 0.2280679 -1.3299230 0.1835437 0.6082264
BCL7Cbis 359.3221847 -0.2842731 0.2359661 -1.2047198 0.2283115 0.6551848
BDNF 61.3354801 0.6504623 0.6972247 0.9329305 0.3508558 0.7763095
BIK 76.3224300 1.7610094 0.5947135 2.9611056 0.0030654 0.0665843
BIRC2 44.4001961 1.8741039 0.8714350 2.1505951 0.0315082 0.2747532
BLVRA 93.3903721 -0.5914339 0.5945811 -0.9947068 0.3198789 0.7474380
BLVRAbis 331.3549031 -0.2704115 0.2797092 -0.9667591 0.3336645 0.7539291
BMF 93.0432784 0.9319288 0.6129873 1.5203069 0.1284339 0.5311217
BMP4 40.8378770 -0.3637987 0.9586383 -0.3794953 0.7043201 0.9636086
BMP5 25.0379012 5.4563416 1.7224828 3.1677190 0.0015364 0.0393235
BMPR1B 8.9645943 -1.0936742 1.8244921 -0.5994404 0.5488793 0.9001688
BNIP3 382.1506501 0.0075350 0.3039710 0.0247886 0.9802236 1.0000000
BNIP3L 619.0863338 0.1403444 0.1997846 0.7024786 0.4823808 0.8593713
BRAF 32.2831863 -0.1198757 1.1350625 -0.1056116 0.9158905 0.9963956
BRCA1 19.1340748 1.1358507 1.7920610 0.6338237 0.5261959 0.8874776
BRD4 24.4403342 -0.3133703 0.9226996 -0.3396233 0.7341402 0.9644026
BST1 80.6948934 0.1075024 0.5915901 0.1817176 0.8558043 0.9850616
BTK 41.3053100 -0.2653677 0.7739032 -0.3428952 0.7316773 0.9644026
BTN2A1 0.6646010 0.0000000 4.8566061 0.0000000 1.0000000 NA
BZW2 0.7387484 -2.7799433 4.7634449 -0.5835993 0.5594899 NA
C11orf82 1.7439741 -1.1926065 4.7435964 -0.2514140 0.8014941 0.9751363
C14orf129 582.3910155 0.2835561 0.1811868 1.5649933 0.1175845 0.5225185
C16orf5 62.5893653 0.8034607 0.6444540 1.2467308 0.2124962 0.6393638
C1QA 119.6000737 -0.2494629 0.4177874 -0.5971049 0.5504373 0.9001688
C1QTNF3 98.9811631 1.1168843 0.4499306 2.4823480 0.0130520 0.1711118
C1QTNF3bis 71.8185572 -1.6459942 0.8447259 -1.9485542 0.0513487 0.3628707
C22orf29 568.1818474 0.3190981 0.2310856 1.3808655 0.1673203 0.5720301
C2orf18 229.2981505 -0.6521884 0.3409171 -1.9130410 0.0557428 0.3696922
C5AR1 65.0319384 -0.2394998 0.8232987 -0.2909027 0.7711257 0.9723783
C6orf162 18.5298455 2.8937170 1.6835754 1.7187927 0.0856521 0.4477945
CACNG2 120.8933638 0.1275854 0.3872028 0.3295054 0.7417737 0.9644026
CALD1 85.4563162 0.1644449 0.4556015 0.3609403 0.7181441 0.9644026
CAPN1 10.8256926 -0.5283537 1.6545526 -0.3193333 0.7494738 0.9644026
CAPN2 15.5342678 -1.2479551 1.0767396 -1.1590129 0.2464509 0.6807668
CASP1 234.8902216 -1.1447751 0.2841444 -4.0288495 0.0000561 0.0029489
CASP2 122.6797858 0.2362990 0.4317976 0.5472449 0.5842105 0.9140962
CASP3 54.9567235 -0.1140777 0.5924419 -0.1925550 0.8473075 0.9839667
CASP5 30.6727698 -1.3420089 0.8821440 -1.5213037 0.1281836 0.5311217
CASP5bis 61.1465698 0.6519354 0.6057236 1.0762919 0.2817967 0.7154464
CASP8 50.0774254 0.8664336 0.9341210 0.9275389 0.3536468 0.7771323
CAV1 68.1551797 -0.4317599 0.8718740 -0.4952090 0.6204526 0.9368423
CC2D1B 1.5094372 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
CCDC6 283.9588531 -0.2098988 0.2323775 -0.9032665 0.3663845 0.7939728
CCK 67.9666125 0.2309682 0.6091093 0.3791900 0.7045468 0.9636086
CCL3 211.3157156 -0.0844993 0.3256944 -0.2594434 0.7952931 0.9751363
CCL5 190.1217177 1.3264544 0.3638831 3.6452763 0.0002671 0.0101179
CCNCbis 14.8022899 1.8602416 1.7553381 1.0597626 0.2892526 0.7242846
CCR2 26.1143538 -0.3418969 1.1956919 -0.2859407 0.7749236 0.9751363
CCR6 26.2933325 -0.5558207 1.5210026 -0.3654305 0.7147901 0.9644026
CD19 15.5998668 -0.2399517 1.2619296 -0.1901467 0.8491942 0.9839667
CD200 15.4783912 3.7543134 2.1446355 1.7505601 0.0800217 0.4411191
CD200R1 33.7170690 -1.9951341 1.1783188 -1.6932039 0.0904167 0.4603472
CD27 60.7145593 0.5831297 0.5946262 0.9806660 0.3267575 0.7474380
CD28 44.2577494 0.5441348 0.9102600 0.5977795 0.5499870 0.9001688
CD36 5.9567602 -4.9457320 3.2034697 -1.5438673 0.1226205 0.5225185
CD44 17.3470211 -1.1526709 1.7316907 -0.6656333 0.5056455 0.8714234
CD6 1.6282572 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
CD68 41.4901920 -1.6360718 0.9390465 -1.7422692 0.0814613 0.4433557
CD70 297.5898333 -0.0729297 0.2820911 -0.2585324 0.7959960 0.9751363
CD74 408.6389437 -0.5773670 0.2919910 -1.9773450 0.0480026 0.3491579
CD96 24.3150614 1.7067181 1.5118215 1.1289151 0.2589337 0.6963264
CDC25C 144.3067873 -0.6404598 0.5459786 -1.1730492 0.2407761 0.6746004
CDC42EP2 321.2922324 1.9019487 0.3039983 6.2564448 0.0000000 0.0000001
CDCA5 209.4059088 0.3342663 0.3197576 1.0453741 0.2958501 0.7280014
CDCA7 0.6150384 3.3388505 4.7626292 0.7010520 0.4832706 NA
CDH5 16.6569685 -1.7258360 1.5711422 -1.0984595 0.2720039 0.7105396
CDK1 47.7954857 -1.3162472 0.9164761 -1.4362046 0.1509441 0.5442474
CDK2 900.8878836 -0.1831776 0.1977650 -0.9262385 0.3543220 0.7771323
CDK7 6.5305664 1.9481311 3.1982351 0.6091269 0.5424403 0.9001688
CDKN1A 186.1894418 0.1923270 0.3295146 0.5836675 0.5594440 0.9011983
CDKN1B 166.2276638 0.5176994 0.3104771 1.6674320 0.0954285 0.4714691
CDKN2D 197.0631615 0.3276017 0.2975114 1.1011400 0.2708357 0.7104749
CDX2 287.5360636 0.1697559 0.2952124 0.5750298 0.5652712 0.9011983
CEP170P1 86.0105164 0.2007902 0.5384581 0.3728984 0.7092241 0.9636086
CEP55 57.4736278 -0.2252791 0.6322379 -0.3563201 0.7216009 0.9644026
CFLAR 175.5519450 -0.7335448 0.3612786 -2.0304133 0.0423145 0.3200484
CHCHD10 29.6076287 2.3322176 2.2826847 1.0216994 0.3069232 0.7355359
CHIA 58.1881862 -1.4148003 0.5994187 -2.3602871 0.0182608 0.2160011
CHID1 30.5623148 1.2505737 0.8980422 1.3925556 0.1637542 0.5632542
CHRFAM7A 1.5827202 1.3875365 3.2449409 0.4275999 0.6689424 0.9627484
CHRNA7 68.4767088 0.6368121 0.6239974 1.0205364 0.3074741 0.7355359
CIDEB 132.6241100 0.6416658 0.3582902 1.7909109 0.0733076 0.4299714
CLEC7A 372.1849905 0.0090577 0.3199738 0.0283077 0.9774168 1.0000000
CLOCK 0.7585792 2.4722549 4.7939371 0.5157045 0.6060608 NA
CLU 47.4932577 2.0894280 0.8617567 2.4246149 0.0153246 0.1909530
CMA1 94.7117100 0.2595826 0.5994333 0.4330468 0.6649808 0.9627484
CMA1bis 101.4659147 -0.7577784 0.5308571 -1.4274621 0.1534467 0.5442474
CNOT4 29.3091715 2.2102060 0.9775488 2.2609675 0.0237613 0.2433565
CNTF 113.1407575 1.4148499 0.9719868 1.4556267 0.1454958 0.5442474
COL2A1 265.3869464 0.2737488 0.2600993 1.0524778 0.2925804 0.7253237
COPS6 201.5530807 0.3971883 0.3470350 1.1445193 0.2524083 0.6853581
CREB1 345.5545062 -0.3009369 0.3177450 -0.9471020 0.3435868 0.7673978
CREB3 112.9201025 1.4423008 0.5207175 2.7698337 0.0056085 0.0973092
CREB3bis 65.3760628 -0.1929964 0.8020547 -0.2406275 0.8098438 0.9773957
CREB3L1 77.3707620 0.9526031 0.5785719 1.6464732 0.0996664 0.4840208
CRH 54.7066227 0.2253777 0.8261227 0.2728138 0.7849963 0.9751363
CRIP1 187.6792160 1.0415792 0.3256092 3.1988626 0.0013797 0.0384290
CRP 16.0121046 -0.0591311 0.9719114 -0.0608401 0.9514866 1.0000000
CRPbis 65.1093445 0.1946582 0.6135252 0.3172783 0.7510325 0.9644026
CRY1 49.5832751 -0.5260758 0.6383408 -0.8241301 0.4098657 0.8189469
CRYAB 248.2812652 -0.4333620 0.3070921 -1.4111792 0.1581918 0.5543983
CSF2 77.2092542 -0.4380509 0.7226628 -0.6061622 0.5444071 0.9001688
CSNK2A1 76.4105442 -0.6907967 0.4479508 -1.5421263 0.1230429 0.5225185
CSNK2A2 456.3761947 -0.1760007 0.2483163 -0.7087762 0.4784633 0.8571639
CTH 91.5795769 -0.8754587 0.4552073 -1.9232088 0.0544538 0.3683413
CTNNA1 60.1083741 -1.0061440 0.7651520 -1.3149596 0.1885235 0.6135111
CTNNBIP1 780.4244066 0.6716207 0.2084727 3.2216244 0.0012747 0.0365789
CTTN 52.7531426 0.7749299 0.6151312 1.2597798 0.2077488 0.6334051
CUEDC2 533.6336665 0.2015525 0.1988098 1.0137953 0.3106804 0.7355359
CUL1 39.0154141 0.4812016 1.1788836 0.4081842 0.6831385 0.9636086
CUL2 5.0493572 4.7502967 2.4527509 1.9367220 0.0527793 0.3639027
CUL3 3.0934301 0.0000000 4.5461842 0.0000000 1.0000000 1.0000000
CUL5 0.9661242 0.0000000 4.8168241 0.0000000 1.0000000 NA
CX3CL1 35.7565675 -1.2816232 1.2147361 -1.0550631 0.2913964 0.7242846
CXCL13 148.2514765 0.5380919 0.4250129 1.2660603 0.2054915 0.6334051
CXCR7 26.4476109 0.3286639 1.0430802 0.3150898 0.7526935 0.9644026
CYP19A1 137.2172684 -0.4548788 0.4144147 -1.0976416 0.2723610 0.7105396
CYP19A1bis 85.7985855 0.1380195 0.4924724 0.2802583 0.7792793 0.9751363
CYP19A1bis2 22.2364691 0.0666210 1.1821559 0.0563555 0.9550586 1.0000000
CYSTM1 0.8099162 0.0000000 4.8566061 0.0000000 1.0000000 NA
DAB2IP 2.9000384 -0.2597791 2.4646743 -0.1054010 0.9160576 0.9963956
DAGLB 23.4822780 -0.9701731 1.1792594 -0.8226969 0.4106804 0.8189469
DAXX 82.6282062 -0.1127312 0.5958218 -0.1892029 0.8499338 0.9839667
DAZ3 29.5882295 -0.4668066 0.9396216 -0.4968028 0.6193281 0.9368423
DAZ4 7.2737810 6.8490704 2.9483526 2.3230160 0.0201783 0.2182650
DBH 37.6187078 0.1156569 0.7987530 0.1447968 0.8848713 0.9880121
DCUN1D4 2.0527464 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
DDHD1 6.6259822 1.0268104 2.0679183 0.4965430 0.6195113 0.9368423
DDI1 8.3265855 -0.9747997 4.5258979 -0.2153826 0.8294690 0.9829258
DDIT3 114.8949439 2.0442585 0.4746150 4.3071932 0.0000165 0.0013048
DDIT4 269.2259522 0.9892509 0.2524111 3.9192048 0.0000888 0.0042067
DDX21 1.1483962 -3.8707430 4.7379674 -0.8169628 0.4139497 0.8195195
DDX47 64.6884948 0.1197833 0.6347893 0.1886977 0.8503298 0.9839667
DEDD 5.6914983 2.6973096 3.0166218 0.8941491 0.3712421 0.7978119
DEDD2 35.2340203 0.0430750 0.7126865 0.0604403 0.9518049 1.0000000
DFNA5 107.1883097 -0.0823781 0.4770675 -0.1726759 0.8629062 0.9861531
DFNA5bis 75.6219437 -0.3202826 0.5864082 -0.5461769 0.5849443 0.9140962
DIABLO 60.8216294 -2.1850018 0.7955964 -2.7463696 0.0060259 0.0983881
DMC1 6.1564469 0.9291392 2.9048411 0.3198589 0.7490753 0.9644026
DNAJA1 98.9613915 -0.6280808 0.4383703 -1.4327632 0.1519255 0.5442474
DNAJC10 8.3648970 -2.4795065 2.3642789 -1.0487369 0.2942992 0.7276798
DNAJC30 62.5280930 0.2152967 0.6258941 0.3439826 0.7308594 0.9644026
DNM1L 25.2671383 -0.3414158 1.1693192 -0.2919783 0.7703032 0.9723783
DUOXA1 8.0041645 0.6996181 2.5697946 0.2722467 0.7854323 0.9751363
DUOXA2 61.9587453 -0.4413698 0.5683404 -0.7765941 0.4373983 0.8351132
DUSP10 132.8387634 0.7890589 0.3995795 1.9747233 0.0482996 0.3491579
DUSP10bis 191.2659037 0.9459222 0.3321454 2.8479162 0.0044007 0.0801480
DUSP16 109.8477439 -0.4646780 0.5232091 -0.8881306 0.3744705 0.7987017
DUSP23 2.8967229 -4.0237100 4.1733209 -0.9641506 0.3349704 0.7539291
DUSP3 1067.5533968 -0.0767448 0.1766045 -0.4345572 0.6638839 0.9627484
DUSP4 252.5145819 1.9045672 0.3545150 5.3723170 0.0000001 0.0000184
DUSP6 136.5617933 -0.4109328 0.3892173 -1.0557927 0.2910629 0.7242846
DUSP7 61.0311075 1.4129782 0.6742235 2.0957120 0.0361077 0.2941416
DYNC1I2 94.7709314 -0.2397389 0.5350313 -0.4480839 0.6540926 0.9586285
DYNC1LI1 100.2737109 0.0878174 0.4784682 0.1835387 0.8543753 0.9850616
DYRK2 4.0044856 1.2302462 3.6503088 0.3370252 0.7360979 0.9644026
E2F2 308.4662227 0.2881983 0.2419500 1.1911482 0.2335954 0.6683228
EDA2R 31.3012494 0.4382464 1.3401787 0.3270060 0.7436634 0.9644026
EDNRB 12.2023639 -1.1598758 2.0088818 -0.5773739 0.5636869 0.9011983
EIF2AK2 32.0537018 -1.6075857 1.0181643 -1.5789061 0.1143576 0.5182650
EIF4ENIF1 0.6432526 -3.3037014 4.7494357 -0.6955987 0.4866802 NA
ELANE 71.4165979 0.9861812 0.5426793 1.8172448 0.0691796 0.4275297
ELK1 7.0459835 -1.0019227 2.3909148 -0.4190541 0.6751766 0.9633554
ELK3 147.1454439 0.3846659 0.3267158 1.1773716 0.2390472 0.6738380
ELK4 44.6758264 -0.2159807 0.6718623 -0.3214657 0.7478575 0.9644026
ELL 0.6597596 0.0000000 4.8566061 0.0000000 1.0000000 NA
ELL3 61.1130167 -0.1493525 0.5789111 -0.2579887 0.7964156 0.9751363
EPB49 299.2365644 0.3028225 0.2625985 1.1531769 0.2488378 0.6807668
EPHB6 26.3193635 0.0684009 0.8701193 0.0786110 0.9373421 1.0000000
EPO 40.9331404 -0.8478389 0.9941650 -0.8528151 0.3937618 0.8177466
EPS8 31.9542904 0.6085822 1.1243108 0.5412936 0.5883053 0.9170690
ERBB3 37.8995990 -3.1331929 1.0644039 -2.9436127 0.0032441 0.0682694
ERG 119.3990955 0.0232653 0.3729867 0.0623757 0.9502636 1.0000000
ERO1L 3.9566776 -0.2799125 2.5031575 -0.1118238 0.9109631 0.9963956
ERO1Lbis 13.3788545 0.9100350 1.8368609 0.4954295 0.6202970 0.9368423
ERP29 196.9720349 -0.9649675 0.3702826 -2.6060301 0.0091598 0.1301820
ESR1 141.6934015 0.0164106 0.3407885 0.0481549 0.9615928 1.0000000
ESR2 30.7838562 1.8929896 1.0817100 1.7499973 0.0801188 0.4411191
ETS1 656.4127032 1.1353647 0.1594024 7.1226327 0.0000000 0.0000000
ETV6 128.3350761 -1.0650293 0.3943240 -2.7008988 0.0069152 0.1091455
EXOC7 345.3272404 -0.2379165 0.2449566 -0.9712601 0.3314188 0.7526465
EXOSC1 1.0137083 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
EYA1 13.4467977 1.5406254 1.8678517 0.8248114 0.4094787 0.8189469
EYA1bis 78.3092174 0.2607059 0.5081270 0.5130724 0.6079007 0.9315242
EYA2 36.7256058 -0.2374028 0.8949117 -0.2652807 0.7907932 0.9751363
EYA3 48.7640530 -1.6658788 0.7751414 -2.1491289 0.0316242 0.2747532
F12 26.9225445 -0.5954573 0.8651161 -0.6882976 0.4912654 0.8666517
F2R 3.3320832 0.0000000 4.1788303 0.0000000 1.0000000 1.0000000
F3 37.5935125 1.7564475 1.1726123 1.4978929 0.1341611 0.5415128
F8 169.5302601 -0.9454423 0.3224923 -2.9316737 0.0033714 0.0694070
FABP4 132.2956889 0.0338725 0.4056255 0.0835069 0.9334485 1.0000000
FADD 256.4123602 -0.1029756 0.2465202 -0.4177167 0.6761543 0.9633554
FAIM 315.5848453 0.8946418 0.2573421 3.4764692 0.0005081 0.0171834
FAIM2 268.8924988 -0.1883319 0.3191278 -0.5901457 0.5550930 0.9001688
FAM103A1 147.7536634 -0.8635914 0.3869978 -2.2315147 0.0256471 0.2529975
FAM105B 12.9783767 0.7666076 1.2037309 0.6368596 0.5242163 0.8864872
FAM105Bbis 896.4391553 -0.1586568 0.1772384 -0.8951606 0.3707013 0.7978119
FAM13C 22.9325398 0.4427767 1.5152076 0.2922218 0.7701171 0.9723783
FAM195B 431.6811162 -0.0653942 0.2355769 -0.2775918 0.7813258 0.9751363
FAM19A3 63.4014153 -0.6639513 0.6715511 -0.9886832 0.3228182 0.7474380
FAM207A 275.8319750 0.2526398 0.2999791 0.8421912 0.3996809 0.8189469
FANCD2 243.3309771 0.7239528 0.3274852 2.2106428 0.0270606 0.2532997
FAS 64.1769378 -0.4709991 0.7098978 -0.6634745 0.5070267 0.8714234
FASLG 215.0651873 0.5562210 0.3211701 1.7318581 0.0832988 0.4456377
FASN 18.9104368 0.2473127 1.6681285 0.1482576 0.8821395 0.9880121
FAXC 36.7612246 1.7766942 1.1253371 1.5788107 0.1143795 0.5182650
FBXO4 88.4575866 -0.6861209 0.4132752 -1.6602035 0.0968735 0.4753327
FBXW7 8.4778476 0.6666775 2.0933688 0.3184711 0.7501276 0.9644026
FCER1G 93.2447026 0.6587744 0.4454663 1.4788422 0.1391825 0.5424108
FCGR2B 54.7418218 -0.5877198 0.9859050 -0.5961221 0.5510937 0.9001688
FECH 61.1858818 -0.4784118 0.6551996 -0.7301772 0.4652819 0.8571639
FFAR2 48.1766476 -2.4461684 0.6393656 -3.8259306 0.0001303 0.0058750
FFAR3 100.6610863 0.3452144 0.3871000 0.8917964 0.3725021 0.7978119
FGA 60.1330592 -1.8972483 0.8410527 -2.2558018 0.0240830 0.2433565
FGAbis 17.5806452 -5.9123283 1.8592228 -3.1799999 0.0014728 0.0393235
FGB 37.3016782 -1.0764068 1.2899714 -0.8344424 0.4040318 0.8189469
FGF10 77.2688269 -0.2172030 0.5656896 -0.3839613 0.7010071 0.9636086
FGFR2 10.4628017 0.6074871 2.1465670 0.2830040 0.7771737 0.9751363
FGG 37.0847404 1.2830873 1.1651084 1.1012601 0.2707835 0.7104749
FHIT 736.9172664 0.2989751 0.2122982 1.4082788 0.1590485 0.5543983
FHL3 411.2653632 0.2029161 0.2461229 0.8244503 0.4096838 0.8189469
FHL3bis 262.6345801 0.1659687 0.2353197 0.7052902 0.4806297 0.8587855
FIGNL1 24.3666459 0.1270791 1.1331573 0.1121461 0.9107076 0.9963956
FIGNL1bis 21.7428648 -0.4745950 1.1021860 -0.4305943 0.6667634 0.9627484
FIS1 123.9736152 -0.0411758 0.5788696 -0.0711313 0.9432932 1.0000000
FLAD1 13.0310041 -1.8259188 1.5639559 -1.1675002 0.2430084 0.6788466
FNDC4 44.7002366 -1.1858082 0.7730549 -1.5339250 0.1250481 0.5286632
FOS 122.8047922 1.2344861 0.3887844 3.1752462 0.0014971 0.0393235
FOXM1 53.4666663 1.0900319 0.6009768 1.8137669 0.0697136 0.4275297
FOXO3 28.2282016 1.4065924 0.9901682 1.4205591 0.1554450 0.5492776
FOXP1 82.1705387 0.8671768 0.6060367 1.4308982 0.1524594 0.5442474
FOXP3 197.9982435 0.4167320 0.2527992 1.6484700 0.0992563 0.4840208
FPR2 30.6410903 -0.0713571 1.2882527 -0.0553906 0.9558273 1.0000000
FRS2 0.8230325 0.0000000 4.8295743 0.0000000 1.0000000 NA
FUT7 197.1272311 -1.1558392 0.3228973 -3.5795875 0.0003441 0.0125345
FXN 209.6559030 -0.4912950 0.3265558 -1.5044749 0.1324591 0.5415128
FXR2 1.1122827 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
FYN 191.3283429 -0.2531674 0.3043976 -0.8316996 0.4055785 0.8189469
G0S2 100.0676528 0.3741527 0.5243705 0.7135273 0.4755195 0.8571639
GAB1 2.0546376 -0.4475121 4.6178638 -0.0969089 0.9227987 0.9975918
GAB2 160.6895490 -0.2780183 0.3540134 -0.7853326 0.4322586 0.8330360
GABARAP 579.7498765 -0.0167956 0.2030788 -0.0827048 0.9340863 1.0000000
GABRR1 37.7470645 -1.6165874 0.9019101 -1.7924042 0.0730682 0.4299714
GATA1 157.3778060 0.8884493 0.3432452 2.5883803 0.0096428 0.1323445
GATA2 141.6801353 0.2565676 0.2903580 0.8836252 0.3768986 0.7992984
GATA2bis 75.1468194 -0.6177968 0.4828236 -1.2795496 0.2007036 0.6314495
GATA2bis2 156.4174478 -0.1226634 0.2784384 -0.4405405 0.6595457 0.9611894
GBA 38.8134238 0.7409557 0.9347796 0.7926528 0.4279801 0.8305270
GBP5 69.6186282 0.0605569 0.5659480 0.1070009 0.9147883 0.9963956
GCLC 38.0116698 -0.0866356 1.1070242 -0.0782599 0.9376213 1.0000000
GCLM 291.6400782 -0.1985852 0.2719541 -0.7302158 0.4652583 0.8571639
GDNF 44.8102022 0.6372040 0.9820944 0.6488215 0.5164538 0.8796434
GGCT 438.2170549 -0.0372537 0.2246226 -0.1658501 0.8682749 0.9866368
GGT1 136.7702790 -0.3294129 0.3320423 -0.9920813 0.3211579 0.7474380
GHRL 156.8396590 1.2837463 0.4649604 2.7609800 0.0057628 0.0974534
GHSR 48.1277685 0.6414864 0.7453648 0.8606342 0.3894395 0.8105478
GJA1 80.7653106 -1.9311696 0.6220477 -3.1045364 0.0019058 0.0451192
GLRA3 6.5377381 -21.0801888 3.0960897 -6.8086492 0.0000000 0.0000000
GLUD2 1.5705231 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
GMFB 137.8183696 0.3359726 0.4118278 0.8158085 0.4146097 0.8195195
GNAI2 82.6948031 -0.2167492 0.5025158 -0.4313281 0.6662299 0.9627484
GNAI3 32.1102252 -0.4970945 1.1773057 -0.4222306 0.6728567 0.9633554
GNB2L1 281.3065376 -0.2372966 0.2742339 -0.8653074 0.3868701 0.8087549
GORASP1 205.5901994 -0.2986850 0.2880728 -1.0368385 0.2998111 0.7317555
GORASP2 71.4760199 0.6657546 0.6019285 1.1060359 0.2687110 0.7088281
GPR17 1.9820397 -3.9938939 4.7121490 -0.8475738 0.3966754 0.8189469
GPRC5B 60.6724692 -1.4656262 0.7239525 -2.0244784 0.0429210 0.3200484
GPS2 81.1913586 1.0469580 0.4921739 2.1272113 0.0334025 0.2843950
GPSM3 433.5547302 0.1974565 0.2592616 0.7616110 0.4462922 0.8452775
GPX1 69.4634341 0.4975463 0.6982984 0.7125125 0.4761475 0.8571639
GPX4 165.0553666 0.2666423 0.3994211 0.6675719 0.5044069 0.8714234
GRB2 257.9020950 0.1212037 0.2443239 0.4960780 0.6198394 0.9368423
GRINA 226.7284651 -0.6871889 0.3241779 -2.1197893 0.0340238 0.2851377
GRN 35.8896746 1.1582699 0.7452521 1.5541988 0.1201370 0.5225185
GRNbis 50.8450689 0.5072049 0.6632993 0.7646698 0.4444682 0.8452036
H2BFS 23.7330687 0.1487411 1.3645821 0.1090012 0.9132015 0.9963956
H3F3B 530.7844801 -0.1340448 0.2351760 -0.5699766 0.5686936 0.9036121
HCK 118.6186746 -0.3196785 0.4104836 -0.7787851 0.4361063 0.8343287
HDAC1 38.7995574 -1.7095942 1.0451188 -1.6357894 0.1018837 0.4859820
HDAC6 387.1877995 0.8057672 0.2556954 3.1512779 0.0016256 0.0405111
HDAC6bis 4.5244985 -0.1908748 2.6315413 -0.0725335 0.9421774 1.0000000
HECTD3 39.7327770 -0.3873041 1.1084859 -0.3493992 0.7267896 0.9644026
HELLS 58.2065350 0.5327744 0.6518189 0.8173656 0.4137195 0.8195195
HERPUD1 16.7595052 2.0760110 1.4540579 1.4277361 0.1533678 0.5442474
HFE 3.5919966 -3.8880806 3.6585169 -1.0627477 0.2878964 0.7242846
HGF 105.5862631 -0.4653791 0.5673450 -0.8202752 0.4120593 0.8195195
HIF1A 1.4780287 20.2613690 4.7677574 4.2496644 0.0000214 0.0015596
HIGD1A 39.3721669 1.3868607 0.8627990 1.6073972 0.1079673 0.5086816
HINT1 77.2342124 1.9633665 0.8447901 2.3240880 0.0201208 0.2182650
HIP1R 116.0354172 0.0355260 0.4245600 0.0836771 0.9333132 1.0000000
HIST1H3A 234.0769489 -0.0665833 0.2570756 -0.2590030 0.7956329 0.9751363
HIST1H3B 658.5922789 -0.8568283 0.2041394 -4.1972711 0.0000270 0.0017056
HIST1H3C 598.2193725 -0.8332808 0.2758378 -3.0209082 0.0025202 0.0582099
HIST1H3F 375.9623695 -0.0413278 0.2456553 -0.1682349 0.8663985 0.9861531
HIST1H3Fbis 944.6463196 -0.4716504 0.1727018 -2.7310099 0.0063141 0.1013460
HIST1H3Fbis2 395.5794761 -0.4877780 0.2508672 -1.9443678 0.0518511 0.3628707
HIST1H3G 466.6918606 -0.4133351 0.2037171 -2.0289659 0.0424618 0.3200484
HIST1H3H 489.2703800 -0.4686810 0.2114749 -2.2162491 0.0266744 0.2532997
HIST1H3Hbis 725.3774391 -0.6368375 0.1972106 -3.2292260 0.0012413 0.0365789
HIST1H3I 400.8235706 -0.8648830 0.2500347 -3.4590512 0.0005421 0.0177018
HLA-DRB1 115.3365689 1.3986343 0.9771317 1.4313673 0.1523250 0.5442474
HLA-DRB1bis 137.7194753 1.8252041 0.3880767 4.7032044 0.0000026 0.0004818
HLA-DRB1bis2 69.2775162 1.4736785 0.5174624 2.8478949 0.0044009 0.0801480
HLA-E 41.6234272 -1.0624597 1.1331975 -0.9375769 0.3484619 0.7746325
HMG20B 0.0000000 NA NA NA NA NA
HMGB1 209.8763639 -0.1687578 0.2872172 -0.5875616 0.5568266 0.9011983
HMGB2 379.2477056 -0.1154423 0.3201827 -0.3605514 0.7184348 0.9644026
HMMR 11.0776417 -0.3981088 2.4969881 -0.1594356 0.8733257 0.9880121
HMMRbis 10.0472801 3.2177018 2.3714502 1.3568498 0.1748289 0.5871029
HMOX1 421.5574913 -0.0311867 0.2386346 -0.1306879 0.8960222 0.9917563
HNRNPA1 1.4708103 -4.1711049 4.7337836 -0.8811355 0.3782445 0.7992984
HNRNPH1 91.4504396 -0.3521248 0.4770429 -0.7381407 0.4604289 0.8571639
HRAS 56.7248910 -1.1934579 0.8326474 -1.4333293 0.1517637 0.5442474
HSF1 198.1321075 -0.0771215 0.2737355 -0.2817371 0.7781451 0.9751363
HSP90AA1 28.2933080 0.1307941 0.7628435 0.1714560 0.8638652 0.9861531
HSPA1A 41.4738714 -0.7943795 0.6572928 -1.2085625 0.2268310 0.6529146
HSPB8 454.7872102 0.1614676 0.2873012 0.5620149 0.5741059 0.9061304
HSPD1bis 3.2415116 0.0000000 4.0285825 0.0000000 1.0000000 1.0000000
HYOU1 41.8759999 -1.7450518 0.9597490 -1.8182377 0.0690278 0.4275297
ICAM1 24.6886643 -1.6216509 1.0893594 -1.4886279 0.1365854 0.5424108
ICAM2 10.8083361 4.6565237 2.0061845 2.3210845 0.0202823 0.2182650
IDO1 0.0000000 NA NA NA NA NA
IER3 1.1809823 4.1680545 4.7375295 0.8797949 0.3789704 0.7992984
IFI16 3.0145957 1.2219223 4.0811221 0.2994084 0.7646284 0.9693482
IFI27 133.6605816 0.2011118 0.5092755 0.3948979 0.6929182 0.9636086
IFI6 51.6441150 -0.7290505 0.6194995 -1.1768379 0.2392602 0.6738380
IFNAR1 2.2071676 -4.9308917 4.7255229 -1.0434595 0.2967355 0.7280014
IFNB1 115.0826951 -0.4901972 0.4495973 -1.0903027 0.2755798 0.7125520
IFNG 52.7909510 0.5185616 0.9617319 0.5391956 0.5897519 0.9170690
IFNGR1 6.1510082 0.0000000 3.0959334 0.0000000 1.0000000 1.0000000
IGLV3-21 2.4224592 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
IKBKE 46.3759206 -0.7311328 0.6504978 -1.1239589 0.2610305 0.6963264
IL10 79.7137372 0.1729389 0.5041800 0.3430103 0.7315907 0.9644026
IL12A 108.6412831 1.1069182 0.4752477 2.3291395 0.0198517 0.2182650
IL13 2.5923485 -0.8053568 2.5153324 -0.3201791 0.7488326 0.9644026
IL13bis 40.7959949 -1.2359361 0.9741580 -1.2687225 0.2045401 0.6334051
IL15 6.5935537 3.7875812 2.9039857 1.3042699 0.1921416 0.6182408
IL16 179.8707091 0.5403618 0.4351371 1.2418196 0.2143031 0.6393638
IL17A 87.2446406 1.9839449 0.6834404 2.9028790 0.0036975 0.0745006
IL17B 71.6468821 0.1035508 0.6721124 0.1540676 0.8775564 0.9880121
IL17F 77.8016239 1.5195186 0.7893064 1.9251315 0.0542129 0.3683413
IL17RC 7.2777545 1.4023639 2.3685988 0.5920648 0.5538072 0.9001688
IL18 309.7585522 0.1562900 0.2829172 0.5524229 0.5806586 0.9140962
IL1A 45.0736149 -1.8580769 1.0773373 -1.7246938 0.0845827 0.4474849
IL1F10 381.6769491 -0.0244070 0.2415763 -0.1010322 0.9195249 0.9963956
IL1R2 37.0905905 0.5317007 0.8659975 0.6139749 0.5392319 0.9001688
IL1RL1 35.8570444 -1.4589164 1.1822617 -1.2340046 0.2172012 0.6393638
IL1RL2 55.9825928 -1.6233759 1.0178027 -1.5949808 0.1107165 0.5164953
IL1RN 571.6522134 0.0768281 0.1870601 0.4107137 0.6812825 0.9633554
IL2 144.8215332 -0.8067585 1.1294156 -0.7143151 0.4750324 0.8571639
IL20 55.6884174 -2.3504737 0.7213006 -3.2586606 0.0011194 0.0341957
IL20RA 18.4164890 -0.0713215 1.4679206 -0.0485867 0.9612487 1.0000000
IL20RB 54.0160452 -1.2094190 0.6055465 -1.9972358 0.0457996 0.3362186
IL20RBbis 49.2576943 -1.8949042 2.1279701 -0.8904750 0.3732109 0.7978119
IL22RA2 16.8197049 -1.4675835 3.1106781 -0.4717889 0.6370775 0.9498334
IL23A 109.1115991 -0.3430646 0.4501234 -0.7621568 0.4459664 0.8452775
IL25 208.0090943 -0.2033952 0.2638029 -0.7710118 0.4407000 0.8397241
IL27RA 1.0611980 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
IL2RB 26.3807028 0.9785621 0.8703169 1.1243745 0.2608542 0.6963264
IL31RA 48.5051522 -1.3737565 0.9357839 -1.4680274 0.1420968 0.5426034
IL33 56.3907079 0.8600131 0.8466422 1.0157929 0.3097280 0.7355359
IL36A 622.5187231 0.3020199 0.1757729 1.7182393 0.0857530 0.4477945
IL36Abis 469.3636974 -0.0301741 0.1979780 -0.1524111 0.8788627 0.9880121
IL36G 152.6844332 -0.0864951 0.3408484 -0.2537642 0.7996777 0.9751363
IL36RN 120.3478587 -1.0147345 0.4174885 -2.4305686 0.0150752 0.1903489
IL37 152.2799865 0.1430838 0.3725375 0.3840790 0.7009199 0.9636086
IL4 79.2997552 -0.9831006 0.5455881 -1.8019101 0.0715596 0.4289044
IL5RA 51.4116958 0.6499576 1.6271616 0.3994425 0.6895672 0.9636086
IL6R 62.5852980 0.1989099 0.7136619 0.2787172 0.7804618 0.9751363
IL6Rbis 34.0819412 1.8772920 0.9311852 2.0160243 0.0437974 0.3240326
IL7 18.9726126 0.7059398 3.0228136 0.2335373 0.8153442 0.9793368
ILKAP 427.1482358 -0.4282457 0.2556310 -1.6752498 0.0938852 0.4704197
ILVBL 2.1107921 -2.2484682 4.7860146 -0.4697997 0.6384981 0.9498334
INCA1 5.4596668 2.9919675 2.9328781 1.0201472 0.3076586 0.7355359
ING2 46.7277673 0.5585479 0.8135331 0.6865706 0.4923534 0.8666517
ING5bis 98.8481568 -0.7636358 0.6603600 -1.1563931 0.2475204 0.6807668
INHBA 16.4108306 -0.2877931 1.6103093 -0.1787191 0.8581582 0.9850616
INS 68.6444894 -1.4714258 0.8250490 -1.7834407 0.0745146 0.4299714
IRF3 106.1290440 0.0812696 0.4485875 0.1811677 0.8562360 0.9850616
ITGA6 3.3376216 -2.9646695 3.3494635 -0.8851177 0.3760931 0.7992984
ITGB2 7.3090425 -0.8969915 2.1580054 -0.4156577 0.6776605 0.9633554
ITM2C 193.1879647 0.3879220 0.4302934 0.9015291 0.3673071 0.7941548
ITM2Cbis 225.2178055 0.0763841 0.3524256 0.2167382 0.8284124 0.9829258
JAM3 94.6600212 -0.6585099 0.6027323 -1.0925413 0.2745953 0.7124431
JMY 45.1534006 -1.2244490 0.8191027 -1.4948663 0.1349493 0.5415128
JUNB 124.5270874 0.9830930 0.4230238 2.3239665 0.0201273 0.2182650
KARS 16.1534811 -1.0917284 1.3126519 -0.8316968 0.4055801 0.8189469
KATNB1 6.8867684 2.0625198 2.1400842 0.9637564 0.3351681 0.7539291
KCND2 30.9729479 0.5221927 1.1275800 0.4631092 0.6432861 0.9518624
KHDRBS2 633.3182183 -0.3562157 0.2829251 -1.2590460 0.2080137 0.6334051
KIAA0141 54.5318841 0.4301130 0.8300040 0.5182059 0.6043146 0.9290356
KIAA0141bis 44.9809488 0.5015561 0.7146197 0.7018503 0.4827725 0.8593713
KIAA1430 36.1463271 -0.9492442 0.9270724 -1.0239159 0.3058750 0.7355359
KIAA1967 459.3982787 0.2740014 0.2167798 1.2639621 0.2062436 0.6334051
KLC1 81.6095851 -0.3296144 0.4652222 -0.7085097 0.4786288 0.8571639
KLKB1 11.9947984 -0.3334324 2.2173435 -0.1503747 0.8804690 0.9880121
KLKB1bis 7.1375474 -2.1120778 2.5200919 -0.8380955 0.4019771 0.8189469
KPNA6 78.7557231 -0.6349600 0.5209933 -1.2187489 0.2229395 0.6496115
KRT1 26.1320843 -0.4602468 0.8623402 -0.5337184 0.5935364 0.9182334
KRT13 0.8267083 0.0000000 4.8566061 0.0000000 1.0000000 NA
KRT18 148.3032779 -0.2483473 0.3658726 -0.6787807 0.4972769 0.8704634
KRT8 394.9110795 0.0857307 0.2630523 0.3259075 0.7444943 0.9644026
KSR2 31.8415019 0.5228931 1.0077253 0.5188846 0.6038413 0.9290356
LACC1 11.4850743 0.2405318 1.6946032 0.1419399 0.8871275 0.9880121
LAMP1 174.6287309 0.8206232 0.3179390 2.5810716 0.0098494 0.1332485
LAMTOR3 30.2340247 -0.2403589 1.0934588 -0.2198152 0.8260150 0.9829258
LARP1 5.0621559 0.4196523 3.1808473 0.1319310 0.8950389 0.9917563
LAT 41.3861596 -0.2716192 1.2858713 -0.2112336 0.8327050 0.9839667
LAX1 11.8489479 0.6511835 1.6854794 0.3863491 0.6992381 0.9636086
LBP 62.7000058 0.3701211 0.5151538 0.7184673 0.4724692 0.8571639
LCN2 209.8172314 0.8888459 0.3725111 2.3860925 0.0170285 0.2067431
LEP 133.7657871 0.6651693 1.0917198 0.6092858 0.5423351 0.9001688
LEPROTL1 46.9563137 0.7694601 1.0174529 0.7562612 0.4494926 0.8466290
LGALS12 224.7092124 -0.6488591 0.2889472 -2.2455978 0.0247298 0.2465169
LGALS3 152.5985830 -0.2235373 0.3885880 -0.5752553 0.5651187 0.9011983
LGALS9 233.7534276 -0.3616890 0.3342215 -1.0821835 0.2791710 0.7126010
LIN28A 3.2314221 0.3066086 4.5663321 0.0671455 0.9464659 1.0000000
LMNA 134.1075140 -0.4122968 0.3345012 -1.2325717 0.2177356 0.6393638
LMNAbis 86.9566376 -0.6807427 0.4865087 -1.3992407 0.1617408 0.5593427
LOC554223 9.0511458 0.0000000 2.8156339 0.0000000 1.0000000 1.0000000
LPL 78.8947217 1.1354931 0.7431611 1.5279232 0.1265316 0.5311217
LRRC19 18.3714135 1.3875824 2.9824444 0.4652501 0.6417524 0.9517173
LTA 188.7780489 0.4730326 0.3106201 1.5228656 0.1277923 0.5311217
LTBR 53.9485419 -0.2029624 0.8785502 -0.2310197 0.8172995 0.9797248
LYN 460.6606503 -0.2120155 0.2462864 -0.8608496 0.3893209 0.8105478
LYNbis 318.2551371 -0.1288958 0.2420574 -0.5325008 0.5943792 0.9182334
MAEL 29.6678766 3.7213279 1.3518639 2.7527386 0.0059099 0.0981874
MAFG 311.0688365 0.0135121 0.2817366 0.0479602 0.9617480 1.0000000
MAL 349.4930344 -0.0756105 0.2014616 -0.3753096 0.7074302 0.9636086
MAP2K5 121.1239918 -0.5731154 0.4310810 -1.3294844 0.1836882 0.6082264
MAPK13 340.0908299 0.0714251 0.2687205 0.2657970 0.7903956 0.9751363
MAPK14 151.0191768 -0.5539120 0.3503241 -1.5811415 0.1138457 0.5182650
MAPK8 35.4364846 0.9501463 0.8885656 1.0693035 0.2849329 0.7214746
MAPK8IP1 9.9517952 1.6707071 1.6475339 1.0140654 0.3105515 0.7355359
MAPK8IP2 171.2293704 0.2823088 0.2876841 0.9813154 0.3264372 0.7474380
MAPK9 202.4549873 0.7726122 0.3313219 2.3319083 0.0197055 0.2182650
MAPKAPK2 20.5461488 4.8717823 4.3966771 1.1080601 0.2678359 0.7084932
MAPKAPK5 398.7340636 0.0564176 0.2218433 0.2543128 0.7992539 0.9751363
MAPT 598.5460422 -0.1418569 0.2403925 -0.5901054 0.5551200 0.9001688
MAS1 57.4909061 1.0530422 0.7932718 1.3274671 0.1843542 0.6083047
MAZ 177.7667463 0.1848699 0.2890255 0.6396318 0.5224120 0.8855050
MBL2 54.3498896 -0.2957894 0.9498323 -0.3114122 0.7554873 0.9644026
MBP 833.9942347 -0.3049101 0.2049592 -1.4876622 0.1368400 0.5424108
MCL1 19.0553814 1.3331239 1.3642521 0.9771830 0.3284786 0.7495643
MCPH1 62.1447530 0.4747415 0.6338934 0.7489296 0.4538997 0.8511742
MCTS1 39.7001577 0.5592316 1.1647571 0.4801272 0.6311370 0.9457068
MDK 74.1678309 0.9812316 0.6601194 1.4864457 0.1371613 0.5424108
MED1 32.6473983 -1.4259015 0.7976426 -1.7876447 0.0738333 0.4299714
MEIS2 0.0000000 NA NA NA NA NA
METTL21C 286.2497210 0.5207026 0.2676376 1.9455510 0.0517087 0.3628707
MFF 207.8857103 0.3730958 0.3913881 0.9532629 0.3404569 0.7640110
MGLL 618.2818936 0.0431803 0.1923369 0.2245036 0.8223655 0.9829258
MGST2 37.1126472 -0.1732470 1.1320504 -0.1530382 0.8783681 0.9880121
MIF 700.0911671 0.2675100 0.1673467 1.5985375 0.1099234 0.5153340
MITF 65.2711319 1.3557700 0.7384695 1.8359188 0.0663697 0.4190138
MITFbis 255.4095235 -0.1740723 0.3170506 -0.5490363 0.5829806 0.9140962
MKNK2 42.6836672 0.3522905 0.7830938 0.4498701 0.6528041 0.9586285
MLH1 31.1278877 -0.6071289 1.2393390 -0.4898812 0.6242180 0.9398004
MLLT11 913.5791620 -0.1754305 0.1615947 -1.0856204 0.2776470 0.7125520
MMP10 19.8488070 0.4248913 1.5507431 0.2739921 0.7840907 0.9751363
MMP26 2.8617654 1.2546373 2.7907060 0.4495770 0.6530155 0.9586285
MMP3 40.9637848 0.1085854 1.2053068 0.0900894 0.9282162 1.0000000
MMP9 6.2542114 -2.4940268 2.4387746 -1.0226557 0.3064707 0.7355359
MOAP1 61.1914993 -0.7953304 0.5646420 -1.4085570 0.1589662 0.5543983
MSH2 4.1934924 -4.1846665 3.2191377 -1.2999340 0.1936236 0.6182408
MSX2 5.4794836 7.0626601 3.1994635 2.2074513 0.0272825 0.2532997
MT1DP 1.0443467 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
MUC1 25.7240051 0.5545369 1.2958871 0.4279207 0.6687089 0.9627484
MUC1bis 70.8201900 -1.0386066 0.7424351 -1.3989190 0.1618373 0.5593427
MVK 326.6053607 -0.5000346 0.2675530 -1.8689177 0.0616343 0.3970588
MYB 29.5325220 2.4789956 1.3741515 1.8040192 0.0712283 0.4289044
MYD88 306.6383084 -0.0194081 0.2482409 -0.0781826 0.9376828 1.0000000
MYLK3 11.0415208 -2.8822343 1.6514743 -1.7452493 0.0809415 0.4430727
MZF1 16.7949101 0.0991635 1.2306856 0.0805758 0.9357793 1.0000000
NAMPT 17.0250496 -0.2676354 1.7658298 -0.1515635 0.8795312 0.9880121
NANOG 522.0342155 0.4766443 0.2331283 2.0445581 0.0408984 0.3200484
NANOS3 282.1584780 1.0578628 0.3390448 3.1201272 0.0018077 0.0438954
NCF1 410.8852104 -0.0884368 0.2432282 -0.3635960 0.7161597 0.9644026
NCF2 127.7588897 -0.1468843 0.3936114 -0.3731709 0.7090212 0.9636086
NCK1 40.0043580 -2.0339971 1.1518347 -1.7658759 0.0774167 0.4363904
NCK2 128.9373932 -0.2792490 0.4368806 -0.6391883 0.5227004 0.8855050
NCKIPSD 115.8192543 -0.7494426 0.4135843 -1.8120672 0.0699758 0.4275297
NDE1 39.0806571 -0.1758679 0.7161034 -0.2455901 0.8059996 0.9760634
NDEL1 86.2342060 -0.2457935 0.5877493 -0.4181945 0.6758049 0.9633554
NDUFA13 98.9386889 -0.2409426 0.5872069 -0.4103197 0.6815714 0.9633554
NDUFS3 67.8292301 -0.4275384 0.6549637 -0.6527666 0.5139068 0.8783129
NEFL 107.3188790 0.8940006 0.4540353 1.9690114 0.0489518 0.3511920
NEFM 7.0741934 0.9320749 1.9769440 0.4714726 0.6373033 0.9498334
NEK2 50.2513271 1.3590676 1.0465603 1.2986043 0.1940798 0.6182408
NEUROD1 51.8690482 1.5244121 0.8893996 1.7139788 0.0865326 0.4477945
NFATC1 16.4577236 -0.2681143 1.1064503 -0.2423194 0.8085327 0.9773957
NFATC1bis 47.3651739 0.2543580 0.6813666 0.3733057 0.7089209 0.9636086
NFE2L1 16.6423983 -5.0167675 1.6958431 -2.9582734 0.0030937 0.0665843
NFE2L2 85.3881461 1.6545964 0.5060131 3.2698689 0.0010760 0.0339649
NFKB1 33.7572268 -0.4433730 0.7502837 -0.5909404 0.5545603 0.9001688
NFKBIA 369.7745554 0.4004281 0.2334675 1.7151342 0.0863206 0.4477945
NGF 130.8143419 1.0999365 0.5230164 2.1030632 0.0354602 0.2941416
NGFRAP1 250.2807871 0.2204200 0.2706352 0.8144543 0.4153848 0.8195195
NLRP10 25.5021683 -1.4292139 0.8560410 -1.6695624 0.0950060 0.4714691
NLRP12 5.9817521 1.1532492 1.9830541 0.5815521 0.5608684 0.9011983
NLRX1 53.9398293 -1.5611162 0.5454835 -2.8618943 0.0042112 0.0801480
NME5 71.4817150 -0.2496205 0.6393342 -0.3904382 0.6962125 0.9636086
NMT1 277.4703811 -0.2674090 0.2951105 -0.9061316 0.3648662 0.7924960
BC008840 2.6080618 0.0000000 4.7361997 0.0000000 1.0000000 1.0000000
BC110533 0.6795433 0.6115803 4.7995630 0.1274242 0.8986047 NA
NOC2L 92.4455482 -0.5853811 0.4044707 -1.4472769 0.1478194 0.5442474
NONO 244.1623490 -0.0968269 0.3699871 -0.2617034 0.7935501 0.9751363
NOV 73.2013302 0.4111538 0.5637998 0.7292550 0.4658457 0.8571639
NOX1 11.8865545 2.0883144 2.2444875 0.9304193 0.3521540 0.7771323
NOX5 5.0893791 -5.2110063 2.9244431 -1.7818799 0.0747688 0.4299714
NPFF 166.6648662 0.3842712 0.4646025 0.8270967 0.4081822 0.8189469
NPPA 119.1236079 0.6672608 0.5498148 1.2136102 0.2248966 0.6508382
NR1D2 10.7007813 4.2881648 2.5073119 1.7102638 0.0872171 0.4488837
NR1H3 103.1152784 -0.0797552 0.5045892 -0.1580596 0.8744098 0.9880121
NR1H3bis 65.5544500 -0.4408267 0.6640776 -0.6638180 0.5068068 0.8714234
NR1H4 22.9571186 -1.5169405 1.2764563 -1.1883999 0.2346759 0.6693918
NR2C2 93.3760800 -0.3174587 0.4192790 -0.7571540 0.4489576 0.8466290
NR3C1 56.3899773 -0.4403635 0.6218010 -0.7082065 0.4788170 0.8571639
NR4A1 221.2595026 -0.3238379 0.2999492 -1.0796426 0.2803014 0.7135629
NR5A1 254.9549609 -0.3587205 0.2383975 -1.5047156 0.1323972 0.5415128
NRP1 17.1990233 0.4938739 1.5986641 0.3089292 0.7573754 0.9649886
NT5C1A 3.4531660 0.4531853 4.3546140 0.1040701 0.9171137 0.9963956
NT5E 29.6689858 1.1795626 1.0896128 1.0825521 0.2790073 0.7126010
NT5Ebis 15.3166563 -2.6947383 1.7469418 -1.5425461 0.1229409 0.5225185
NTNG1 2.9615221 -22.2968947 4.7773009 -4.6672577 0.0000031 0.0004818
NTRK3 25.7010335 0.2901929 1.3470248 0.2154325 0.8294301 0.9829258
NUP50 328.5697193 -0.8173132 0.3138507 -2.6041462 0.0092103 0.1301820
NUPR1 330.4161883 0.1376517 0.2427388 0.5670774 0.5706616 0.9037066
O3FAR1 33.5283919 0.9704507 0.9868609 0.9833712 0.3254248 0.7474380
OGG1 22.6593776 -1.9961810 1.2549149 -1.5906903 0.1116793 0.5182650
OPA1 1.0937004 -2.0110984 4.8033282 -0.4186885 0.6754438 0.9633554
OPRM1 31.2856887 -0.3103522 1.4139743 -0.2194893 0.8262689 0.9829258
OR51E1 2.3762068 17.1620484 4.8080545 3.5694372 0.0003577 0.0125477
OSM 54.2101768 -0.0614561 0.9586542 -0.0641066 0.9488853 1.0000000
P2RX1 53.3558395 0.1660012 0.7131617 0.2327679 0.8159416 0.9793368
P2RX1bis 34.2659178 -0.9962918 0.7973682 -1.2494751 0.2114913 0.6393638
P4HB 39.5806494 -0.5586674 0.8824485 -0.6330878 0.5266763 0.8874776
PAK1 14.7941470 -0.2608582 1.9631044 -0.1328805 0.8942879 0.9917563
PAK7 77.4198319 0.3875468 0.4659637 0.8317103 0.4055725 0.8189469
PAM16 74.0805066 -0.1234144 0.6416069 -0.1923520 0.8474665 0.9839667
PAPSS2 66.3211189 -0.0652761 0.6073752 -0.1074725 0.9144141 0.9963956
PARK2 27.3059386 -0.0432094 1.0369828 -0.0416683 0.9667631 1.0000000
PBK 120.6188281 0.4905580 0.4300764 1.1406299 0.2540240 0.6853581
PCGF2 381.7227606 0.3353651 0.2341017 1.4325618 0.1519831 0.5442474
PCLO 415.1103361 0.2497760 0.2124156 1.1758837 0.2396414 0.6738380
PCP4 0.0000000 NA NA NA NA NA
PCYT1A 420.6553055 -0.1436483 0.2052387 -0.6999083 0.4839845 0.8599125
PDCD10 129.4658214 -0.7593360 0.4085986 -1.8583910 0.0631135 0.4038412
PDCD5 406.2429024 0.1748024 0.2120998 0.8241515 0.4098535 0.8189469
PDCL3 823.7615901 -0.1271998 0.2068734 -0.6148677 0.5386421 0.9001688
PDE4D 73.1461871 1.4601251 0.5619722 2.5982158 0.0093710 0.1305044
PDE4Dbis 20.7629763 -2.0940996 1.3540165 -1.5465835 0.1219637 0.5225185
PDE6G 106.5190639 -0.2178607 0.6312516 -0.3451250 0.7300004 0.9644026
PDIA3 2.3630672 -0.4345514 3.0895250 -0.1406531 0.8881440 0.9880121
PDIA3bis 15.2067842 0.4730971 1.7572698 0.2692228 0.7877582 0.9751363
PDK1 22.6973925 -1.1808433 0.8125637 -1.4532317 0.1461594 0.5442474
PDPK1 126.0372316 -0.9646660 0.4003104 -2.4097947 0.0159615 0.1963057
PDXDC1 55.6617036 -0.2600386 0.5818673 -0.4469035 0.6549447 0.9586285
PEA15 79.9798712 0.6530219 0.6183341 1.0560988 0.2909231 0.7242846
PEA15bis 42.3420492 -0.5701046 0.9779228 -0.5829750 0.5599101 0.9011983
PEA15bis2 1.3122605 0.1747889 4.4180431 0.0395625 0.9684419 1.0000000
PELI3 243.7513147 -0.0516534 0.2602623 -0.1984666 0.8426800 0.9839667
PER1 146.8118942 0.2593889 0.3568577 0.7268693 0.4673060 0.8571639
PER1bis 259.7689263 0.6532873 0.2493525 2.6199348 0.0087947 0.1281314
PF4 51.7237377 0.6693629 0.8137773 0.8225382 0.4107706 0.8189469
PGK1 138.9200405 -0.0534611 0.4107963 -0.1301402 0.8964555 0.9917563
PGLYRP1 61.1574725 1.0793860 0.4873094 2.2149911 0.0267607 0.2532997
PHIP 18.9939667 2.7889483 1.6128007 1.7292579 0.0837629 0.4456377
PHLDA3 123.0310643 0.4363949 0.3776359 1.1555968 0.2478461 0.6807668
PHLDB1 109.5320975 0.3265635 0.4555309 0.7168856 0.4734447 0.8571639
PIAS4 25.4883680 -2.6029633 1.0501786 -2.4785911 0.0131902 0.1711118
PIH1D1 582.7653490 0.3325654 0.2034477 1.6346477 0.1021229 0.4859820
PIK3AP1 67.8054575 -0.8015685 0.6947768 -1.1537064 0.2486205 0.6807668
PIK3R1 68.1142285 -0.3997088 0.6043946 -0.6613375 0.5083959 0.8721937
PIKFYVE 2.2603891 -3.7189956 4.7410563 -0.7844234 0.4327917 0.8330360
PKM2 9.3947075 -0.6868407 2.2069489 -0.3112173 0.7556354 0.9644026
PLA2G2D 68.9895179 0.8086372 0.6221903 1.2996621 0.1937168 0.6182408
PLA2G7 7.1873323 -2.2258491 2.6326175 -0.8454890 0.3978379 0.8189469
PLAT 11.0719525 1.4793121 1.6020544 0.9233845 0.3558069 0.7771323
PLAUR 75.1889347 1.6956085 0.5940957 2.8541001 0.0043159 0.0801480
PLCB1 2.1229729 -3.7353961 4.7498479 -0.7864244 0.4316189 0.8330360
PLD3 63.5045675 0.1961535 0.5693381 0.3445291 0.7304484 0.9644026
PLD4 136.8093938 -0.9088164 0.4220664 -2.1532546 0.0312987 0.2747532
PLEKHF1 246.7645781 -0.1687253 0.2853194 -0.5913561 0.5542819 0.9001688
PLSCR1 48.2530498 -0.1104364 0.9239203 -0.1195302 0.9048553 0.9963956
PMAIP1 190.1712348 0.4118676 0.4180641 0.9851780 0.3245367 0.7474380
PML 33.5691930 -0.6135790 0.8349316 -0.7348854 0.4624093 0.8571639
PMS1 1.5544840 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
PNMA1 119.5272138 -0.0429008 0.3807806 -0.1126654 0.9102958 0.9963956
POLB 66.8611454 -0.0402010 0.8281382 -0.0485439 0.9612828 1.0000000
POU5F1 165.2322306 -0.3752647 0.3449963 -1.0877356 0.2767118 0.7125520
PPARA 151.3827287 -0.1399432 0.3374857 -0.4146642 0.6783877 0.9633554
PPARD 256.0250405 -0.2600736 0.2969580 -0.8757923 0.3811429 0.8012022
PPARG 10.3483913 0.3649275 2.0777544 0.1756355 0.8605803 0.9854529
PPIA 369.6286106 0.0828193 0.2285166 0.3624215 0.7170371 0.9644026
PPIAbis 16.6149484 0.2270938 0.9065797 0.2504951 0.8022045 0.9751363
PPP1CA 764.2205302 0.0760192 0.2521239 0.3015151 0.7630218 0.9686081
PPP1R13B 6.7215193 -5.6756178 2.5173026 -2.2546427 0.0241558 0.2433565
PPP1R15A 30.2445834 -0.6062151 0.8945750 -0.6776571 0.4979891 0.8704634
PPP2R1A 93.9823392 0.0610118 0.4367379 0.1396989 0.8888979 0.9880121
PPP2R5C 34.4292250 -0.0932669 0.9430927 -0.0988947 0.9212219 0.9970253
PPP3CC 68.5332361 -0.9175795 0.7130313 -1.2868714 0.1981391 0.6275510
PRDX2 221.1471870 0.2758744 0.3404627 0.8102926 0.4177720 0.8197691
PRDX6 393.5744892 -0.1835479 0.2672205 -0.6868779 0.4921597 0.8666517
PRELID1 54.3090532 2.2899781 1.0570535 2.1663786 0.0302823 0.2747532
PRKCA 35.8147161 -0.9226554 0.8070571 -1.1432343 0.2529413 0.6853581
PRKRA 35.9061407 0.3224758 1.0339347 0.3118918 0.7551228 0.9644026
PROC 40.6301183 0.2288864 1.0682953 0.2142538 0.8303491 0.9829258
PRODH 52.8748286 -0.9977801 0.6287166 -1.5870109 0.1125102 0.5182650
PRRC1 3.4158295 -4.8901709 3.8622431 -1.2661479 0.2054602 0.6334051
PSMA1 79.3547800 -0.5953347 0.5615739 -1.0601182 0.2890908 0.7242846
PSMA1bis 64.4134957 1.4832639 0.7292305 2.0340123 0.0419504 0.3200484
PSMB4 83.3545885 -0.1795557 0.5830980 -0.3079340 0.7581326 0.9649886
PSMD10 210.1632958 -0.1809087 0.3034727 -0.5961283 0.5510895 0.9001688
PSME3 117.7367255 0.8906204 0.5027510 1.7714940 0.0764786 0.4336840
PTGER3 13.8048767 -3.7389447 1.7863230 -2.0930955 0.0363406 0.2941416
PTGER4 23.5289562 1.5132703 1.2162412 1.2442190 0.2134190 0.6393638
PTGES 41.0196316 -1.7655422 0.8204467 -2.1519281 0.0314030 0.2747532
PTGIS 35.6405964 -0.2202651 1.1551701 -0.1906776 0.8487782 0.9839667
PTH 69.0170792 -0.0043055 0.5632549 -0.0076440 0.9939011 1.0000000
PTPN11 594.9847715 -0.1003164 0.2055399 -0.4880630 0.6255052 0.9402436
PTPN12 8.8186404 -6.4026688 2.3909429 -2.6778844 0.0074089 0.1130987
PTPN2 19.5492931 -1.0623656 1.5988509 -0.6644557 0.5063987 0.8714234
PTPN6 111.3032368 0.7057436 0.4006333 1.7615700 0.0781420 0.4378725
PTPRE 9.9427284 5.8781622 2.2973209 2.5587031 0.0105063 0.1401339
PTPRR 42.3735968 2.1710214 0.8821092 2.4611708 0.0138484 0.1772227
PTPRRbis 54.0852257 3.6143229 0.7934436 4.5552359 0.0000052 0.0006034
PTTG1 94.4949553 1.0370327 0.5068160 2.0461720 0.0407395 0.3200484
PYCARD 517.6360511 0.0922389 0.1707946 0.5400577 0.5891573 0.9170690
QARS 111.9767079 -0.0269694 0.3747572 -0.0719649 0.9426299 1.0000000
QARSbis 117.0748854 -0.1915121 0.4272829 -0.4482090 0.6540024 0.9586285
RAB4A 132.6168686 -0.1601018 0.3636419 -0.4402731 0.6597393 0.9611894
RAB5A 35.0185951 0.7888986 1.0112891 0.7800921 0.4353367 0.8343287
RABGEF1 46.2507986 0.0914802 0.7722806 0.1184546 0.9057075 0.9963956
RABGGTB 0.0000000 NA NA NA NA NA
RAC1 274.2967408 1.2576782 0.3312989 3.7962039 0.0001469 0.0060496
RAPGEF5 2.3566505 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
RARA 345.7142988 -0.0523043 0.2518346 -0.2076931 0.8354686 0.9839667
RARG 137.5317056 0.4343433 0.4173974 1.0405990 0.2980617 0.7293655
RASGRP2 40.2122823 -0.0699229 0.6867004 -0.1018245 0.9188960 0.9963956
RBCK1 7.6901904 -3.9866204 2.3013933 -1.7322638 0.0832266 0.4456377
RBM17 55.3420651 0.9398684 0.9357058 1.0044486 0.3151624 0.7442863
RBMX 53.5440659 -0.4731761 0.7990605 -0.5921655 0.5537398 0.9001688
RBPJ 26.9684751 -2.1564340 1.0624041 -2.0297681 0.0423801 0.3200484
RBPMS 47.6625768 1.1445127 0.8931762 1.2813964 0.2000545 0.6314495
RCAN1 163.2470255 0.2200850 0.4109541 0.5355463 0.5922721 0.9179734
REG3A 87.6577780 0.0554958 0.4888537 0.1135224 0.9096164 0.9963956
REG3G 90.6871012 0.6056245 1.1021603 0.5494886 0.5826702 0.9140962
RELA 441.5672077 0.3412023 0.2322268 1.4692631 0.1417615 0.5426034
RELL1 69.0291132 0.3396390 0.7309587 0.4646487 0.6421830 0.9517173
REN 1.1809823 4.1680545 4.7375295 0.8797949 0.3789704 0.7992984
RET 31.6272157 0.6429079 1.1243045 0.5718272 0.5674390 0.9031341
RETbis 0.8876380 -0.9025190 4.8451690 -0.1862719 0.8522315 NA
RFFL 352.8393553 0.0326524 0.2226295 0.1466669 0.8833949 0.9880121
RHBDD3 69.4617671 0.2096908 0.6698697 0.3130322 0.7542562 0.9644026
RHOA 102.3123010 1.2331575 0.5603652 2.2006317 0.0277621 0.2552497
RHOAbis 1.4076324 -0.3648903 3.1704194 -0.1150921 0.9083721 0.9963956
RHOT2 112.5597157 -0.2894087 0.4588243 -0.6307615 0.5281965 0.8884584
RICTOR 49.1700953 -0.5559124 0.6024693 -0.9227233 0.3561514 0.7771323
RIPK3 190.2651853 -0.5956294 0.3638643 -1.6369547 0.1016399 0.4859820
RNF183 98.3487979 0.0514939 0.5532311 0.0930784 0.9258413 0.9997397
RNF41 340.5006393 -0.0425578 0.2372080 -0.1794114 0.8576147 0.9850616
RPL11 308.5135195 -0.2152459 0.2559364 -0.8410131 0.4003406 0.8189469
RPL26 104.1397622 -0.1123500 0.5894620 -0.1905974 0.8488410 0.9839667
RPS19 935.4936917 -0.1530436 0.1486294 -1.0296993 0.3031512 0.7355359
RPS27L 186.8910402 0.3507334 0.3544708 0.9894566 0.3224398 0.7474380
RPS27Lbis 52.4700081 -0.3194111 0.8683213 -0.3678490 0.7129858 0.9644026
RPS3 569.7854852 -0.2257615 0.1832970 -1.2316701 0.2180723 0.6393638
RPS6KA1 1.6265427 0.9919184 4.7492513 0.2088579 0.8345592 0.9839667
RPS6KA3 50.7243454 2.0250790 0.9461584 2.1403172 0.0323291 0.2783245
RPS6KA4 70.7989156 0.4708369 0.5958342 0.7902146 0.4294024 0.8315830
RPS6KA5 53.2260690 -0.2335071 0.6196205 -0.3768551 0.7062813 0.9636086
RPS6KB1 201.5919050 -0.0458466 0.2594902 -0.1766796 0.8597601 0.9854529
RPS7 52.7538677 -0.6337357 1.0625612 -0.5964228 0.5508928 0.9001688
RPSAbis 3.4216287 -18.7332982 4.3026444 -4.3539034 0.0000134 0.0011513
RPTOR 60.8176258 -0.7862922 0.5018877 -1.5666695 0.1171920 0.5225185
RRM2B 68.8773895 -0.1706116 0.8266866 -0.2063800 0.8364941 0.9839667
RRN3 24.7657565 -0.6089901 1.1130315 -0.5471454 0.5842788 0.9140962
RSPH3 74.6751499 0.7616922 0.7498344 1.0158139 0.3097180 0.7355359
RTN4 33.4164873 0.8026838 0.8115675 0.9890536 0.3226369 0.7474380
RUNX1 236.0814406 -0.5150520 0.3282187 -1.5692342 0.1165934 0.5225185
S100A8 74.4129551 1.2958782 0.6806640 1.9038443 0.0569305 0.3728392
S100A9 52.7016301 -1.1136401 0.6901689 -1.6135763 0.1066194 0.5048427
SAA1 36.9345020 2.4678989 0.9225584 2.6750599 0.0074716 0.1130987
SAA2 82.5867644 -1.2632051 0.6019355 -2.0985724 0.0358546 0.2941416
SAA4 34.9518733 -1.2750488 1.0518162 -1.2122353 0.2254223 0.6508382
SAFB2 151.6527521 -0.0726032 0.3831431 -0.1894937 0.8497059 0.9839667
SCAMP1 2.9862327 -2.6793618 3.9939294 -0.6708586 0.5023106 0.8714234
SCG2 25.9360404 -0.3347373 1.5196383 -0.2202743 0.8256575 0.9829258
SCGB1A1 97.5944404 -0.4917619 0.6129448 -0.8022939 0.4223830 0.8230384
SCNN1B 31.6019045 -2.0441740 1.1018493 -1.8552211 0.0635647 0.4039982
SCNN1G 25.0172765 -2.6069093 1.4415248 -1.8084388 0.0705382 0.4282033
SELE 25.1716920 -3.0504858 1.3720805 -2.2232557 0.0261986 0.2532997
SEMA7A 4.8209288 3.1276662 2.4574200 1.2727438 0.2031090 0.6334051
SERPINA3 95.3120459 0.4928562 0.5004501 0.9848258 0.3247096 0.7474380
SERPINC1 48.9508841 -3.2140754 1.6892699 -1.9026417 0.0570873 0.3728392
SERPINF1 70.7722452 0.4594891 0.7705652 0.5963014 0.5509739 0.9001688
SETD6 45.8699308 0.2134350 0.5821833 0.3666114 0.7139089 0.9644026
SFN 451.2809536 0.1528306 0.2345946 0.6514669 0.5147451 0.8783129
SFPQ 18.4347443 2.0923279 1.6025724 1.3056058 0.1916866 0.6182408
SFRP1 45.5478945 0.4630822 0.7979485 0.5803410 0.5616847 0.9011983
SFRP2 234.0933471 0.4057305 0.2916364 1.3912205 0.1641586 0.5632542
SGK1 39.6813840 -1.5405974 1.2492903 -1.2331781 0.2175093 0.6393638
SH2B1 266.9448047 -0.2983488 0.2912483 -1.0243794 0.3056561 0.7355359
SH2D3C 14.1533771 0.2882776 1.3296690 0.2168040 0.8283611 0.9829258
SH3GLB1 129.0413701 -0.2037956 0.3180298 -0.6408067 0.5216483 0.8855050
SHARPIN 161.3754747 0.5226934 0.3861684 1.3535375 0.1758840 0.5885588
SHC1 169.3256653 -0.5727348 0.3766667 -1.5205346 0.1283767 0.5311217
SHISA5 47.6764259 -0.1961531 0.8923458 -0.2198174 0.8260134 0.9829258
SHPK 176.1304542 -0.4149658 0.3367161 -1.2323907 0.2178032 0.6393638
SIAH1 13.1601770 -3.0535311 2.1100523 -1.4471352 0.1478591 0.5442474
SIGLEC10 6.1132214 -6.3096520 3.1138280 -2.0263328 0.0427307 0.3200484
SIVA1 129.4671716 1.8952016 0.4180545 4.5333841 0.0000058 0.0006034
SLAMF8 73.3937875 0.0919257 0.6151215 0.1494432 0.8812039 0.9880121
SLC25A5 31.4433500 -1.3171661 1.0153561 -1.2972455 0.1945467 0.6182408
SLC7A2 7.2481554 -4.4205593 2.8629763 -1.5440433 0.1225778 0.5225185
SLC9A1 16.6622076 -1.1693831 1.2072758 -0.9686131 0.3327383 0.7538353
SLC9A3R1 261.8676753 0.4305152 0.2985502 1.4420196 0.1492968 0.5442474
SMAD1 99.5355108 -0.0715577 0.4532856 -0.1578646 0.8745635 0.9880121
SMAD2 192.9030418 0.9701888 0.3637169 2.6674284 0.0076434 0.1130987
SMAD3 746.2447989 0.0164482 0.1619381 0.1015710 0.9190972 0.9963956
SMAD3bis 262.6627598 0.0856723 0.2643522 0.3240840 0.7458745 0.9644026
SMAD4 128.3167821 -0.1256083 0.3337831 -0.3763170 0.7066812 0.9636086
SMARCAD1 1.2939190 20.0332702 4.7660593 4.2033195 0.0000263 0.0017056
SMPDL3B 112.9019650 0.3595094 0.5056246 0.7110204 0.4770716 0.8571639
SMYD3 1.8698886 4.8199067 4.7281407 1.0194085 0.3080091 0.7355359
SNAI1 129.3332277 0.7297523 0.4341746 1.6807806 0.0928055 0.4674832
SNAP23 67.3156804 -1.7825839 0.8292238 -2.1497017 0.0315788 0.2747532
SNCA 806.2714404 -0.1136340 0.1866742 -0.6087290 0.5427041 0.9001688
SNCG 545.1620934 0.0745206 0.2202791 0.3383009 0.7351365 0.9644026
SNIP1 161.7046442 -0.5068623 0.4349274 -1.1653953 0.2438590 0.6792191
SNW1 22.1930531 0.8460226 1.2496423 0.6770118 0.4983984 0.8704634
SNX4 81.6408919 0.3930188 0.4850293 0.8102992 0.4177683 0.8197691
SOCS3 240.3567917 0.0822301 0.3427495 0.2399131 0.8103976 0.9773957
SOCS5 2.9980564 0.0000000 4.1280250 0.0000000 1.0000000 1.0000000
SOD1 205.0791254 -0.1021290 0.2838790 -0.3597624 0.7190248 0.9644026
SOD2 474.6016885 -0.0536275 0.2099139 -0.2554736 0.7983573 0.9751363
SORBS3 505.8813592 0.1219511 0.2037690 0.5984771 0.5495216 0.9001688
SOX10 104.3600305 0.0563521 0.3940058 0.1430234 0.8862717 0.9880121
SOX2 526.0346927 -0.1758226 0.1863101 -0.9437094 0.3453182 0.7694501
SP100 168.5770703 -0.2591509 0.3942084 -0.6573958 0.5109265 0.8749500
SPAST 13.7918473 -1.3725031 1.2552250 -1.0934320 0.2742042 0.7124431
SPATA2 155.4717887 -0.0057254 0.3507605 -0.0163228 0.9869769 1.0000000
SPHK1 73.9183442 -0.2238394 0.5077875 -0.4408132 0.6593483 0.9611894
SPN 71.3396475 -0.7635186 0.5570377 -1.3706766 0.1704758 0.5750708
SREBF1 0.6366254 1.4871250 4.8313243 0.3078090 0.7582277 NA
SRPX 34.2976063 -4.3489653 1.0736940 -4.0504701 0.0000511 0.0028474
SSBP3 1.9949446 0.9263404 4.7622457 0.1945176 0.8457706 0.9839667
SSBP3bis 30.3943271 0.0602164 1.0878935 0.0553514 0.9558585 1.0000000
ST3GAL2 0.8386887 3.7652279 4.7497844 0.7927155 0.4279436 NA
STAP1 141.7917476 -0.1481928 0.4058571 -0.3651353 0.7150104 0.9644026
STAR 89.8430003 -0.3095368 0.4328648 -0.7150888 0.4745542 0.8571639
STAT1 12.6023152 0.4255792 1.3099077 0.3248925 0.7452624 0.9644026
STAT3 69.6851405 -0.0050695 0.6177130 -0.0082068 0.9934520 1.0000000
STAT5A 23.2416475 -0.8426641 0.9432524 -0.8933601 0.3716644 0.7978119
STAT5B 119.6598979 -0.5413566 0.4095236 -1.3219181 0.1861954 0.6101283
STK10 26.8636816 -2.5175482 1.4549135 -1.7303766 0.0835630 0.4456377
STK11 164.2624217 0.0360220 0.3149406 0.1143770 0.9089389 0.9963956
STK25 149.8935194 -0.3186929 0.2686124 -1.1864418 0.2354479 0.6695770
STK4 360.0840596 1.1691784 0.2590304 4.5136719 0.0000064 0.0006034
STMN1 596.7058250 0.0598393 0.2263519 0.2643641 0.7914994 0.9751363
STMN2 605.0509161 -0.0643648 0.2030319 -0.3170181 0.7512299 0.9644026
STMN3 362.0886656 -0.1071294 0.2471405 -0.4334757 0.6646692 0.9627484
STRADB 37.1095327 0.2176756 0.8654006 0.2515316 0.8014031 0.9751363
STX4 578.1767650 0.1365468 0.1967264 0.6940951 0.4876226 0.8647539
SUCNR1 72.3337621 0.4872355 0.5779481 0.8430437 0.3992040 0.8189469
SULT4A1 118.1838226 -1.4760712 0.3999863 -3.6903046 0.0002240 0.0088381
SULT4A1bis 218.9246461 -0.2393249 0.2638423 -0.9070756 0.3643668 0.7924960
SUPT5H 4.4663856 -0.1402619 2.7048403 -0.0518559 0.9586435 1.0000000
SUPV3L1 11.9769212 -0.0937975 1.8683803 -0.0502026 0.9599610 1.0000000
SYK 0.7046512 0.0000000 4.8440695 0.0000000 1.0000000 NA
SYN3 34.0419339 0.0851385 1.1245423 0.0757095 0.9396502 1.0000000
SYT11 22.5727393 -0.1999763 1.2300248 -0.1625791 0.8708499 0.9876585
TAC1 26.8670583 -1.5351795 1.3329749 -1.1516942 0.2494468 0.6807668
TACR1 14.5148948 0.0320578 1.7693465 0.0181184 0.9855444 1.0000000
TAGLN2 101.9595578 0.0416291 0.5858864 0.0710532 0.9433554 1.0000000
TAL2 39.5405677 -0.3116850 0.7788462 -0.4001881 0.6890180 0.9636086
TBC1D23 54.8518692 -2.0102789 1.6230371 -1.2385908 0.2154971 0.6393638
TCF7L2 70.4693651 -0.6304989 0.7812488 -0.8070398 0.4196436 0.8197691
TEFM 1.8866562 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
TFAP4 310.1230356 -0.0138830 0.2391509 -0.0580512 0.9537079 1.0000000
TFCP2 51.9587122 -0.9967652 0.9381948 -1.0624289 0.2880410 0.7242846
TFDP2 69.2460704 0.1128587 0.6680153 0.1689462 0.8658389 0.9861531
TFEB 233.8897584 -0.0632686 0.3022918 -0.2092965 0.8342168 0.9839667
TFPI 52.7161081 -1.9802502 1.5722397 -1.2595091 0.2078465 0.6334051
TFR2 12.6636450 -1.0824186 1.4550211 -0.7439195 0.4569252 0.8551544
TGM2 76.4738718 1.1554548 0.5679092 2.0345766 0.0418935 0.3200484
TGS1 166.2262165 -0.5419752 0.3689211 -1.4690817 0.1418106 0.5426034
TH 82.9484863 -0.8462459 0.4751576 -1.7809794 0.0749158 0.4299714
THAP2 6.1931716 1.4232123 2.8637341 0.4969778 0.6192047 0.9368423
THRB 81.8771498 0.6623216 0.5878203 1.1267416 0.2598517 0.6963264
TICAM1 179.4661407 0.2652623 0.3961545 0.6695931 0.5031172 0.8714234
TIMM50 114.4074668 1.4182808 0.5124772 2.7675005 0.0056488 0.0973092
TIMM50bis 45.9979703 0.7254555 0.6476702 1.1201001 0.2626711 0.6967774
TIMP3 88.8745162 2.4247719 0.5848876 4.1457060 0.0000339 0.0020051
TLR2 11.8373424 -4.5056268 2.3288699 -1.9346837 0.0530291 0.3639027
TLR3 15.1936079 -3.4017931 1.8598188 -1.8290992 0.0673847 0.4226050
TLR6 3.4178723 0.0000000 3.8452195 0.0000000 1.0000000 1.0000000
TLR7 3.5349143 5.8863274 4.0754444 1.4443400 0.1486434 0.5442474
TLR8 0.3890392 0.0000000 4.8566061 0.0000000 1.0000000 NA
TMBIM1 326.5055208 0.0395237 0.2343288 0.1686676 0.8660581 0.9861531
TMEM102 60.3132894 0.1458458 0.5623891 0.2593325 0.7953787 0.9751363
TMEM120A 12.0941672 -1.0402914 2.0539476 -0.5064839 0.6125170 0.9357097
TMEM14A 106.3465415 -1.4101602 0.6113546 -2.3066157 0.0210763 0.2217691
TMEM161A 32.9944969 0.5015460 0.9905048 0.5063539 0.6126082 0.9357097
TMEM173 84.1070910 -0.7242289 0.5275229 -1.3728861 0.1697878 0.5750708
TNF 147.1388763 0.8504604 0.5463964 1.5564896 0.1195917 0.5225185
TNFAIP3 171.5841416 -0.0024566 0.2938087 -0.0083613 0.9933287 1.0000000
TNFAIP3bis 57.1060761 0.1167525 0.6238130 0.1871595 0.8515356 0.9839667
TNFAIP6 21.5278036 -0.5155333 1.6312363 -0.3160384 0.7519734 0.9644026
TNFAIP8L2 127.0246135 0.6649052 0.3423456 1.9422044 0.0521124 0.3628707
TNFRSF10A 11.1047060 0.9557013 1.9343837 0.4940598 0.6212639 0.9368423
TNFRSF10B 2.7787661 5.6961484 3.8004962 1.4987907 0.1339279 0.5415128
TNFRSF10C 206.6974376 -0.1024725 0.2946131 -0.3478208 0.7279748 0.9644026
TNFRSF1A 39.5728420 -0.5799979 1.0221571 -0.5674254 0.5704252 0.9037066
TNFRSF1B 28.8120947 0.3024008 1.1164784 0.2708524 0.7865046 0.9751363
TNFSF11 263.1255979 1.0435485 0.6695830 1.5585049 0.1191136 0.5225185
TNFSF11bis 275.5316136 0.7242332 0.4119188 1.7581940 0.0787145 0.4384861
TNFSF12 71.9666928 -0.0918744 0.4924777 -0.1865555 0.8520092 0.9839667
TNFSF4 78.3368406 -0.2382133 0.5791721 -0.4112996 0.6808529 0.9633554
TNIP1bis 31.1215559 1.1603007 0.7722653 1.5024639 0.1329773 0.5415128
TOB1 15.9138403 1.3159312 1.9511568 0.6744364 0.5000339 0.8704634
TOX2 121.3716776 0.0226433 0.4421036 0.0512172 0.9591525 1.0000000
TP53 352.3689340 -0.0607938 0.2565866 -0.2369330 0.8127087 0.9779354
TP63 34.1557502 -1.2979455 1.0517237 -1.2341127 0.2171609 0.6393638
TPD52L1 361.9086747 0.7165927 0.2417123 2.9646512 0.0030303 0.0665843
TPPP 211.7160578 0.4695744 0.3171024 1.4808286 0.1386523 0.5424108
TPPPbis 310.6199518 0.3076233 0.2533896 1.2140327 0.2247352 0.6508382
TPT1 36.9577578 -0.9056999 1.1981179 -0.7559356 0.4496878 0.8466290
TPX2 22.0756350 1.9785564 0.9473145 2.0885950 0.0367442 0.2948877
TRAF2 49.8135143 -0.3216692 0.7007532 -0.4590334 0.6462102 0.9532477
TRAP1 15.8929055 1.3876606 0.9658876 1.4366687 0.1508121 0.5442474
TREM1 37.4943609 0.6014458 1.1569563 0.5198518 0.6031669 0.9290356
TREX1 107.4851401 -0.4848305 0.4124442 -1.1755058 0.2397924 0.6738380
TRIAP1 112.4647670 0.9792412 0.4410081 2.2204608 0.0263875 0.2532997
TRIB3 172.4263090 0.3603253 0.2931581 1.2291157 0.2190284 0.6401850
TRIM32 38.9465728 0.3306789 0.7714473 0.4286475 0.6681798 0.9627484
TRIM39 33.2528688 0.6333174 0.8667144 0.7307106 0.4649560 0.8571639
TRIM39bis 37.7886785 -0.6863649 0.7887015 -0.8702467 0.3841656 0.8048780
TRIM55 94.1114486 0.1493958 0.4738540 0.3152780 0.7525506 0.9644026
TRPS1 5.6006053 -2.0673831 2.8556044 -0.7239739 0.4690817 0.8571639
TRPV3 8.9498755 3.3389088 2.3719800 1.4076463 0.1592358 0.5543983
TSEN2 60.4186379 -0.3796285 0.7851983 -0.4834811 0.6287542 0.9436295
TTK 3.5935454 0.0000000 4.8032196 0.0000000 1.0000000 1.0000000
TUBA1A 0.1941014 0.0000000 4.8566061 0.0000000 1.0000000 NA
TXNDC12 91.8867324 -0.3940306 0.4780015 -0.8243293 0.4097525 0.8189469
TXNDC12bis 107.8284618 -1.7291454 0.6248397 -2.7673423 0.0056515 0.0973092
TXNDC15 56.0867254 3.4714477 0.9130641 3.8019758 0.0001435 0.0060496
TYROBP 95.8099571 -1.2342643 0.5268566 -2.3426950 0.0191450 0.2182650
UBE2K 62.2069779 -0.3066087 0.5955347 -0.5148460 0.6066606 0.9311307
UBE2S 66.3437813 -0.1223891 0.4936840 -0.2479097 0.8042043 0.9751363
UBE2Sbis 126.1249635 -0.0039871 0.3542786 -0.0112541 0.9910207 1.0000000
UGT1A1 1.0582093 0.0000000 4.8566061 0.0000000 1.0000000 1.0000000
USP18 54.4936153 0.2063492 0.7421452 0.2780442 0.7809784 0.9751363
USP28 3.7545447 4.7766346 3.7514068 1.2732916 0.2029146 0.6334051
USP47 68.9934869 -0.5361099 0.6718052 -0.7980140 0.4248624 0.8261697
VAV1 8.2115462 -1.1896830 2.0589406 -0.5778132 0.5633902 0.9011983
VCAM1 19.3471103 2.4816012 1.4889946 1.6666287 0.0955883 0.4714691
VDAC2 112.1341270 -0.3886469 0.5302866 -0.7328997 0.4636196 0.8571639
VPS35 18.6620373 1.0066482 1.0616022 0.9482348 0.3430099 0.7673978
VSIG4 0.8563493 0.0000000 4.8566061 0.0000000 1.0000000 NA
WDR83 211.7435016 -0.7654937 0.2868365 -2.6687455 0.0076135 0.1130987
WHSC2 73.0525385 0.2705777 0.5039051 0.5369616 0.5912941 0.9179599
WNT4 19.5446306 -0.8754349 1.0004786 -0.8750161 0.3815651 0.8012022
WNT5A 49.1927043 -0.5607440 0.7168897 -0.7821901 0.4341029 0.8338649
WWOX 104.6810017 -0.1161419 0.5689518 -0.2041332 0.8382494 0.9839667
XCL1 85.7978422 1.3026731 0.6937933 1.8776097 0.0604346 0.3919968
XPA 270.4913005 0.6948432 0.3058901 2.2715455 0.0231140 0.2405377
XRN2 34.1799873 -0.3074266 0.8031726 -0.3827652 0.7018938 0.9636086
YWHAB 1018.3884450 -0.0820095 0.2165405 -0.3787260 0.7048913 0.9636086
YWHABbis 299.7032891 0.1930601 0.2856847 0.6757803 0.4991801 0.8704634
YWHAG 177.2409245 -0.4577907 0.3341162 -1.3701542 0.1706388 0.5750708
YWHAQ 535.7223620 -0.2213609 0.2250760 -0.9834939 0.3253644 0.7474380
ZBP1 55.9033420 -0.8485708 0.5364773 -1.5817461 0.1137076 0.5182650
ZC3H12A 237.1077969 0.0966835 0.3613024 0.2675972 0.7890094 0.9751363
ZC3HC1 206.3299536 0.0914980 0.2719058 0.3365062 0.7364892 0.9644026
ZC3HC1bis 198.9937493 0.2639763 0.3695084 0.7143987 0.4749807 0.8571639
ZFYVE26 50.0252192 -0.3743007 0.7460630 -0.5017012 0.6158777 0.9368423
ZMYND11 24.9104728 0.4172620 1.0587312 0.3941151 0.6934961 0.9636086
ZNF281 17.0017165 -1.3399805 1.1951711 -1.1211621 0.2622189 0.6967774
ZSWIM2 9.0855900 -1.5954539 2.3442525 -0.6805811 0.4961366 0.8704634
ZYX 132.8259374 0.0898224 0.3760746 0.2388420 0.8112281 0.9773957

Notice that the comparison is performed based on the MLE estimates \(\hat{\beta}^{\text{MLE}}_i\). To perform the comparison based on the MAP estimates \(\hat{\beta}^{\text{MAP}}_i\), one must further call the lfcShrink function:

resMAP <- lfcShrink(dds, contrast=contrast, type="normal")
log2 fold change (MAP): condition NoDrug vs Ctrl
baseMean log2FoldChange lfcSE stat pvalue padj
ABHD12 310.9719028 0.0938280 0.2284685 0.4125846 0.6799110 0.9633554
ABI2 1.5453307 0.0000000 0.2200468 0.0000000 1.0000000 1.0000000
ABLIM1 174.4158662 -0.0267959 0.3109373 -0.0822915 0.9344149 1.0000000
ABO 1.4663182 -0.0619855 0.2209780 -0.0782167 0.9376557 1.0000000
ACAA2 46.0824707 -0.1263136 0.5142458 -0.0506796 0.9595808 1.0000000
ACP5 46.3211428 -0.0860733 0.4731584 -0.3195450 0.7493133 0.9644026
ACVR1 74.6916526 -1.2490265 0.4460180 -2.8591072 0.0042484 0.0801480
ADA 490.8209066 -0.0024453 0.2552970 -0.0079526 0.9936548 1.0000000
ADAM17 80.5679219 -0.4514758 0.4714550 -1.3053164 0.1917851 0.6182408
ADAMTS12 54.4463986 0.3164790 0.4709431 0.5633335 0.5732078 0.9061304
ADCYAP1 33.7390346 -0.6328013 0.5181200 -1.6949694 0.0900812 0.4603472
ADIPOQ 77.8239090 -0.4519212 0.4943297 -1.1419920 0.2534574 0.6853581
ADORA1 71.1025285 -0.3811835 0.5131052 -1.3166766 0.1879471 0.6135111
ADORA2A 15.9369812 0.1652945 0.4839185 0.3043875 0.7608327 0.9671256
ADORA2B 56.9670207 0.4050707 0.5036753 0.8066997 0.4198395 0.8197691
ADRB3 1.9743938 0.1760629 0.2767908 1.0439476 0.2965096 0.7280014
ADRBK1 3.9748573 -0.1744141 0.2972318 -0.6887310 0.4909926 0.8666517
AEN 73.3790876 -0.1300593 0.4053901 -0.3180638 0.7504366 0.9644026
AENbis 83.8109172 -0.1662618 0.3974145 -0.3751078 0.7075803 0.9636086
AES 0.0557007 0.0000000 0.2133403 0.0000000 1.0000000 NA
AGER 31.2161675 0.5027825 0.5186474 1.7822606 0.0747068 0.4299714
AGT 26.2541762 0.3555346 0.5123720 1.6842521 0.0921330 0.4665771
AGTR1 28.4888007 -0.4467526 0.5186627 -1.1617304 0.2453450 0.6807668
AGTR1bis 37.9888588 0.4780640 0.5125891 1.0870492 0.2770151 0.7125520
AHCY 132.5387580 -0.5221342 0.3352671 -1.5642581 0.1177570 0.5225185
AHNAK 699.9748623 0.0076763 0.1479232 0.0534971 0.9573358 1.0000000
AHNAKbis 405.2041864 0.1217057 0.2034359 0.5999745 0.5485232 0.9001688
AHNAK2 93.2009034 -0.1741601 0.4205839 -0.4692291 0.6389059 0.9498334
AHSG 47.2848703 0.2564758 0.5060209 0.3855470 0.6998322 0.9636086
AIF1 113.8829316 0.5825575 0.3909850 1.4949638 0.1349239 0.5415128
AIFM1 17.7343569 0.0845454 0.4979496 0.4699597 0.6383838 0.9498334
AIFM1bis 17.2154638 -0.0593080 0.4658928 0.2951667 0.7678665 0.9721518
AKT1 86.3225242 -0.7077255 0.4219649 -1.7749349 0.0759086 0.4330451
ALOX15 14.8489248 -0.6865276 0.5171591 -1.6353326 0.1019793 0.4859820
ALOX5 28.6382975 -0.5568721 0.5203346 -1.4614683 0.1438870 0.5429116
ALOX5AP 45.2483630 -0.2821956 0.5205155 -0.8080092 0.4190853 0.8197691
AMPH 140.3655278 0.0618368 0.3416428 0.1870548 0.8516177 0.9839667
ANKRD2 119.4247595 0.4366021 0.3418369 1.2431536 0.2138112 0.6393638
ANO6 5.1865240 0.0740068 0.3453872 0.1983378 0.8427808 0.9839667
ANXA1 187.2626815 -0.1174888 0.3387259 -0.3729885 0.7091570 0.9636086
ANXA1bis 251.5187815 -0.3531698 0.2432296 -1.4614304 0.1438974 0.5429116
ANXA5 265.4528888 0.2695471 0.2735312 0.9724735 0.3308151 0.7526465
ANXA5bis 14.1177118 0.5314573 0.5204995 0.9346453 0.3499711 0.7761655
AOAH 27.3552166 -0.5569360 0.5200718 -1.4618730 0.1437760 0.5429116
APBB1 37.3418291 -0.5812558 0.4933697 -1.2695769 0.2042354 0.6334051
APCS 27.4414622 0.2891101 0.5074491 0.9947204 0.3198723 0.7474380
APOA1 43.9786548 -0.1708070 0.4960851 -0.5795385 0.5622258 0.9011983
APOA2 32.4161352 -0.1015906 0.5012038 0.0203805 0.9837398 1.0000000
APOD 348.2616908 -0.3793145 0.2456067 -1.5494892 0.1212642 0.5225185
APOPT1 79.8866136 -0.0836109 0.4246169 -0.2485221 0.8037304 0.9751363
APP 32.9247970 0.3300586 0.5182565 0.8423396 0.3995979 0.8189469
APPL1 53.7366502 -0.4539441 0.4980055 -0.9242521 0.3553551 0.7771323
APPL2 162.3873602 0.1444044 0.3359588 0.4070945 0.6839386 0.9636086
ARHGAP26 27.9089834 -0.1899254 0.5006869 -0.3400295 0.7338343 0.9644026
ARHGEF2 31.7571263 -0.1641013 0.4936532 -0.3201505 0.7488543 0.9644026
ARL6IP5 97.8575226 0.3548104 0.4444892 0.7364575 0.4614523 0.8571639
ARRB1 163.6295125 -0.0447679 0.2819016 -0.1454468 0.8843581 0.9880121
ARRB2 104.0870034 0.0121969 0.3388048 0.0106119 0.9915331 1.0000000
ASAH2 20.2410362 -0.4704980 0.4892906 -1.5513171 0.1208257 0.5225185
ASB2 39.1525422 -0.2109781 0.5191218 -0.4212156 0.6735976 0.9633554
ASB4 8.5243797 -0.2379545 0.3583742 -1.4814042 0.1384989 0.5424108
ASS1 187.2297776 -0.4402240 0.3030580 -1.4728228 0.1407988 0.5426034
ASS1bis 165.4515253 -0.5361656 0.2788262 -1.9133470 0.0557036 0.3696922
ATF2 169.8378643 1.6832377 0.3802597 4.5736545 0.0000048 0.0006034
ATF2bis 96.1886316 0.0802120 0.4142387 0.1650468 0.8689072 0.9866368
ATF4 58.9712823 0.6556113 0.5148505 1.4518787 0.1465353 0.5442474
ATG2B 13.7238503 -0.4082606 0.4634007 -2.3756389 0.0175186 0.2100014
ATM 24.2111835 0.3634405 0.5184835 1.9124017 0.0558247 0.3696922
ATP6V1G1 2.7089157 -0.1112341 0.2691786 -0.9974775 0.3185328 0.7474380
ATPIF1 62.9647397 -0.1008274 0.5130491 -0.4589982 0.6462355 0.9532477
AVP 38.3494878 -0.1902904 0.4965632 -0.3872709 0.6985557 0.9636086
AZU1 133.7629280 0.1247528 0.3624296 0.3563075 0.7216103 0.9644026
B4GALT1 69.9696991 0.2749707 0.4633416 0.7281635 0.4665135 0.8571639
BAD 835.6076430 0.0061965 0.1795148 0.0299256 0.9761264 1.0000000
BAG5 66.2036602 -0.2324976 0.4560422 -0.3979363 0.6906771 0.9636086
BAK1 154.3227100 -0.7694658 0.3307422 -2.3559525 0.0184753 0.2160011
BANP 266.1907259 -1.0695693 0.2720252 -3.9224039 0.0000877 0.0042067
BAP1 46.3077313 -0.2023668 0.4801553 -0.3895373 0.6968788 0.9636086
BCAP31 78.5306397 0.3328204 0.4280051 0.7497111 0.4534287 0.8511742
BCL10 191.0730675 0.2357728 0.3162045 0.7222758 0.4701249 0.8571639
BCL2L1 115.2901085 0.7046653 0.3317907 2.1244217 0.0336349 0.2843950
BCL2L10 72.6515552 -0.6477345 0.4597130 -1.4832835 0.1379991 0.5424108
BCL2L11 286.7354783 0.5896912 0.2563258 2.3118227 0.0207875 0.2211879
BCL2L12 90.1931690 0.5186221 0.3726593 1.3735934 0.1695680 0.5750708
BCL2L12bis 72.5327073 -0.0330406 0.4264894 -0.1009528 0.9195879 0.9963956
BCL2L14 258.8622201 -0.4065952 0.2750810 -1.4765537 0.1397953 0.5425663
BCL2L2 378.5707399 -0.2817654 0.2129762 -1.3223306 0.1860581 0.6101283
BCL6 9.3683868 0.4402187 0.4075020 1.3458807 0.1783410 0.5946793
BCL6B 41.0712108 0.1474031 0.4850594 0.3929564 0.6943517 0.9636086
BCL7C 330.6323489 -0.2881130 0.2176172 -1.3299230 0.1835437 0.6082264
BCL7Cbis 359.3221847 -0.2657801 0.2244557 -1.2047198 0.2283115 0.6551848
BDNF 61.3354801 0.4526715 0.4815427 0.9329305 0.3508558 0.7763095
BIK 76.3224300 1.2768375 0.4434285 2.9611056 0.0030654 0.0665843
BIRC2 44.4001961 1.0297195 0.5110772 2.1505951 0.0315082 0.2747532
BLVRA 93.3903721 -0.4394799 0.4483136 -0.9947068 0.3198789 0.7474380
BLVRAbis 331.3549031 -0.2461918 0.2609435 -0.9667591 0.3336645 0.7539291
BMF 93.0432784 0.6307075 0.4538759 1.5203069 0.1284339 0.5311217
BMP4 40.8378770 -0.0887772 0.5178213 -0.3794953 0.7043201 0.9636086
BMP5 25.0379012 0.7921215 0.5114869 3.1677190 0.0015364 0.0393235
BMPR1B 8.9645943 -0.3323320 0.4679065 -0.5994404 0.5488793 0.9001688
BNIP3 382.1506501 -0.0020615 0.2800276 0.0247886 0.9802236 1.0000000
BNIP3L 619.0863338 0.1358011 0.1927038 0.7024786 0.4823808 0.8593713
BRAF 32.2831863 -0.0920051 0.5183784 -0.1056116 0.9158905 0.9963956
BRCA1 19.1340748 0.3101017 0.4553762 0.6338237 0.5261959 0.8874776
BRD4 24.4403342 -0.1252704 0.5166032 -0.3396233 0.7341402 0.9644026
BST1 80.6948934 0.0625040 0.4458720 0.1817176 0.8558043 0.9850616
BTK 41.3053100 -0.2259387 0.4984184 -0.3428952 0.7316773 0.9644026
BTN2A1 0.6646010 0.0000000 0.2146094 0.0000000 1.0000000 NA
BZW2 0.7387484 -0.1180315 0.2164346 -0.5835993 0.5594899 NA
C11orf82 1.7439741 -0.0572146 0.2236330 -0.2514140 0.8014941 0.9751363
C14orf129 582.3910155 0.2752580 0.1758816 1.5649933 0.1175845 0.5225185
C16orf5 62.5893653 0.5474278 0.4651489 1.2467308 0.2124962 0.6393638
C1QA 119.6000737 -0.2462865 0.3593336 -0.5971049 0.5504373 0.9001688
C1QTNF3 98.9811631 0.9185506 0.3776626 2.4823480 0.0130520 0.1711118
C1QTNF3bis 71.8185572 -0.9142903 0.5085404 -1.9485542 0.0513487 0.3628707
C22orf29 568.1818474 0.3038008 0.2202583 1.3808655 0.1673203 0.5720301
C2orf18 229.2981505 -0.5856686 0.3079456 -1.9130410 0.0557428 0.3696922
C5AR1 65.0319384 -0.2267233 0.5043805 -0.2909027 0.7711257 0.9723783
C6orf162 18.5298455 0.5658149 0.4754116 1.7187927 0.0856521 0.4477945
CACNG2 120.8933638 0.1144877 0.3388803 0.3295054 0.7417737 0.9644026
CALD1 85.4563162 0.1310939 0.3824796 0.3609403 0.7181441 0.9644026
CAPN1 10.8256926 0.1732530 0.4727877 -0.3193333 0.7494738 0.9644026
CAPN2 15.5342678 -0.6422034 0.5202217 -1.1590129 0.2464509 0.6807668
CASP1 234.8902216 -1.0601863 0.2643661 -4.0288495 0.0000561 0.0029489
CASP2 122.6797858 0.1919362 0.3685647 0.5472449 0.5842105 0.9140962
CASP3 54.9567235 -0.0916535 0.4472040 -0.1925550 0.8473075 0.9839667
CASP5 30.6727698 -0.7448403 0.5129792 -1.5213037 0.1281836 0.5311217
CASP5bis 61.1465698 0.4843040 0.4522205 1.0762919 0.2817967 0.7154464
CASP8 50.0774254 0.4489236 0.5172432 0.9275389 0.3536468 0.7771323
CAV1 68.1551797 -0.2584956 0.5114454 -0.4952090 0.6204526 0.9368423
CC2D1B 1.5094372 0.0000000 0.2204743 0.0000000 1.0000000 1.0000000
CCDC6 283.9588531 -0.2015078 0.2214551 -0.9032665 0.3663845 0.7939728
CCK 67.9666125 0.1894947 0.4510285 0.3791900 0.7045468 0.9636086
CCL3 211.3157156 -0.0796424 0.2960707 -0.2594434 0.7952931 0.9751363
CCL5 190.1217177 1.1732341 0.3232916 3.6452763 0.0002671 0.0101179
CCNCbis 14.8022899 0.4675242 0.4581141 1.0597626 0.2892526 0.7242846
CCR2 26.1143538 -0.2637993 0.5166456 -0.2859407 0.7749236 0.9751363
CCR6 26.2933325 -0.2734044 0.4890306 -0.3654305 0.7147901 0.9644026
CD19 15.5998668 0.1848428 0.5110828 -0.1901467 0.8491942 0.9839667
CD200 15.4783912 0.1832606 0.4585981 1.7505601 0.0800217 0.4411191
CD200R1 33.7170690 -0.6161537 0.5181680 -1.6932039 0.0904167 0.4603472
CD27 60.7145593 0.4326103 0.4468237 0.9806660 0.3267575 0.7474380
CD28 44.2577494 0.3408336 0.5149753 0.5977795 0.5499870 0.9001688
CD36 5.9567602 -0.1706357 0.3209774 -1.5438673 0.1226205 0.5225185
CD44 17.3470211 -0.2444332 0.4640383 -0.6656333 0.5056455 0.8714234
CD6 1.6282572 0.0000000 0.2209740 0.0000000 1.0000000 1.0000000
CD68 41.4901920 -0.6784422 0.5148239 -1.7422692 0.0814613 0.4433557
CD70 297.5898333 -0.0713014 0.2628670 -0.2585324 0.7959960 0.9751363
CD74 408.6389437 -0.5249611 0.2706902 -1.9773450 0.0480026 0.3491579
CD96 24.3150614 0.0281650 0.4924614 1.1289151 0.2589337 0.6963264
CDC25C 144.3067873 -0.4671250 0.4283186 -1.1730492 0.2407761 0.6746004
CDC42EP2 321.2922324 1.7455770 0.2795321 6.2564448 0.0000000 0.0000001
CDCA5 209.4059088 0.2909885 0.2920666 1.0453741 0.2958501 0.7280014
CDCA7 0.6150384 0.3299390 0.2143626 0.7010520 0.4832706 NA
CDH5 16.6569685 -0.3306962 0.4867976 -1.0984595 0.2720039 0.7105396
CDK1 47.7954857 -0.6705233 0.5160542 -1.4362046 0.1509441 0.5442474
CDK2 900.8878836 -0.1774244 0.1908997 -0.9262385 0.3543220 0.7771323
CDK7 6.5305664 0.1997351 0.3159484 0.6091269 0.5424403 0.9001688
CDKN1A 186.1894418 0.1874794 0.2991164 0.5836675 0.5594440 0.9011983
CDKN1B 166.2276638 0.4727757 0.2850669 1.6674320 0.0954285 0.4714691
CDKN2D 197.0631615 0.3012079 0.2750399 1.1011400 0.2708357 0.7104749
CDX2 287.5360636 0.1515993 0.2731366 0.5750298 0.5652712 0.9011983
CEP170P1 86.0105164 0.1422412 0.4249325 0.3728984 0.7092241 0.9636086
CEP55 57.4736278 -0.1679643 0.4603193 -0.3563201 0.7216009 0.9644026
CFLAR 175.5519450 -0.6554654 0.3223497 -2.0304133 0.0423145 0.3200484
CHCHD10 29.6076287 0.4151742 0.3932721 1.0216994 0.3069232 0.7355359
CHIA 58.1881862 -1.0643829 0.4477909 -2.3602871 0.0182608 0.2160011
CHID1 30.5623148 0.6060986 0.5125070 1.3925556 0.1637542 0.5632542
CHRFAM7A 1.5827202 0.0681580 0.3183518 0.4275999 0.6689424 0.9627484
CHRNA7 68.4767088 0.4594853 0.4563862 1.0205364 0.3074741 0.7355359
CIDEB 132.6241100 0.5722875 0.3200889 1.7909109 0.0733076 0.4299714
CLEC7A 372.1849905 0.0177671 0.2923441 0.0283077 0.9774168 1.0000000
CLOCK 0.7585792 0.1091493 0.2162293 0.5157045 0.6060608 NA
CLU 47.4932577 1.0762126 0.5096342 2.4246149 0.0153246 0.1909530
CMA1 94.7117100 0.1004564 0.4467476 0.4330468 0.6649808 0.9627484
CMA1bis 101.4659147 -0.5756144 0.4189049 -1.4274621 0.1534467 0.5442474
CNOT4 29.3091715 0.9149848 0.5181187 2.2609675 0.0237613 0.2433565
CNTF 113.1407575 0.5797120 0.5191374 1.4556267 0.1454958 0.5442474
COL2A1 265.3869464 0.2595884 0.2448221 1.0524778 0.2925804 0.7253237
COPS6 201.5530807 0.3569432 0.3123142 1.1445193 0.2524083 0.6853581
CREB1 345.5545062 -0.2739998 0.2906295 -0.9471020 0.3435868 0.7673978
CREB3 112.9201025 1.1093883 0.4153111 2.7698337 0.0056085 0.0973092
CREB3bis 65.3760628 -0.0747007 0.5020442 -0.2406275 0.8098438 0.9773957
CREB3L1 77.3707620 0.6793970 0.4401450 1.6464732 0.0996664 0.4840208
CRH 54.7066227 0.1522900 0.5060436 0.2728138 0.7849963 0.9751363
CRIP1 187.6792160 0.9464341 0.2962163 3.1988626 0.0013797 0.0384290
CRP 16.0121046 -0.0356392 0.5189703 -0.0608401 0.9514866 1.0000000
CRPbis 65.1093445 0.1303292 0.4527044 0.3172783 0.7510325 0.9644026
CRY1 49.5832751 -0.3965235 0.4639105 -0.8241301 0.4098657 0.8189469
CRYAB 248.2812652 -0.3959305 0.2825462 -1.4111792 0.1581918 0.5543983
CSF2 77.2092542 -0.3118524 0.4864473 -0.6061622 0.5444071 0.9001688
CSNK2A1 76.4105442 -0.5642751 0.3777201 -1.5421263 0.1230429 0.5225185
CSNK2A2 456.3761947 -0.1613321 0.2349503 -0.7087762 0.4784633 0.8571639
CTH 91.5795769 -0.7293918 0.3815963 -1.9232088 0.0544538 0.3683413
CTNNA1 60.1083741 -0.4864286 0.4950858 -1.3149596 0.1885235 0.6135111
CTNNBIP1 780.4244066 0.6443899 0.2004357 3.2216244 0.0012747 0.0365789
CTTN 52.7531426 0.5741345 0.4559901 1.2597798 0.2077488 0.6334051
CUEDC2 533.6336665 0.1950249 0.1918516 1.0137953 0.3106804 0.7355359
CUL1 39.0154141 0.1533464 0.5176295 0.4081842 0.6831385 0.9636086
CUL2 5.0493572 0.2871647 0.3887522 1.9367220 0.0527793 0.3639027
CUL3 3.0934301 0.0000000 0.2384112 0.0000000 1.0000000 1.0000000
CUL5 0.9661242 0.0000000 0.2176681 0.0000000 1.0000000 NA
CX3CL1 35.7565675 -0.4715791 0.5149888 -1.0550631 0.2913964 0.7242846
CXCL13 148.2514765 0.4716060 0.3640494 1.2660603 0.2054915 0.6334051
CXCR7 26.4476109 0.1976798 0.5203552 0.3150898 0.7526935 0.9644026
CYP19A1 137.2172684 -0.3803616 0.3561734 -1.0976416 0.2723610 0.7105396
CYP19A1bis 85.7985855 0.1141866 0.4013313 0.2802583 0.7792793 0.9751363
CYP19A1bis2 22.2364691 0.0969161 0.5185942 0.0563555 0.9550586 1.0000000
CYSTM1 0.8099162 0.0000000 0.2164912 0.0000000 1.0000000 NA
DAB2IP 2.9000384 -0.0522063 0.3769230 -0.1054010 0.9160576 0.9963956
DAGLB 23.4822780 -0.4794467 0.5174062 -0.8226969 0.4106804 0.8189469
DAXX 82.6282062 -0.0842282 0.4489366 -0.1892029 0.8499338 0.9839667
DAZ3 29.5882295 -0.2047411 0.5174573 -0.4968028 0.6193281 0.9368423
DAZ4 7.2737810 0.5167873 0.3460241 2.3230160 0.0201783 0.2182650
DBH 37.6187078 0.1062494 0.5010307 0.1447968 0.8848713 0.9880121
DCUN1D4 2.0527464 0.0000000 0.2218351 0.0000000 1.0000000 1.0000000
DDHD1 6.6259822 0.5049626 0.4162631 0.4965430 0.6195113 0.9368423
DDI1 8.3265855 -0.1624848 0.2276612 -0.2153826 0.8294690 0.9829258
DDIT3 114.8949439 1.6140957 0.3910370 4.3071932 0.0000165 0.0013048
DDIT4 269.2259522 0.9360055 0.2384018 3.9192048 0.0000888 0.0042067
DDX21 1.1483962 -0.3490370 0.2187785 -0.8169628 0.4139497 0.8195195
DDX47 64.6884948 0.0348960 0.4613915 0.1886977 0.8503298 0.9839667
DEDD 5.6914983 0.0237213 0.3333537 0.8941491 0.3712421 0.7978119
DEDD2 35.2340203 0.0061441 0.4841164 0.0604403 0.9518049 1.0000000
DFNA5 107.1883097 -0.0581914 0.3944985 -0.1726759 0.8629062 0.9861531
DFNA5bis 75.6219437 -0.2421229 0.4453908 -0.5461769 0.5849443 0.9140962
DIABLO 60.8216294 -1.1956603 0.4978971 -2.7463696 0.0060259 0.0983881
DMC1 6.1564469 0.1083689 0.3428326 0.3198589 0.7490753 0.9644026
DNAJA1 98.9613915 -0.5410642 0.3721904 -1.4327632 0.1519255 0.5442474
DNAJC10 8.3648970 -0.1802568 0.4247900 -1.0487369 0.2942992 0.7276798
DNAJC30 62.5280930 0.1428898 0.4583502 0.3439826 0.7308594 0.9644026
DNM1L 25.2671383 -0.2129054 0.5170415 -0.2919783 0.7703032 0.9723783
DUOXA1 8.0041645 0.0141917 0.3801238 0.2722467 0.7854323 0.9751363
DUOXA2 61.9587453 -0.3090162 0.4302776 -0.7765941 0.4373983 0.8351132
DUSP10 132.8387634 0.6879501 0.3480636 1.9747233 0.0482996 0.3491579
DUSP10bis 191.2659037 0.8624853 0.3012636 2.8479162 0.0044007 0.0801480
DUSP16 109.8477439 -0.3513874 0.4179317 -0.8881306 0.3744705 0.7987017
DUSP23 2.8967229 0.0214605 0.2491601 -0.9641506 0.3349704 0.7539291
DUSP3 1067.5533968 -0.0746284 0.1716813 -0.4345572 0.6638839 0.9627484
DUSP4 252.5145819 1.6988848 0.3170908 5.3723170 0.0000001 0.0000184
DUSP6 136.5617933 -0.3957274 0.3412462 -1.0557927 0.2910629 0.7242846
DUSP7 61.0311075 0.8928085 0.4731658 2.0957120 0.0361077 0.2941416
DYNC1I2 94.7709314 -0.1945773 0.4230570 -0.4480839 0.6540926 0.9586285
DYNC1LI1 100.2737109 0.0747848 0.3949883 0.1835387 0.8543753 0.9850616
DYRK2 4.0044856 0.0993987 0.2882980 0.3370252 0.7360979 0.9644026
E2F2 308.4662227 0.2776955 0.2294838 1.1911482 0.2335954 0.6683228
EDA2R 31.3012494 0.0283927 0.5092373 0.3270060 0.7436634 0.9644026
EDNRB 12.2023639 -0.1339883 0.4300971 -0.5773739 0.5636869 0.9011983
EIF2AK2 32.0537018 -0.7199161 0.5202284 -1.5789061 0.1143576 0.5182650
EIF4ENIF1 0.6432526 -0.3169607 0.2151041 -0.6955987 0.4866802 NA
ELANE 71.4165979 0.7238542 0.4235324 1.8172448 0.0691796 0.4275297
ELK1 7.0459835 -0.2182944 0.4009930 -0.4190541 0.6751766 0.9633554
ELK3 147.1454439 0.3453485 0.2973317 1.1773716 0.2390472 0.6738380
ELK4 44.6758264 -0.1557292 0.4741866 -0.3214657 0.7478575 0.9644026
ELL 0.6597596 0.0000000 0.2141747 0.0000000 1.0000000 NA
ELL3 61.1130167 -0.1053457 0.4414728 -0.2579887 0.7964156 0.9751363
EPB49 299.2365644 0.2825456 0.2469499 1.1531769 0.2488378 0.6807668
EPHB6 26.3193635 0.0050437 0.5115801 0.0786110 0.9373421 1.0000000
EPO 40.9331404 -0.3667858 0.5197914 -0.8528151 0.3937618 0.8177466
EPS8 31.9542904 0.1388371 0.5190745 0.5412936 0.5883053 0.9170690
ERBB3 37.8995990 -1.1739551 0.5203541 -2.9436127 0.0032441 0.0682694
ERG 119.3990955 0.0176427 0.3306531 0.0623757 0.9502636 1.0000000
ERO1L 3.9566776 -0.0904247 0.3797032 -0.1118238 0.9109631 0.9963956
ERO1Lbis 13.3788545 0.0733000 0.4641554 0.4954295 0.6202970 0.9368423
ERP29 196.9720349 -0.8380129 0.3284449 -2.6060301 0.0091598 0.1301820
ESR1 141.6934015 0.0111727 0.3077921 0.0481549 0.9615928 1.0000000
ESR2 30.7838562 0.5705113 0.5204701 1.7499973 0.0801188 0.4411191
ETS1 656.4127032 1.1086195 0.1557068 7.1226327 0.0000000 0.0000000
ETV6 128.3350761 -0.9284388 0.3446638 -2.7008988 0.0069152 0.1091455
EXOC7 345.3272404 -0.2268345 0.2320822 -0.9712601 0.3314188 0.7526465
EXOSC1 1.0137083 0.0000000 0.2170557 0.0000000 1.0000000 1.0000000
EYA1 13.4467977 0.1619923 0.4449145 0.8248114 0.4094787 0.8189469
EYA1bis 78.3092174 0.2166336 0.4104227 0.5130724 0.6079007 0.9315242
EYA2 36.7256058 -0.1777681 0.5146792 -0.2652807 0.7907932 0.9751363
EYA3 48.7640530 -1.0043681 0.4966621 -2.1491289 0.0316242 0.2747532
F12 26.9225445 -0.3564133 0.5117985 -0.6882976 0.4912654 0.8666517
F2R 3.3320832 0.0000000 0.2592691 0.0000000 1.0000000 1.0000000
F3 37.5935125 0.5074050 0.5176678 1.4978929 0.1341611 0.5415128
F8 169.5302601 -0.8596301 0.2939476 -2.9316737 0.0033714 0.0694070
FABP4 132.2956889 0.0067376 0.3526563 0.0835069 0.9334485 1.0000000
FADD 256.4123602 -0.0984551 0.2335647 -0.4177167 0.6761543 0.9633554
FAIM 315.5848453 0.8447776 0.2422823 3.4764692 0.0005081 0.0171834
FAIM2 268.8924988 -0.1713906 0.2917854 -0.5901457 0.5550930 0.9001688
FAM103A1 147.7536634 -0.7566238 0.3394637 -2.2315147 0.0256471 0.2529975
FAM105B 12.9783767 0.2865888 0.5160306 0.6368596 0.5242163 0.8864872
FAM105Bbis 896.4391553 -0.1537775 0.1722652 -0.8951606 0.3707013 0.7978119
FAM13C 22.9325398 0.2501356 0.4853090 0.2922218 0.7701171 0.9723783
FAM195B 431.6811162 -0.0601058 0.2241396 -0.2775918 0.7813258 0.9751363
FAM19A3 63.4014153 -0.4480843 0.4730338 -0.9886832 0.3228182 0.7474380
FAM207A 275.8319750 0.2287075 0.2768164 0.8421912 0.3996809 0.8189469
FANCD2 243.3309771 0.6615234 0.2979227 2.2106428 0.0270606 0.2532997
FAS 64.1769378 -0.2435993 0.4822321 -0.6634745 0.5070267 0.8714234
FASLG 215.0651873 0.5068393 0.2932195 1.7318581 0.0832988 0.4456377
FASN 18.9104368 0.0181396 0.4669756 0.1482576 0.8821395 0.9880121
FAXC 36.7612246 0.4894884 0.5205126 1.5788107 0.1143795 0.5182650
FBXO4 88.4575866 -0.6031019 0.3571389 -1.6602035 0.0968735 0.4753327
FBXW7 8.4778476 0.0230626 0.4263048 0.3184711 0.7501276 0.9644026
FCER1G 93.2447026 0.5478027 0.3750337 1.4788422 0.1391825 0.5424108
FCGR2B 54.7418218 -0.0683021 0.5193832 -0.5961221 0.5510937 0.9001688
FECH 61.1858818 -0.4398166 0.4687543 -0.7301772 0.4652819 0.8571639
FFAR2 48.1766476 -1.6257829 0.4495084 -3.8259306 0.0001303 0.0058750
FFAR3 100.6610863 0.3164445 0.3397258 0.8917964 0.3725021 0.7978119
FGA 60.1330592 -0.9944268 0.5074517 -2.2558018 0.0240830 0.2433565
FGAbis 17.5806452 -0.7602785 0.4915727 -3.1799999 0.0014728 0.0393235
FGB 37.3016782 -0.3640800 0.5102460 -0.8344424 0.4040318 0.8189469
FGF10 77.2688269 -0.1656485 0.4356587 -0.3839613 0.7010071 0.9636086
FGFR2 10.4628017 0.1101390 0.4375700 0.2830040 0.7771737 0.9751363
FGG 37.0847404 0.4505985 0.5179965 1.1012601 0.2707835 0.7104749
FHIT 736.9172664 0.2873462 0.2038491 1.4082788 0.1590485 0.5543983
FHL3 411.2653632 0.1889217 0.2331347 0.8244503 0.4096838 0.8189469
FHL3bis 262.6345801 0.1568970 0.2239215 0.7052902 0.4806297 0.8587855
FIGNL1 24.3666459 0.2241565 0.5191058 0.1121461 0.9107076 0.9963956
FIGNL1bis 21.7428648 -0.1776419 0.5196172 -0.4305943 0.6667634 0.9627484
FIS1 123.9736152 0.0290649 0.4418756 -0.0711313 0.9432932 1.0000000
FLAD1 13.0310041 -0.4021945 0.4809577 -1.1675002 0.2430084 0.6788466
FNDC4 44.7002366 -0.5841711 0.4959842 -1.5339250 0.1250481 0.5286632
FOS 122.8047922 1.0621895 0.3400026 3.1752462 0.0014971 0.0393235
FOXM1 53.4666663 0.7513601 0.4495783 1.8137669 0.0697136 0.4275297
FOXO3 28.2282016 0.5208493 0.5197756 1.4205591 0.1554450 0.5492776
FOXP1 82.1705387 0.5427829 0.4497346 1.4308982 0.1524594 0.5442474
FOXP3 197.9982435 0.3936412 0.2386850 1.6484700 0.0992563 0.4840208
FPR2 30.6410903 -0.2024666 0.5132216 -0.0553906 0.9558273 1.0000000
FRS2 0.8230325 0.0000000 0.2164125 0.0000000 1.0000000 NA
FUT7 197.1272311 -1.0579381 0.2943719 -3.5795875 0.0003441 0.0125345
FXN 209.6559030 -0.4495840 0.2973352 -1.5044749 0.1324591 0.5415128
FXR2 1.1122827 0.0000000 0.2187418 0.0000000 1.0000000 1.0000000
FYN 191.3283429 -0.2302526 0.2806261 -0.8316996 0.4055785 0.8189469
G0S2 100.0676528 0.3010372 0.4170716 0.7135273 0.4755195 0.8571639
GAB1 2.0546376 -0.0567632 0.2223353 -0.0969089 0.9227987 0.9975918
GAB2 160.6895490 -0.2449425 0.3172129 -0.7853326 0.4322586 0.8330360
GABARAP 579.7498765 -0.0160288 0.1956570 -0.0827048 0.9340863 1.0000000
GABRR1 37.7470645 -0.6984345 0.5125728 -1.7924042 0.0730682 0.4299714
GATA1 157.3778060 0.7972379 0.3093434 2.5883803 0.0096428 0.1323445
GATA2 141.6801353 0.2375923 0.2694868 0.8836252 0.3768986 0.7992984
GATA2bis 75.1468194 -0.5212462 0.3969222 -1.2795496 0.2007036 0.6314495
GATA2bis2 156.4174478 -0.1162052 0.2598982 -0.4405405 0.6595457 0.9611894
GBA 38.8134238 0.3914010 0.5174399 0.7926528 0.4279801 0.8305270
GBP5 69.6186282 0.0303167 0.4365043 0.1070009 0.9147883 0.9963956
GCLC 38.0116698 -0.1744223 0.5195074 -0.0782599 0.9376213 1.0000000
GCLM 291.6400782 -0.1874141 0.2546881 -0.7302158 0.4652583 0.8571639
GDNF 44.8102022 0.1521025 0.5193063 0.6488215 0.5164538 0.8796434
GGCT 438.2170549 -0.0357578 0.2146733 -0.1658501 0.8682749 0.9866368
GGT1 136.7702790 -0.2949899 0.3005167 -0.9920813 0.3211579 0.7474380
GHRL 156.8396590 1.0164656 0.3850194 2.7609800 0.0057628 0.0974534
GHSR 48.1277685 0.3719699 0.4921853 0.8606342 0.3894395 0.8105478
GJA1 80.7653106 -1.2311015 0.4442811 -3.1045364 0.0019058 0.0451192
GLRA3 6.5377381 -0.3526597 0.3312839 -6.8086492 0.0000000 0.0000000
GLUD2 1.5705231 0.0000000 0.2201457 0.0000000 1.0000000 1.0000000
GMFB 137.8183696 0.2973517 0.3553346 0.8158085 0.4146097 0.8195195
GNAI2 82.6948031 -0.1533004 0.4077260 -0.4313281 0.6662299 0.9627484
GNAI3 32.1102252 -0.2309724 0.5166960 -0.4222306 0.6728567 0.9633554
GNB2L1 281.3065376 -0.2250998 0.2565727 -0.8653074 0.3868701 0.8087549
GORASP1 205.5901994 -0.2695377 0.2677074 -1.0368385 0.2998111 0.7317555
GORASP2 71.4760199 0.4998906 0.4516687 1.1060359 0.2687110 0.7088281
GPR17 1.9820397 -0.1623914 0.2220544 -0.8475738 0.3966754 0.8189469
GPRC5B 60.6724692 -0.8731624 0.4848319 -2.0244784 0.0429210 0.3200484
GPS2 81.1913586 0.8224961 0.4018714 2.1272113 0.0334025 0.2843950
GPSM3 433.5547302 0.1880457 0.2441600 0.7616110 0.4462922 0.8452775
GPX1 69.4634341 0.2290214 0.4810281 0.7125125 0.4761475 0.8571639
GPX4 165.0553666 0.2153204 0.3475878 0.6675719 0.5044069 0.8714234
GRB2 257.9020950 0.1138027 0.2315578 0.4960780 0.6198394 0.9368423
GRINA 226.7284651 -0.6264863 0.2955392 -2.1197893 0.0340238 0.2851377
GRN 35.8896746 0.7612463 0.4920592 1.5541988 0.1201370 0.5225185
GRNbis 50.8450689 0.4022679 0.4717131 0.7646698 0.4444682 0.8452036
H2BFS 23.7330687 0.0307610 0.5027170 0.1090012 0.9132015 0.9963956
H3F3B 530.7844801 -0.1267040 0.2237763 -0.5699766 0.5686936 0.9036121
HCK 118.6186746 -0.3036034 0.3552589 -0.7787851 0.4361063 0.8343287
HDAC1 38.7995574 -0.6306443 0.5205151 -1.6357894 0.1018837 0.4859820
HDAC6 387.1877995 0.7563940 0.2411789 3.1512779 0.0016256 0.0405111
HDAC6bis 4.5244985 -0.0141326 0.3706064 -0.0725335 0.9421774 1.0000000
HECTD3 39.7327770 -0.0231734 0.5194477 -0.3493992 0.7267896 0.9644026
HELLS 58.2065350 0.3915402 0.4673030 0.8173656 0.4137195 0.8195195
HERPUD1 16.7595052 0.4180832 0.5053093 1.4277361 0.1533678 0.5442474
HFE 3.5919966 -0.1131856 0.2845776 -1.0627477 0.2878964 0.7242846
HGF 105.5862631 -0.3161118 0.4339904 -0.8202752 0.4120593 0.8195195
HIF1A 1.4780287 0.0730576 0.2207090 4.2496644 0.0000214 0.0015596
HIGD1A 39.3721669 0.6656631 0.5063400 1.6073972 0.1079673 0.5086816
HINT1 77.2342124 1.0521362 0.5077771 2.3240880 0.0201208 0.2182650
HIP1R 116.0354172 0.0323712 0.3640130 0.0836771 0.9333132 1.0000000
HIST1H3A 234.0769489 -0.0628943 0.2423488 -0.2590030 0.7956329 0.9751363
HIST1H3B 658.5922789 -0.8241678 0.1965923 -4.1972711 0.0000270 0.0017056
HIST1H3C 598.2193725 -0.7760564 0.2577335 -3.0209082 0.0025202 0.0582099
HIST1H3F 375.9623695 -0.0342029 0.2327362 -0.1682349 0.8663985 0.9861531
HIST1H3Fbis 944.6463196 -0.4586026 0.1680813 -2.7310099 0.0063141 0.1013460
HIST1H3Fbis2 395.5794761 -0.4578179 0.2371071 -1.9443678 0.0518511 0.3628707
HIST1H3G 466.6918606 -0.3988014 0.1962347 -2.0289659 0.0424618 0.3200484
HIST1H3H 489.2703800 -0.4487818 0.2030839 -2.2162491 0.0266744 0.2532997
HIST1H3Hbis 725.3774391 -0.6148599 0.1903966 -3.2292260 0.0012413 0.0365789
HIST1H3I 400.8235706 -0.8163462 0.2363751 -3.4590512 0.0005421 0.0177018
HLA-DRB1 115.3365689 0.5868320 0.5193178 1.4313673 0.1523250 0.5442474
HLA-DRB1bis 137.7194753 1.5839466 0.3395429 4.7032044 0.0000026 0.0004818
HLA-DRB1bis2 69.2775162 1.1856320 0.4131291 2.8478949 0.0044009 0.0801480
HLA-E 41.6234272 -0.4006811 0.5197276 -0.9375769 0.3484619 0.7746325
HMG20B 0.0000000 NA NA NA NA NA
HMGB1 209.8763639 -0.1433868 0.2670132 -0.5875616 0.5568266 0.9011983
HMGB2 379.2477056 -0.1013444 0.2925855 -0.3605514 0.7184348 0.9644026
HMMR 11.0776417 -0.0729699 0.3742308 -0.1594356 0.8733257 0.9880121
HMMRbis 10.0472801 0.0232233 0.4010977 1.3568498 0.1748289 0.5871029
HMOX1 421.5574913 -0.0305822 0.2266971 -0.1306879 0.8960222 0.9917563
HNRNPA1 1.4708103 -0.3198136 0.2204112 -0.8811355 0.3782445 0.7992984
HNRNPH1 91.4504396 -0.2973033 0.3943836 -0.7381407 0.4604289 0.8571639
HRAS 56.7248910 -0.5674388 0.5059264 -1.4333293 0.1517637 0.5442474
HSF1 198.1321075 -0.0728373 0.2561015 -0.2817371 0.7781451 0.9751363
HSP90AA1 28.2933080 0.1196374 0.4965469 0.1714560 0.8638652 0.9861531
HSPA1A 41.4738714 -0.6131042 0.4694540 -1.2085625 0.2268310 0.6529146
HSPB8 454.7872102 0.1484031 0.2670179 0.5620149 0.5741059 0.9061304
HSPD1bis 3.2415116 0.0000000 0.2688057 0.0000000 1.0000000 1.0000000
HYOU1 41.8759999 -0.8573256 0.5169000 -1.8182377 0.0690278 0.4275297
ICAM1 24.6886643 -0.6659989 0.5204815 -1.4886279 0.1365854 0.5424108
ICAM2 10.8083361 0.4563482 0.4720163 2.3210845 0.0202823 0.2182650
IDO1 0.0000000 NA NA NA NA NA
IER3 1.1809823 0.3505203 0.2188947 0.8797949 0.3789704 0.7992984
IFI16 3.0145957 -0.0659495 0.2486248 0.2994084 0.7646284 0.9693482
IFI27 133.6605816 0.1540520 0.4092373 0.3948979 0.6929182 0.9636086
IFI6 51.6441150 -0.5019659 0.4557766 -1.1768379 0.2392602 0.6738380
IFNAR1 2.2071676 -0.2742667 0.2223451 -1.0434595 0.2967355 0.7280014
IFNB1 115.0826951 -0.3869210 0.3769123 -1.0903027 0.2755798 0.7125520
IFNG 52.7909510 0.2900511 0.5185892 0.5391956 0.5897519 0.9170690
IFNGR1 6.1510082 0.0000000 0.3378041 0.0000000 1.0000000 1.0000000
IGLV3-21 2.4224592 0.0000000 0.2225541 0.0000000 1.0000000 1.0000000
IKBKE 46.3759206 -0.5309023 0.4676461 -1.1239589 0.2610305 0.6963264
IL10 79.7137372 0.1512528 0.4076199 0.3430103 0.7315907 0.9644026
IL12A 108.6412831 0.9248511 0.3916487 2.3291395 0.0198517 0.2182650
IL13 2.5923485 -0.1618831 0.3770079 -0.3201791 0.7488326 0.9644026
IL13bis 40.7959949 -0.4908994 0.5190642 -1.2687225 0.2045401 0.6334051
IL15 6.5935537 0.1425328 0.3472209 1.3042699 0.1921416 0.6182408
IL16 179.8707091 0.4583872 0.3704699 1.2418196 0.2143031 0.6393638
IL17A 87.2446406 1.0515151 0.4739290 2.9028790 0.0036975 0.0745006
IL17B 71.6468821 -0.0044000 0.4738849 0.1540676 0.8775564 0.9880121
IL17F 77.8016239 0.8033145 0.4984769 1.9251315 0.0542129 0.3683413
IL17RC 7.2777545 0.1374855 0.3833077 0.5920648 0.5538072 0.9001688
IL18 309.7585522 0.1471854 0.2635401 0.5524229 0.5806586 0.9140962
IL1A 45.0736149 -0.6387191 0.5203620 -1.7246938 0.0845827 0.4474849
IL1F10 381.6769491 -0.0226949 0.2292777 -0.1010322 0.9195249 0.9963956
IL1R2 37.0905905 0.3343595 0.5112293 0.6139749 0.5392319 0.9001688
IL1RL1 35.8570444 -0.6121352 0.5165201 -1.2340046 0.2172012 0.6393638
IL1RL2 55.9825928 -0.5569853 0.5202979 -1.5949808 0.1107165 0.5164953
IL1RN 571.6522134 0.0755794 0.1812302 0.4107137 0.6812825 0.9633554
IL2 144.8215332 -0.2673023 0.5189030 -0.7143151 0.4750324 0.8571639
IL20 55.6884174 -1.3744393 0.4759201 -3.2586606 0.0011194 0.0341957
IL20RA 18.4164890 -0.1343376 0.5020999 -0.0485867 0.9612487 1.0000000
IL20RB 54.0160452 -0.8070444 0.4486350 -1.9972358 0.0457996 0.3362186
IL20RBbis 49.2576943 -0.0997711 0.4182361 -0.8904750 0.3732109 0.7978119
IL22RA2 16.8197049 -0.0085794 0.3150384 -0.4717889 0.6370775 0.9498334
IL23A 109.1115991 -0.2818370 0.3777305 -0.7621568 0.4459664 0.8452775
IL25 208.0090943 -0.1905077 0.2478357 -0.7710118 0.4407000 0.8397241
IL27RA 1.0611980 0.0000000 0.2184757 0.0000000 1.0000000 1.0000000
IL2RB 26.3807028 0.5461474 0.5104451 1.1243745 0.2608542 0.6963264
IL31RA 48.5051522 -0.7689505 0.5160663 -1.4680274 0.1420968 0.5426034
IL33 56.3907079 0.5572063 0.5097692 1.0157929 0.3097280 0.7355359
IL36A 622.5187231 0.2939463 0.1709032 1.7182393 0.0857530 0.4477945
IL36Abis 469.3636974 -0.0305241 0.1910893 -0.1524111 0.8788627 0.9880121
IL36G 152.6844332 -0.0776828 0.3081304 -0.2537642 0.7996777 0.9751363
IL36RN 120.3478587 -0.8569566 0.3592992 -2.4305686 0.0150752 0.1903489
IL37 152.2799865 0.1213428 0.3303777 0.3840790 0.7009199 0.9636086
IL4 79.2997552 -0.7565342 0.4265062 -1.8019101 0.0715596 0.4289044
IL5RA 51.4116958 0.0470667 0.4743703 0.3994425 0.6895672 0.9636086
IL6R 62.5852980 0.1697775 0.4855127 0.2787172 0.7804618 0.9751363
IL6Rbis 34.0819412 0.8776347 0.5153968 2.0160243 0.0437974 0.3240326
IL7 18.9726126 0.0961993 0.3234454 0.2335373 0.8153442 0.9793368
ILKAP 427.1482358 -0.4045741 0.2411497 -1.6752498 0.0938852 0.4704197
ILVBL 2.1107921 -0.0475045 0.2221427 -0.4697997 0.6384981 0.9498334
INCA1 5.4596668 0.1600860 0.3321582 1.0201472 0.3076586 0.7355359
ING2 46.7277673 0.3859599 0.5049685 0.6865706 0.4923534 0.8666517
ING5bis 98.8481568 -0.5111672 0.4700117 -1.1563931 0.2475204 0.6807668
INHBA 16.4108306 -0.1197047 0.4790133 -0.1787191 0.8581582 0.9850616
INS 68.6444894 -0.6879876 0.5036035 -1.7834407 0.0745146 0.4299714
IRF3 106.1290440 0.0620501 0.3783247 0.1811677 0.8562360 0.9850616
ITGA6 3.3376216 -0.1616835 0.3113669 -0.8851177 0.3760931 0.7992984
ITGB2 7.3090425 -0.1041265 0.4216309 -0.4156577 0.6776605 0.9633554
ITM2C 193.1879647 0.2935832 0.3674318 0.9015291 0.3673071 0.7941548
ITM2Cbis 225.2178055 0.0538047 0.3162521 0.2167382 0.8284124 0.9829258
JAM3 94.6600212 -0.5027574 0.4499081 -1.0925413 0.2745953 0.7124431
JMY 45.1534006 -0.5083697 0.5026921 -1.4948663 0.1349493 0.5415128
JUNB 124.5270874 0.8347017 0.3625847 2.3239665 0.0201273 0.2182650
KARS 16.1534811 -0.3364442 0.5067719 -0.8316968 0.4055801 0.8189469
KATNB1 6.8867684 0.2533325 0.4167890 0.9637564 0.3351681 0.7539291
KCND2 30.9729479 0.1231236 0.5197748 0.4631092 0.6432861 0.9518624
KHDRBS2 633.3182183 -0.3334031 0.2634770 -1.2590460 0.2080137 0.6334051
KIAA0141 54.5318841 0.2628094 0.5063412 0.5182059 0.6043146 0.9290356
KIAA0141bis 44.9809488 0.3319625 0.4839918 0.7018503 0.4827725 0.8593713
KIAA1430 36.1463271 -0.5108827 0.5164058 -1.0239159 0.3058750 0.7355359
KIAA1967 459.3982787 0.2599732 0.2078038 1.2639621 0.2062436 0.6334051
KLC1 81.6095851 -0.2855773 0.3877972 -0.7085097 0.4786288 0.8571639
KLKB1 11.9947984 -0.0097857 0.4308168 -0.1503747 0.8804690 0.9880121
KLKB1bis 7.1375474 -0.2410605 0.3721639 -0.8380955 0.4019771 0.8189469
KPNA6 78.7557231 -0.5041921 0.4162201 -1.2187489 0.2229395 0.6496115
KRT1 26.1320843 -0.3680695 0.5107490 -0.5337184 0.5935364 0.9182334
KRT13 0.8267083 0.0000000 0.2158652 0.0000000 1.0000000 NA
KRT18 148.3032779 -0.2199826 0.3256204 -0.6787807 0.4972769 0.8704634
KRT8 394.9110795 0.0807153 0.2473197 0.3259075 0.7444943 0.9644026
KSR2 31.8415019 0.3522208 0.5202437 0.5188846 0.6038413 0.9290356
LACC1 11.4850743 -0.3612217 0.4645666 0.1419399 0.8871275 0.9880121
LAMP1 174.6287309 0.7416214 0.2904764 2.5810716 0.0098494 0.1332485
LAMTOR3 30.2340247 -0.1006769 0.5199900 -0.2198152 0.8260150 0.9829258
LARP1 5.0621559 0.0198254 0.3130756 0.1319310 0.8950389 0.9917563
LAT 41.3861596 0.0610779 0.5136190 -0.2112336 0.8327050 0.9839667
LAX1 11.8489479 0.2948610 0.4736983 0.3863491 0.6992381 0.9636086
LBP 62.7000058 0.3047188 0.4122510 0.7184673 0.4724692 0.8571639
LCN2 209.8172314 0.7898135 0.3298959 2.3860925 0.0170285 0.2067431
LEP 133.7657871 0.1699691 0.5200235 0.6092858 0.5423351 0.9001688
LEPROTL1 46.9563137 0.2945269 0.5202417 0.7562612 0.4494926 0.8466290
LGALS12 224.7092124 -0.6047234 0.2682995 -2.2455978 0.0247298 0.2465169
LGALS3 152.5985830 -0.1821361 0.3412933 -0.5752553 0.5651187 0.9011983
LGALS9 233.7534276 -0.3242729 0.3031337 -1.0821835 0.2791710 0.7126010
LIN28A 3.2314221 0.0125345 0.2342503 0.0671455 0.9464659 1.0000000
LMNA 134.1075140 -0.3705006 0.3032572 -1.2325717 0.2177356 0.6393638
LMNAbis 86.9566376 -0.5435846 0.3988479 -1.3992407 0.1617408 0.5593427
LOC554223 9.0511458 0.0000000 0.3876660 0.0000000 1.0000000 1.0000000
LPL 78.8947217 0.7059423 0.4915549 1.5279232 0.1265316 0.5311217
LRRC19 18.3714135 0.1546108 0.3265488 0.4652501 0.6417524 0.9517173
LTA 188.7780489 0.4339565 0.2851807 1.5228656 0.1277923 0.5311217
LTBR 53.9485419 -0.0538905 0.5116752 -0.2310197 0.8172995 0.9797248
LYN 460.6606503 -0.2010397 0.2332783 -0.8608496 0.3893209 0.8105478
LYNbis 318.2551371 -0.1218870 0.2296913 -0.5325008 0.5943792 0.9182334
MAEL 29.6678766 0.8173938 0.5125757 2.7527386 0.0059099 0.0981874
MAFG 311.0688365 0.0107222 0.2625298 0.0479602 0.9617480 1.0000000
MAL 349.4930344 -0.0730900 0.1942216 -0.3753096 0.7074302 0.9636086
MAP2K5 121.1239918 -0.4738930 0.3678743 -1.3294844 0.1836882 0.6082264
MAPK13 340.0908299 0.0716705 0.2520203 0.2657970 0.7903956 0.9751363
MAPK14 151.0191768 -0.5018376 0.3148184 -1.5811415 0.1138457 0.5182650
MAPK8 35.4364846 0.5405359 0.5134178 1.0693035 0.2849329 0.7214746
MAPK8IP1 9.9517952 0.6080946 0.4692219 1.0140654 0.3105515 0.7355359
MAPK8IP2 171.2293704 0.2620732 0.2674527 0.9813154 0.3264372 0.7474380
MAPK9 202.4549873 0.7004284 0.3009465 2.3319083 0.0197055 0.2182650
MAPKAPK2 20.5461488 0.1731717 0.2356987 1.1080601 0.2678359 0.7084932
MAPKAPK5 398.7340636 0.0537199 0.2122495 0.2543128 0.7992539 0.9751363
MAPT 598.5460422 -0.1348629 0.2282556 -0.5901054 0.5551200 0.9001688
MAS1 57.4909061 0.4005665 0.5003651 1.3274671 0.1843542 0.6083047
MAZ 177.7667463 0.1709451 0.2683132 0.6396318 0.5224120 0.8855050
MBL2 54.3498896 -0.2027832 0.5182413 -0.3114122 0.7554873 0.9644026
MBP 833.9942347 -0.2926655 0.1973274 -1.4876622 0.1368400 0.5424108
MCL1 19.0553814 0.3270833 0.5055134 0.9771830 0.3284786 0.7495643
MCPH1 62.1447530 0.3978002 0.4627257 0.7489296 0.4538997 0.8511742
MCTS1 39.7001577 0.1776667 0.5182599 0.4801272 0.6311370 0.9457068
MDK 74.1678309 0.5970377 0.4684490 1.4864457 0.1371613 0.5424108
MED1 32.6473983 -0.9471851 0.5014754 -1.7876447 0.0738333 0.4299714
MEIS2 0.0000000 NA NA NA NA NA
METTL21C 286.2497210 0.4903108 0.2510886 1.9455510 0.0517087 0.3628707
MFF 207.8857103 0.3251239 0.3427546 0.9532629 0.3404569 0.7640110
MGLL 618.2818936 0.0425730 0.1860193 0.2245036 0.8223655 0.9829258
MGST2 37.1126472 -0.0441577 0.5193874 -0.1530382 0.8783681 0.9880121
MIF 700.0911671 0.2605296 0.1631627 1.5985375 0.1099234 0.5153340
MITF 65.2711319 0.7726264 0.4866518 1.8359188 0.0663697 0.4190138
MITFbis 255.4095235 -0.1544346 0.2900647 -0.5490363 0.5829806 0.9140962
MKNK2 42.6836672 0.2103421 0.4983798 0.4498701 0.6528041 0.9586285
MLH1 31.1278877 -0.1250331 0.5126702 -0.4898812 0.6242180 0.9398004
MLLT11 913.5791620 -0.1706281 0.1578160 -1.0856204 0.2776470 0.7125520
MMP10 19.8488070 0.0307465 0.4821119 0.2739921 0.7840907 0.9751363
MMP26 2.8617654 0.2104033 0.3629327 0.4495770 0.6530155 0.9586285
MMP3 40.9637848 -0.0449330 0.5173883 0.0900894 0.9282162 1.0000000
MMP9 6.2542114 -0.0477282 0.3856830 -1.0226557 0.3064707 0.7355359
MOAP1 61.1914993 -0.5846810 0.4344738 -1.4085570 0.1589662 0.5543983
MSH2 4.1934924 -0.2860789 0.3210386 -1.2999340 0.1936236 0.6182408
MSX2 5.4794836 0.3795041 0.3177198 2.2074513 0.0272825 0.2532997
MT1DP 1.0443467 0.0000000 0.2186389 0.0000000 1.0000000 1.0000000
MUC1 25.7240051 -0.0406375 0.5094613 0.4279207 0.6687089 0.9627484
MUC1bis 70.8201900 -0.5130105 0.4893870 -1.3989190 0.1618373 0.5593427
MVK 326.6053607 -0.4660117 0.2509987 -1.8689177 0.0616343 0.3970588
MYB 29.5325220 0.5239442 0.5060599 1.8040192 0.0712283 0.4289044
MYD88 306.6383084 -0.0186694 0.2349382 -0.0781826 0.9376828 1.0000000
MYLK3 11.0415208 -0.4930084 0.4912856 -1.7452493 0.0809415 0.4430727
MZF1 16.7949101 0.0914299 0.5139086 0.0805758 0.9357793 1.0000000
NAMPT 17.0250496 -0.1957715 0.4550979 -0.1515635 0.8795312 0.9880121
NANOG 522.0342155 0.4538408 0.2219765 2.0445581 0.0408984 0.3200484
NANOS3 282.1584780 0.9536701 0.3062473 3.1201272 0.0018077 0.0438954
NCF1 410.8852104 -0.0836259 0.2306386 -0.3635960 0.7161597 0.9644026
NCF2 127.7588897 -0.1178084 0.3443947 -0.3731709 0.7090212 0.9636086
NCK1 40.0043580 -0.7966392 0.5181291 -1.7658759 0.0774167 0.4363904
NCK2 128.9373932 -0.2415142 0.3718087 -0.6391883 0.5227004 0.8855050
NCKIPSD 115.8192543 -0.6533022 0.3572028 -1.8120672 0.0699758 0.4275297
NDE1 39.0806571 -0.1180500 0.4862596 -0.2455901 0.8059996 0.9760634
NDEL1 86.2342060 -0.2436715 0.4457619 -0.4181945 0.6758049 0.9633554
NDUFA13 98.9386889 -0.1529862 0.4435715 -0.4103197 0.6815714 0.9633554
NDUFS3 67.8292301 -0.2473670 0.4682360 -0.6527666 0.5139068 0.8783129
NEFL 107.3188790 0.7542311 0.3807866 1.9690114 0.0489518 0.3511920
NEFM 7.0741934 0.0850941 0.4300451 0.4714726 0.6373033 0.9498334
NEK2 50.2513271 0.5339909 0.5203864 1.2986043 0.1940798 0.6182408
NEUROD1 51.8690482 0.7329407 0.5135851 1.7139788 0.0865326 0.4477945
NFATC1 16.4577236 -0.1052947 0.5194561 -0.2423194 0.8085327 0.9773957
NFATC1bis 47.3651739 0.1908448 0.4771910 0.3733057 0.7089209 0.9636086
NFE2L1 16.6423983 -0.6807148 0.5120361 -2.9582734 0.0030937 0.0665843
NFE2L2 85.3881461 1.2890499 0.4041011 3.2698689 0.0010760 0.0339649
NFKB1 33.7572268 -0.3099475 0.4941228 -0.5909404 0.5545603 0.9001688
NFKBIA 369.7745554 0.3812223 0.2223274 1.7151342 0.0863206 0.4477945
NGF 130.8143419 0.8055648 0.4153636 2.1030632 0.0354602 0.2941416
NGFRAP1 250.2807871 0.2064820 0.2534470 0.8144543 0.4153848 0.8195195
NLRP10 25.5021683 -0.8143210 0.5092901 -1.6695624 0.0950060 0.4714691
NLRP12 5.9817521 0.2806659 0.4356251 0.5815521 0.5608684 0.9011983
NLRX1 53.9398293 -1.2232919 0.4262689 -2.8618943 0.0042112 0.0801480
NME5 71.4817150 -0.1799574 0.4603038 -0.3904382 0.6962125 0.9636086
NMT1 277.4703811 -0.2426199 0.2732139 -0.9061316 0.3648662 0.7924960
BC008840 2.6080618 0.0000000 0.2282564 0.0000000 1.0000000 1.0000000
BC110533 0.6795433 0.0746969 0.2159951 0.1274242 0.8986047 NA
NOC2L 92.4455482 -0.5055355 0.3513022 -1.4472769 0.1478194 0.5442474
NONO 244.1623490 -0.0845625 0.3285425 -0.2617034 0.7935501 0.9751363
NOV 73.2013302 0.2762553 0.4351421 0.7292550 0.4658457 0.8571639
NOX1 11.8865545 0.1303844 0.4233774 0.9304193 0.3521540 0.7771323
NOX5 5.0893791 -0.3377095 0.3474684 -1.7818799 0.0747688 0.4299714
NPFF 166.6648662 0.3182240 0.3868109 0.8270967 0.4081822 0.8189469
NPPA 119.1236079 0.5419769 0.4286705 1.2136102 0.2248966 0.6508382
NR1D2 10.7007813 0.1922566 0.4020518 1.7102638 0.0872171 0.4488837
NR1H3 103.1152784 -0.0539275 0.4088567 -0.1580596 0.8744098 0.9880121
NR1H3bis 65.5544500 -0.3273196 0.4724157 -0.6638180 0.5068068 0.8714234
NR1H4 22.9571186 -0.2408654 0.5122070 -1.1883999 0.2346759 0.6693918
NR2C2 93.3760800 -0.2723720 0.3605800 -0.7571540 0.4489576 0.8466290
NR3C1 56.3899773 -0.3134276 0.4582158 -0.7082065 0.4788170 0.8571639
NR4A1 221.2595026 -0.2987565 0.2770123 -1.0796426 0.2803014 0.7135629
NR5A1 254.9549609 -0.3410568 0.2265356 -1.5047156 0.1323972 0.5415128
NRP1 17.1990233 0.0204470 0.4778087 0.3089292 0.7573754 0.9649886
NT5C1A 3.4531660 -0.0444948 0.2341589 0.1040701 0.9171137 0.9963956
NT5E 29.6689858 0.4528607 0.5205113 1.0825521 0.2790073 0.7126010
NT5Ebis 15.3166563 -0.4081759 0.4679777 -1.5425461 0.1229409 0.5225185
NTNG1 2.9615221 -0.1587810 0.2246141 -4.6672577 0.0000031 0.0004818
NTRK3 25.7010335 0.0652561 0.5091798 0.2154325 0.8294301 0.9829258
NUP50 328.5697193 -0.7451415 0.2875318 -2.6041462 0.0092103 0.1301820
NUPR1 330.4161883 0.1298298 0.2302711 0.5670774 0.5706616 0.9037066
O3FAR1 33.5283919 0.2875422 0.5193017 0.9833712 0.3254248 0.7474380
OGG1 22.6593776 -0.7472354 0.5124925 -1.5906903 0.1116793 0.5182650
OPA1 1.0937004 -0.0891626 0.2186987 -0.4186885 0.6754438 0.9633554
OPRM1 31.2856887 -0.0678164 0.5179257 -0.2194893 0.8262689 0.9829258
OR51E1 2.3762068 0.1842052 0.2226750 3.5694372 0.0003577 0.0125477
OSM 54.2101768 -0.1139720 0.5169958 -0.0641066 0.9488853 1.0000000
P2RX1 53.3558395 0.1615742 0.4839354 0.2327679 0.8159416 0.9793368
P2RX1bis 34.2659178 -0.6052525 0.5008576 -1.2494751 0.2114913 0.6393638
P4HB 39.5806494 -0.3594228 0.5129611 -0.6330878 0.5266763 0.8874776
PAK1 14.7941470 -0.0968762 0.4298004 -0.1328805 0.8942879 0.9917563
PAK7 77.4198319 0.3136729 0.3880168 0.8317103 0.4055725 0.8189469
PAM16 74.0805066 -0.0342757 0.4636188 -0.1923520 0.8474665 0.9839667
PAPSS2 66.3211189 -0.0433553 0.4534034 -0.1074725 0.9144141 0.9963956
PARK2 27.3059386 -0.0383499 0.5202494 -0.0416683 0.9667631 1.0000000
PBK 120.6188281 0.4188381 0.3676985 1.1406299 0.2540240 0.6853581
PCGF2 381.7227606 0.3184332 0.2228360 1.4325618 0.1519831 0.5442474
PCLO 415.1103361 0.2420673 0.2039148 1.1758837 0.2396414 0.6738380
PCP4 0.0000000 NA NA NA NA NA
PCYT1A 420.6553055 -0.1389889 0.1975667 -0.6999083 0.4839845 0.8599125
PDCD10 129.4658214 -0.6677036 0.3540549 -1.8583910 0.0631135 0.4038412
PDCD5 406.2429024 0.1666451 0.2036640 0.8241515 0.4098535 0.8189469
PDCL3 823.7615901 -0.1225407 0.1990485 -0.6148677 0.5386421 0.9001688
PDE4D 73.1461871 1.0558497 0.4328088 2.5982158 0.0093710 0.1305044
PDE4Dbis 20.7629763 -0.5392315 0.5072686 -1.5465835 0.1219637 0.5225185
PDE6G 106.5190639 -0.1266895 0.4606963 -0.3451250 0.7300004 0.9644026
PDIA3 2.3630672 0.0355305 0.3255776 -0.1406531 0.8881440 0.9880121
PDIA3bis 15.2067842 0.1270636 0.4653412 0.2692228 0.7877582 0.9751363
PDK1 22.6973925 -0.7078853 0.5044355 -1.4532317 0.1461594 0.5442474
PDPK1 126.0372316 -0.8205761 0.3487438 -2.4097947 0.0159615 0.1963057
PDXDC1 55.6617036 -0.1888254 0.4429945 -0.4469035 0.6549447 0.9586285
PEA15 79.9798712 0.5236384 0.4571411 1.0560988 0.2909231 0.7242846
PEA15bis 42.3420492 -0.3495802 0.5192605 -0.5829750 0.5599101 0.9011983
PEA15bis2 1.3122605 0.0037587 0.2380938 0.0395625 0.9684419 1.0000000
PELI3 243.7513147 -0.0500245 0.2450284 -0.1984666 0.8426800 0.9839667
PER1 146.8118942 0.2431204 0.3195664 0.7268693 0.4673060 0.8571639
PER1bis 259.7689263 0.6211322 0.2357483 2.6199348 0.0087947 0.1281314
PF4 51.7237377 0.2296928 0.5030035 0.8225382 0.4107706 0.8189469
PGK1 138.9200405 -0.0608667 0.3555482 -0.1301402 0.8964555 0.9917563
PGLYRP1 61.1574725 0.8727234 0.3973864 2.2149911 0.0267607 0.2532997
PHIP 18.9939667 0.4501950 0.4846222 1.7292579 0.0837629 0.4456377
PHLDA3 123.0310643 0.3915217 0.3333258 1.1555968 0.2478461 0.6807668
PHLDB1 109.5320975 0.2192141 0.3801937 0.7168856 0.4734447 0.8571639
PIAS4 25.4883680 -1.1402574 0.5202102 -2.4785911 0.0131902 0.1711118
PIH1D1 582.7653490 0.3202033 0.1959703 1.6346477 0.1021229 0.4859820
PIK3AP1 67.8054575 -0.4958132 0.4776056 -1.1537064 0.2486205 0.6807668
PIK3R1 68.1142285 -0.2812192 0.4517038 -0.6613375 0.5083959 0.8721937
PIKFYVE 2.2603891 -0.1214673 0.2223084 -0.7844234 0.4327917 0.8330360
PKM2 9.3947075 -0.2095381 0.4085122 -0.3112173 0.7556354 0.9644026
PLA2G2D 68.9895179 0.6185514 0.4573128 1.2996621 0.1937168 0.6182408
PLA2G7 7.1873323 -0.2168311 0.3851972 -0.8454890 0.3978379 0.8189469
PLAT 11.0719525 0.2577635 0.4790675 0.9233845 0.3558069 0.7771323
PLAUR 75.1889347 1.1999915 0.4459461 2.8541001 0.0043159 0.0801480
PLCB1 2.1229729 -0.1628132 0.2223019 -0.7864244 0.4316189 0.8330360
PLD3 63.5045675 0.1578704 0.4383696 0.3445291 0.7304484 0.9644026
PLD4 136.8093938 -0.7916292 0.3623951 -2.1532546 0.0312987 0.2747532
PLEKHF1 246.7645781 -0.1600248 0.2654086 -0.5913561 0.5542819 0.9001688
PLSCR1 48.2530498 -0.1294223 0.5168065 -0.1195302 0.9048553 0.9963956
PMAIP1 190.1712348 0.3599664 0.3591213 0.9851780 0.3245367 0.7474380
PML 33.5691930 -0.3230936 0.5077206 -0.7348854 0.4624093 0.8571639
PMS1 1.5544840 0.0000000 0.2207204 0.0000000 1.0000000 1.0000000
PNMA1 119.5272138 -0.0372611 0.3357111 -0.1126654 0.9102958 0.9963956
POLB 66.8611454 -0.0632770 0.5073342 -0.0485439 0.9612828 1.0000000
POU5F1 165.2322306 -0.3269875 0.3108268 -1.0877356 0.2767118 0.7125520
PPARA 151.3827287 -0.1259344 0.3050929 -0.4146642 0.6783877 0.9633554
PPARD 256.0250405 -0.2438608 0.2746393 -0.8757923 0.3811429 0.8012022
PPARG 10.3483913 -0.0012406 0.4179910 0.1756355 0.8605803 0.9854529
PPIA 369.6286106 0.0773319 0.2180626 0.3624215 0.7170371 0.9644026
PPIAbis 16.6149484 0.1157594 0.5156244 0.2504951 0.8022045 0.9751363
PPP1CA 764.2205302 0.0698984 0.2381863 0.3015151 0.7630218 0.9686081
PPP1R13B 6.7215193 -0.5868257 0.3950174 -2.2546427 0.0241558 0.2433565
PPP1R15A 30.2445834 -0.3973210 0.5146652 -0.6776571 0.4979891 0.8704634
PPP2R1A 93.9823392 0.0513108 0.3714370 0.1396989 0.8888979 0.9880121
PPP2R5C 34.4292250 -0.0671374 0.5177956 -0.0988947 0.9212219 0.9970253
PPP3CC 68.5332361 -0.5613206 0.4850011 -1.2868714 0.1981391 0.6275510
PRDX2 221.1471870 0.2496123 0.3074703 0.8102926 0.4177720 0.8197691
PRDX6 393.5744892 -0.1723116 0.2507859 -0.6868779 0.4921597 0.8666517
PRELID1 54.3090532 0.7969263 0.5205088 2.1663786 0.0302823 0.2747532
PRKCA 35.8147161 -0.5345570 0.5040282 -1.1432343 0.2529413 0.6853581
PRKRA 35.9061407 0.2661510 0.5205022 0.3118918 0.7551228 0.9644026
PROC 40.6301183 0.1564666 0.5204909 0.2142538 0.8303491 0.9829258
PRODH 52.8748286 -0.7776845 0.4608049 -1.5870109 0.1125102 0.5182650
PRRC1 3.4158295 -0.2659837 0.2699041 -1.2661479 0.2054602 0.6334051
PSMA1 79.3547800 -0.4443112 0.4335672 -1.0601182 0.2890908 0.7242846
PSMA1bis 64.4134957 0.9517937 0.4889752 2.0340123 0.0419504 0.3200484
PSMB4 83.3545885 -0.1473821 0.4435584 -0.3079340 0.7581326 0.9649886
PSMD10 210.1632958 -0.1747414 0.2798747 -0.5961283 0.5510895 0.9001688
PSME3 117.7367255 0.6769728 0.4072405 1.7714940 0.0764786 0.4336840
PTGER3 13.8048767 -0.3461831 0.5049413 -2.0930955 0.0363406 0.2941416
PTGER4 23.5289562 0.5952056 0.5146430 1.2442190 0.2134190 0.6393638
PTGES 41.0196316 -0.8422246 0.4976927 -2.1519281 0.0314030 0.2747532
PTGIS 35.6405964 -0.0076117 0.5180110 -0.1906776 0.8487782 0.9839667
PTH 69.0170792 0.0104846 0.4343986 -0.0076440 0.9939011 1.0000000
PTPN11 594.9847715 -0.0955112 0.1978230 -0.4880630 0.6255052 0.9402436
PTPN12 8.8186404 -0.6603395 0.4109677 -2.6778844 0.0074089 0.1130987
PTPN2 19.5492931 -0.3176775 0.4760713 -0.6644557 0.5063987 0.8714234
PTPN6 111.3032368 0.6168391 0.3488073 1.7615700 0.0781420 0.4378725
PTPRE 9.9427284 0.4056953 0.4269123 2.5587031 0.0105063 0.1401339
PTPRR 42.3735968 1.0944812 0.5113704 2.4611708 0.0138484 0.1772227
PTPRRbis 54.0852257 2.0770002 0.4964296 4.5552359 0.0000052 0.0006034
PTTG1 94.4949553 0.8256395 0.4093904 2.0461720 0.0407395 0.3200484
PYCARD 517.6360511 0.0893211 0.1663644 0.5400577 0.5891573 0.9170690
QARS 111.9767079 -0.0215885 0.3319292 -0.0719649 0.9426299 1.0000000
QARSbis 117.0748854 -0.1712406 0.3656543 -0.4482090 0.6540024 0.9586285
RAB4A 132.6168686 -0.1404363 0.3241968 -0.4402731 0.6597393 0.9611894
RAB5A 35.0185951 0.2311346 0.5200403 0.7800921 0.4353367 0.8343287
RABGEF1 46.2507986 0.1568857 0.4976861 0.1184546 0.9057075 0.9963956
RABGGTB 0.0000000 NA NA NA NA NA
RAC1 274.2967408 1.1386053 0.3000435 3.7962039 0.0001469 0.0060496
RAPGEF5 2.3566505 0.0000000 0.2223298 0.0000000 1.0000000 1.0000000
RARA 345.7142988 -0.0464623 0.2379587 -0.2076931 0.8354686 0.9839667
RARG 137.5317056 0.3493504 0.3594239 1.0405990 0.2980617 0.7293655
RASGRP2 40.2122823 -0.0778276 0.4788209 -0.1018245 0.9188960 0.9963956
RBCK1 7.6901904 -0.4752724 0.3999262 -1.7322638 0.0832266 0.4456377
RBM17 55.3420651 0.5493681 0.5175693 1.0044486 0.3151624 0.7442863
RBMX 53.5440659 -0.2870219 0.5031340 -0.5921655 0.5537398 0.9001688
RBPJ 26.9684751 -1.0407442 0.5204596 -2.0297681 0.0423801 0.3200484
RBPMS 47.6625768 0.5892724 0.5138944 1.2813964 0.2000545 0.6314495
RCAN1 163.2470255 0.1706067 0.3553791 0.5355463 0.5922721 0.9179734
REG3A 87.6577780 0.0584086 0.3990368 0.1135224 0.9096164 0.9963956
REG3G 90.6871012 0.2373432 0.5197708 0.5494886 0.5826702 0.9140962
RELA 441.5672077 0.3260810 0.2212517 1.4692631 0.1417615 0.5426034
RELL1 69.0291132 0.1414146 0.4878342 0.4646487 0.6421830 0.9517173
REN 1.1809823 0.3505203 0.2188947 0.8797949 0.3789704 0.7992984
RET 31.6272157 0.1792067 0.5203936 0.5718272 0.5674390 0.9031341
RETbis 0.8876380 -0.0272024 0.2175510 -0.1862719 0.8522315 NA
RFFL 352.8393553 0.0309645 0.2129441 0.1466669 0.8833949 0.9880121
RHBDD3 69.4617671 0.1405677 0.4739652 0.3130322 0.7542562 0.9644026
RHOA 102.3123010 0.9244142 0.4339723 2.2006317 0.0277621 0.2552497
RHOAbis 1.4076324 -0.0542353 0.3096208 -0.1150921 0.9083721 0.9963956
RHOT2 112.5597157 -0.2318841 0.3840775 -0.6307615 0.5281965 0.8884584
RICTOR 49.1700953 -0.3945776 0.4508776 -0.9227233 0.3561514 0.7771323
RIPK3 190.2651853 -0.5430945 0.3242054 -1.6369547 0.1016399 0.4859820
RNF183 98.3487979 0.0305394 0.4293242 0.0930784 0.9258413 0.9997397
RNF41 340.5006393 -0.0385163 0.2255141 -0.1794114 0.8576147 0.9850616
RPL11 308.5135195 -0.1950990 0.2413660 -0.8410131 0.4003406 0.8189469
RPL26 104.1397622 -0.1008780 0.4456519 -0.1905974 0.8488410 0.9839667
RPS19 935.4936917 -0.1500316 0.1456807 -1.0296993 0.3031512 0.7355359
RPS27L 186.8910402 0.3108972 0.3175824 0.9894566 0.3224398 0.7474380
RPS27Lbis 52.4700081 0.0436741 0.5100788 -0.3678490 0.7129858 0.9644026
RPS3 569.7854852 -0.2187996 0.1778129 -1.2316701 0.2180723 0.6393638
RPS6KA1 1.6265427 0.1454527 0.2212426 0.2088579 0.8345592 0.9839667
RPS6KA3 50.7243454 0.8236583 0.5173061 2.1403172 0.0323291 0.2783245
RPS6KA4 70.7989156 0.3702830 0.4490284 0.7902146 0.4294024 0.8315830
RPS6KA5 53.2260690 -0.1547415 0.4578766 -0.3768551 0.7062813 0.9636086
RPS6KB1 201.5919050 -0.0480888 0.2443562 -0.1766796 0.8597601 0.9854529
RPS7 52.7538677 -0.3331909 0.5204900 -0.5964228 0.5508928 0.9001688
RPSAbis 3.4216287 -0.1572099 0.2465430 -4.3539034 0.0000134 0.0011513
RPTOR 60.8176258 -0.6202128 0.4070583 -1.5666695 0.1171920 0.5225185
RRM2B 68.8773895 -0.1604785 0.5068282 -0.2063800 0.8364941 0.9839667
RRN3 24.7657565 -0.2618451 0.5193616 -0.5471454 0.5842788 0.9140962
RSPH3 74.6751499 0.4014796 0.4921062 1.0158139 0.3097180 0.7355359
RTN4 33.4164873 0.4345887 0.5041054 0.9890536 0.3226369 0.7474380
RUNX1 236.0814406 -0.4666283 0.2985431 -1.5692342 0.1165934 0.5225185
S100A8 74.4129551 0.8428997 0.4738238 1.9038443 0.0569305 0.3728392
S100A9 52.7016301 -0.7232387 0.4783114 -1.6135763 0.1066194 0.5048427
SAA1 36.9345020 1.0760718 0.5131203 2.6750599 0.0074716 0.1130987
SAA2 82.5867644 -0.8541653 0.4488539 -2.0985724 0.0358546 0.2941416
SAA4 34.9518733 -0.2841593 0.5204730 -1.2122353 0.2254223 0.6508382
SAFB2 151.6527521 -0.0778476 0.3376605 -0.1894937 0.8497059 0.9839667
SCAMP1 2.9862327 -0.0827839 0.2641780 -0.6708586 0.5023106 0.8714234
SCG2 25.9360404 -0.1006104 0.4902888 -0.2202743 0.8256575 0.9829258
SCGB1A1 97.5944404 -0.3366192 0.4542650 -0.8022939 0.4223830 0.8230384
SCNN1B 31.6019045 -0.5910169 0.5193538 -1.8552211 0.0635647 0.4039982
SCNN1G 25.0172765 -0.4925991 0.4981044 -1.8084388 0.0705382 0.4282033
SELE 25.1716920 -0.7198782 0.5115594 -2.2232557 0.0261986 0.2532997
SEMA7A 4.8209288 0.2872301 0.3878100 1.2727438 0.2031090 0.6334051
SERPINA3 95.3120459 0.3544839 0.4049254 0.9848258 0.3247096 0.7474380
SERPINC1 48.9508841 -0.4224498 0.4711016 -1.9026417 0.0570873 0.3728392
SERPINF1 70.7722452 0.1489979 0.4968028 0.5963014 0.5509739 0.9001688
SETD6 45.8699308 0.1501417 0.4444098 0.3666114 0.7139089 0.9644026
SFN 451.2809536 0.1478498 0.2233006 0.6514669 0.5147451 0.8783129
SFPQ 18.4347443 0.6000226 0.4766899 1.3056058 0.1916866 0.6182408
SFRP1 45.5478945 0.2815303 0.5013202 0.5803410 0.5616847 0.9011983
SFRP2 234.0933471 0.3697714 0.2703842 1.3912205 0.1641586 0.5632542
SGK1 39.6813840 -0.4297724 0.5129522 -1.2331781 0.2175093 0.6393638
SH2B1 266.9448047 -0.2774459 0.2701467 -1.0243794 0.3056561 0.7355359
SH2D3C 14.1533771 0.0704672 0.5054831 0.2168040 0.8283611 0.9829258
SH3GLB1 129.0413701 -0.1891209 0.2911110 -0.6408067 0.5216483 0.8855050
SHARPIN 161.3754747 0.4601015 0.3395699 1.3535375 0.1758840 0.5885588
SHC1 169.3256653 -0.5071601 0.3331781 -1.5205346 0.1283767 0.5311217
SHISA5 47.6764259 -0.0490390 0.5134860 -0.2198174 0.8260134 0.9829258
SHPK 176.1304542 -0.3762366 0.3048536 -1.2323907 0.2178032 0.6393638
SIAH1 13.1601770 -0.1592163 0.4648214 -1.4471352 0.1478591 0.5442474
SIGLEC10 6.1132214 -0.3081431 0.3293016 -2.0263328 0.0427307 0.3200484
SIVA1 129.4671716 1.6056486 0.3583207 4.5333841 0.0000058 0.0006034
SLAMF8 73.3937875 0.0568313 0.4552681 0.1494432 0.8812039 0.9880121
SLC25A5 31.4433500 -0.6051910 0.5202294 -1.2972455 0.1945467 0.6182408
SLC7A2 7.2481554 -0.1675185 0.3570427 -1.5440433 0.1225778 0.5225185
SLC9A1 16.6622076 -0.3251529 0.5178272 -0.9686131 0.3327383 0.7538353
SLC9A3R1 261.8676753 0.3972831 0.2758483 1.4420196 0.1492968 0.5442474
SMAD1 99.5355108 -0.0624651 0.3810221 -0.1578646 0.8745635 0.9880121
SMAD2 192.9030418 0.8546977 0.3237587 2.6674284 0.0076434 0.1130987
SMAD3 746.2447989 0.0159432 0.1581461 0.1015710 0.9190972 0.9963956
SMAD3bis 262.6627598 0.0846955 0.2483776 0.3240840 0.7458745 0.9644026
SMAD4 128.3167821 -0.1153324 0.3029496 -0.3763170 0.7066812 0.9636086
SMARCAD1 1.2939190 0.0971720 0.2200785 4.2033195 0.0000263 0.0017056
SMPDL3B 112.9019650 0.3264375 0.4088769 0.7110204 0.4770716 0.8571639
SMYD3 1.8698886 0.3593278 0.2213647 1.0194085 0.3080091 0.7355359
SNAI1 129.3332277 0.6235449 0.3697419 1.6807806 0.0928055 0.4674832
SNAP23 67.3156804 -0.9791505 0.5058986 -2.1497017 0.0315788 0.2747532
SNCA 806.2714404 -0.1102376 0.1808918 -0.6087290 0.5427041 0.9001688
SNCG 545.1620934 0.0710528 0.2108713 0.3383009 0.7351365 0.9644026
SNIP1 161.7046442 -0.4291805 0.3703456 -1.1653953 0.2438590 0.6792191
SNW1 22.1930531 0.3767545 0.5119138 0.6770118 0.4983984 0.8704634
SNX4 81.6408919 0.2953337 0.3979165 0.8102992 0.4177683 0.8197691
SOCS3 240.3567917 0.0701494 0.3092983 0.2399131 0.8103976 0.9773957
SOCS5 2.9980564 0.0000000 0.2617477 0.0000000 1.0000000 1.0000000
SOD1 205.0791254 -0.1009531 0.2643001 -0.3597624 0.7190248 0.9644026
SOD2 474.6016885 -0.0510844 0.2017159 -0.2554736 0.7983573 0.9751363
SORBS3 505.8813592 0.1172614 0.1962911 0.5984771 0.5495216 0.9001688
SOX10 104.3600305 0.0525756 0.3448952 0.1430234 0.8862717 0.9880121
SOX2 526.0346927 -0.1706927 0.1805406 -0.9437094 0.3453182 0.7694501
SP100 168.5770703 -0.2369768 0.3447493 -0.6573958 0.5109265 0.8749500
SPAST 13.7918473 -0.3874991 0.5127599 -1.0934320 0.2742042 0.7124431
SPATA2 155.4717887 0.0067763 0.3151108 -0.0163228 0.9869769 1.0000000
SPHK1 73.9183442 -0.1818845 0.4104003 -0.4408132 0.6593483 0.9611894
SPN 71.3396475 -0.5734490 0.4318287 -1.3706766 0.1704758 0.5750708
SREBF1 0.6366254 0.0644187 0.2153070 0.3078090 0.7582277 NA
SRPX 34.2976063 -1.6759658 0.5158810 -4.0504701 0.0000511 0.0028474
SSBP3 1.9949446 0.1438125 0.2220967 0.1945176 0.8457706 0.9839667
SSBP3bis 30.3943271 0.0504756 0.5200553 0.0553514 0.9558585 1.0000000
ST3GAL2 0.8386887 0.3426524 0.2166076 0.7927155 0.4279436 NA
STAP1 141.7917476 -0.1362077 0.3522818 -0.3651353 0.7150104 0.9644026
STAR 89.8430003 -0.2687925 0.3685283 -0.7150888 0.4745542 0.8571639
STAT1 12.6023152 0.0745609 0.5068219 0.3248925 0.7452624 0.9644026
STAT3 69.6851405 -0.0079466 0.4566865 -0.0082068 0.9934520 1.0000000
STAT5A 23.2416475 -0.4193315 0.5175097 -0.8933601 0.3716644 0.7978119
STAT5B 119.6598979 -0.4657904 0.3542638 -1.3219181 0.1861954 0.6101283
STK10 26.8636816 -0.5954320 0.5059370 -1.7303766 0.0835630 0.4456377
STK11 164.2624217 0.0264991 0.2887097 0.1143770 0.9089389 0.9963956
STK25 149.8935194 -0.3006355 0.2519360 -1.1864418 0.2354479 0.6695770
STK4 360.0840596 1.0994516 0.2438372 4.5136719 0.0000064 0.0006034
STMN1 596.7058250 0.0581493 0.2161785 0.2643641 0.7914994 0.9751363
STMN2 605.0509161 -0.0584260 0.1956070 -0.3170181 0.7512299 0.9644026
STMN3 362.0886656 -0.0985788 0.2339485 -0.4334757 0.6646692 0.9627484
STRADB 37.1095327 0.0938048 0.5110013 0.2515316 0.8014031 0.9751363
STX4 578.1767650 0.1304346 0.1899377 0.6940951 0.4876226 0.8647539
SUCNR1 72.3337621 0.3491540 0.4407466 0.8430437 0.3992040 0.8189469
SULT4A1 118.1838226 -1.2875790 0.3478312 -3.6903046 0.0002240 0.0088381
SULT4A1bis 218.9246461 -0.2249614 0.2479907 -0.9070756 0.3643668 0.7924960
SUPT5H 4.4663856 -0.0206192 0.3603659 -0.0518559 0.9586435 1.0000000
SUPV3L1 11.9769212 -0.0013169 0.4509985 -0.0502026 0.9599610 1.0000000
SYK 0.7046512 0.0000000 0.2150481 0.0000000 1.0000000 NA
SYN3 34.0419339 0.1814214 0.5197600 0.0757095 0.9396502 1.0000000
SYT11 22.5727393 -0.1185062 0.5156392 -0.1625791 0.8708499 0.9876585
TAC1 26.8670583 -0.4887053 0.5061557 -1.1516942 0.2494468 0.6807668
TACR1 14.5148948 0.1561701 0.4640469 0.0181184 0.9855444 1.0000000
TAGLN2 101.9595578 -0.0283145 0.4443340 0.0710532 0.9433554 1.0000000
TAL2 39.5405677 -0.1850819 0.4984514 -0.4001881 0.6890180 0.9636086
TBC1D23 54.8518692 -0.4337542 0.4742124 -1.2385908 0.2154971 0.6393638
TCF7L2 70.4693651 -0.2628763 0.4994247 -0.8070398 0.4196436 0.8197691
TEFM 1.8866562 0.0000000 0.2215674 0.0000000 1.0000000 1.0000000
TFAP4 310.1230356 -0.0132678 0.2272001 -0.0580512 0.9537079 1.0000000
TFCP2 51.9587122 -0.5405566 0.5175821 -1.0624289 0.2880410 0.7242846
TFDP2 69.2460704 0.0583857 0.4717344 0.1689462 0.8658389 0.9861531
TFEB 233.8897584 -0.0572086 0.2788284 -0.2092965 0.8342168 0.9839667
TFPI 52.7161081 -0.2708846 0.4858475 -1.2595091 0.2078465 0.6334051
TFR2 12.6636450 -0.1933781 0.4943538 -0.7439195 0.4569252 0.8551544
TGM2 76.4738718 0.8902995 0.4374828 2.0345766 0.0418935 0.3200484
TGS1 166.2262165 -0.4701292 0.3269243 -1.4690817 0.1418106 0.5426034
TH 82.9484863 -0.7073287 0.3934709 -1.7809794 0.0749158 0.4299714
THAP2 6.1931716 0.1058804 0.3481951 0.4969778 0.6192047 0.9368423
THRB 81.8771498 0.4541429 0.4453628 1.1267416 0.2598517 0.6963264
TICAM1 179.4661407 0.2415636 0.3460700 0.6695931 0.5031172 0.8714234
TIMM50 114.4074668 1.1371944 0.4123930 2.7675005 0.0056488 0.0973092
TIMM50bis 45.9979703 0.5047795 0.4642414 1.1201001 0.2626711 0.6967774
TIMP3 88.8745162 1.7302141 0.4375762 4.1457060 0.0000339 0.0020051
TLR2 11.8373424 -0.3295851 0.4193638 -1.9346837 0.0530291 0.3639027
TLR3 15.1936079 -0.3039645 0.4994517 -1.8290992 0.0673847 0.4226050
TLR6 3.4178723 0.0000000 0.2815954 0.0000000 1.0000000 1.0000000
TLR7 3.5349143 0.1212674 0.2567701 1.4443400 0.1486434 0.5442474
TLR8 0.3890392 0.0000000 0.2133403 0.0000000 1.0000000 NA
TMBIM1 326.5055208 0.0382528 0.2230914 0.1686676 0.8660581 0.9861531
TMEM102 60.3132894 0.1099186 0.4362792 0.2593325 0.7953787 0.9751363
TMEM120A 12.0941672 -0.1552494 0.4310754 -0.5064839 0.6125170 0.9357097
TMEM14A 106.3465415 -0.9796227 0.4528053 -2.3066157 0.0210763 0.2217691
TMEM161A 32.9944969 0.2693237 0.5195616 0.5063539 0.6126082 0.9357097
TMEM173 84.1070910 -0.5844480 0.4183146 -1.3728861 0.1697878 0.5750708
TNF 147.1388763 0.6501663 0.4272854 1.5564896 0.1195917 0.5225185
TNFAIP3 171.5841416 -0.0155148 0.2722311 -0.0083613 0.9933287 1.0000000
TNFAIP3bis 57.1060761 0.0050628 0.4585647 0.1871595 0.8515356 0.9839667
TNFAIP6 21.5278036 -0.0157785 0.4817096 -0.3160384 0.7519734 0.9644026
TNFAIP8L2 127.0246135 0.6046140 0.3086243 1.9422044 0.0521124 0.3628707
TNFRSF10A 11.1047060 0.2687862 0.4349322 0.4940598 0.6212639 0.9368423
TNFRSF10B 2.7787661 0.2398883 0.2722892 1.4987907 0.1339279 0.5415128
TNFRSF10C 206.6974376 -0.0863780 0.2729040 -0.3478208 0.7279748 0.9644026
TNFRSF1A 39.5728420 -0.1303687 0.5201844 -0.5674254 0.5704252 0.9037066
TNFRSF1B 28.8120947 0.1425367 0.5200471 0.2708524 0.7865046 0.9751363
TNFSF11 263.1255979 0.7022242 0.4737218 1.5585049 0.1191136 0.5225185
TNFSF11bis 275.5316136 0.6205073 0.3563013 1.7581940 0.0787145 0.4384861
TNFSF12 71.9666928 -0.0758881 0.4025717 -0.1865555 0.8520092 0.9839667
TNFSF4 78.3368406 -0.1964946 0.4412692 -0.4112996 0.6808529 0.9633554
TNIP1bis 31.1215559 0.7049379 0.4964599 1.5024639 0.1329773 0.5415128
TOB1 15.9138403 -0.0561414 0.4330895 0.6744364 0.5000339 0.8704634
TOX2 121.3716776 0.0140529 0.3747194 0.0512172 0.9591525 1.0000000
TP53 352.3689340 -0.0579382 0.2419333 -0.2369330 0.8127087 0.9779354
TP63 34.1557502 -0.4515350 0.5204853 -1.2341127 0.2171609 0.6393638
TPD52L1 361.9086747 0.6783881 0.2293599 2.9646512 0.0030303 0.0665843
TPPP 211.7160578 0.4266277 0.2902769 1.4808286 0.1386523 0.5424108
TPPPbis 310.6199518 0.2908291 0.2392829 1.2140327 0.2247352 0.6508382
TPT1 36.9577578 -0.0623567 0.5188928 -0.7559356 0.4496878 0.8466290
TPX2 22.0756350 0.9356383 0.5158471 2.0885950 0.0367442 0.2948877
TRAF2 49.8135143 -0.2566252 0.4819469 -0.4590334 0.6462102 0.9532477
TRAP1 15.8929055 0.7522573 0.5184501 1.4366687 0.1508121 0.5442474
TREM1 37.4943609 0.3717591 0.5180742 0.5198518 0.6031669 0.9290356
TREX1 107.4851401 -0.4225166 0.3563645 -1.1755058 0.2397924 0.6738380
TRIAP1 112.4647670 0.8013485 0.3733064 2.2204608 0.0263875 0.2532997
TRIB3 172.4263090 0.3330412 0.2715458 1.2291157 0.2190284 0.6401850
TRIM32 38.9465728 0.2730269 0.4978291 0.4286475 0.6681798 0.9627484
TRIM39 33.2528688 0.3191359 0.5104311 0.7307106 0.4649560 0.8571639
TRIM39bis 37.7886785 -0.3667068 0.4998120 -0.8702467 0.3841656 0.8048780
TRIM55 94.1114486 0.1271907 0.3916156 0.3152780 0.7525506 0.9644026
TRPS1 5.6006053 -0.0712667 0.3688268 -0.7239739 0.4690817 0.8571639
TRPV3 8.9498755 0.1488529 0.4246698 1.4076463 0.1592358 0.5543983
TSEN2 60.4186379 -0.2927348 0.5001431 -0.4834811 0.6287542 0.9436295
TTK 3.5935454 0.0000000 0.2233229 0.0000000 1.0000000 1.0000000
TUBA1A 0.1941014 0.0000000 0.2133403 0.0000000 1.0000000 NA
TXNDC12 91.8867324 -0.3267629 0.3928296 -0.8243293 0.4097525 0.8189469
TXNDC12bis 107.8284618 -1.1234097 0.4555112 -2.7673423 0.0056515 0.0973092
TXNDC15 56.0867254 1.5465515 0.5123185 3.8019758 0.0001435 0.0060496
TYROBP 95.8099571 -0.9199455 0.4171611 -2.3426950 0.0191450 0.2182650
UBE2K 62.2069779 -0.2089879 0.4485129 -0.5148460 0.6066606 0.9311307
UBE2S 66.3437813 -0.1086987 0.4029481 -0.2479097 0.8042043 0.9751363
UBE2Sbis 126.1249635 -0.0058986 0.3172994 -0.0112541 0.9910207 1.0000000
UGT1A1 1.0582093 0.0000000 0.2184588 0.0000000 1.0000000 1.0000000
USP18 54.4936153 0.1869858 0.4925223 0.2780442 0.7809784 0.9751363
USP28 3.7545447 0.2168538 0.2780319 1.2732916 0.2029146 0.6334051
USP47 68.9934869 -0.4420177 0.4731548 -0.7980140 0.4248624 0.8261697
VAV1 8.2115462 -0.1176303 0.4317535 -0.5778132 0.5633902 0.9011983
VCAM1 19.3471103 0.4251484 0.4971160 1.6666287 0.0955883 0.4714691
VDAC2 112.1341270 -0.2728212 0.4205877 -0.7328997 0.4636196 0.8571639
VPS35 18.6620373 0.4681240 0.5204071 0.9482348 0.3430099 0.7673978
VSIG4 0.8563493 0.0000000 0.2161928 0.0000000 1.0000000 NA
WDR83 211.7435016 -0.7117744 0.2666334 -2.6687455 0.0076135 0.1130987
WHSC2 73.0525385 0.2119980 0.4083582 0.5369616 0.5912941 0.9179599
WNT4 19.5446306 -0.3963822 0.5199083 -0.8750161 0.3815651 0.8012022
WNT5A 49.1927043 -0.3608607 0.4861983 -0.7821901 0.4341029 0.8338649
WWOX 104.6810017 -0.0688697 0.4374071 -0.2041332 0.8382494 0.9839667
XCL1 85.7978422 0.7759672 0.4792388 1.8776097 0.0604346 0.3919968
XPA 270.4913005 0.6385220 0.2816757 2.2715455 0.0231140 0.2405377
XRN2 34.1799873 -0.1784218 0.5016680 -0.3827652 0.7018938 0.9636086
YWHAB 1018.3884450 -0.0799253 0.2075855 -0.3787260 0.7048913 0.9636086
YWHABbis 299.7032891 0.1765391 0.2657069 0.6757803 0.4991801 0.8704634
YWHAG 177.2409245 -0.4160820 0.3029500 -1.3701542 0.1706388 0.5750708
YWHAQ 535.7223620 -0.2103982 0.2150588 -0.9834939 0.3253644 0.7474380
ZBP1 55.9033420 -0.6581128 0.4228158 -1.5817461 0.1137076 0.5182650
ZC3H12A 237.1077969 0.0740494 0.3224665 0.2675972 0.7890094 0.9751363
ZC3HC1 206.3299536 0.0856428 0.2546986 0.3365062 0.7364892 0.9644026
ZC3HC1bis 198.9937493 0.2391919 0.3278606 0.7143987 0.4749807 0.8571639
ZFYVE26 50.0252192 -0.2714692 0.4931395 -0.5017012 0.6158777 0.9368423
ZMYND11 24.9104728 0.2804687 0.5205027 0.3941151 0.6934961 0.9636086
ZNF281 17.0017165 -0.2965785 0.5164791 -1.1211621 0.2622189 0.6967774
ZSWIM2 9.0855900 -0.2674427 0.4161924 -0.6805811 0.4961366 0.8704634
ZYX 132.8259374 0.0676671 0.3326759 0.2388420 0.8112281 0.9773957

Tip 

Note that calling lfcShrink will not change the total number of genes that are identified as significantly differentially expressed with the default MLE, but it will help better discriminate the ranking of the significant genes.

Quoting the \(\bf{\texttt{DESeq2}}\) reference paper:

MAP estimates offers a bias-variance trade-off: for genes with little information for Log Fold Change (LFC) estimation, a reduction of the strong variance is bought at the cost of accepting a bias toward zero, and this can result in an overall reduction in mean squared error. Genes with high information for LFC estimation will have estimates with both low bias and low variance.

The reduction in mean squared error can be observed for the gene \(\text{ABO}\) by comparing the lfcSE values between the two estimations: MLE and MAP. In both cases, a DataFrame object is returned with the following column attributes:

  • baseMean: the gene-wise average of the normalized count values (that is divided by the size factors),
  • log2FoldChange: the log2 fold change estimate,
  • lfcSE: the standard error of the the log2 fold change estimate,
  • stat: the Wald statistic used for the Wald test, obtained by dividing log2FoldChange by its error lfcSE,
  • pvalue: the Wald test p-value,
  • padj: the Wald test p-value adjusted for multiple testing.

Wald test

To test if there is evidence of a differential expression across sample groups, a Wald statistic is built using a contrast of coefficients. The contrast variable and the corresponding Wald statistic read,

\[\beta_i^c = \bm{c}^T \bm{\beta_i} \ , \quad W_i = \dfrac{\beta_i^c}{\sqrt{\bm{c}^T \Sigma_i \bm{c}}} \ ,\]

where \(\Sigma_i\) denotes the covariance matrix of the coefficients \(\bm{\beta_i}\). In the case of a two-contrast level, that is when comparing across two sample groups, the Wald statistic formula reduces to,

\[W_i = \dfrac{\beta_{i,1} - \beta_{i,2}}{\sqrt{\mathbb{Var}[\beta_{i,1} - \beta_{i,2}]}} \ .\]

Under the null hypothesis where there is no differential expression, the Wald statistic should follow a standard normal distribution \(\mathcal{N}(0,1)\). By computing the empirical p-value associated to the Wald statistic and comparing its value to the theoretical quantile of the standard normal distribution, one can decide, for each gene, whether or not the null hypothesis is violated. If the empirical p-value is smaller than the theoretical quantile, the null hypothesis is rejected and there is evidence that the gene is differentially expressed.

Given the large number of genes, the previous computed empirical p-values need to be corrected for multiple testing. To emphasize the importance of this correction, consider an example where a total of \(20 \ 000\) genes are tested for differential expression. Choosing a \(5\%\) cut-off for the empirical p-value means that there is a \(5\%\) chance that the result is a false positive. In total, up to \(1000\) genes are expected to be erroneously marked as differentially expressed. After testing, if \(3000\) were found to be differentially expressed, up to \(33\%\) of them would be false positives.

\(\bf{\texttt{DESeq2}}\) uses the Benjamini-Hochberg (BH) procedure(Benjamini and Hochberg, 1995) to handle the multiple testing problem. This procedure introduces a statistic called False Discovery Rate (FDR) to control, through an upper-bound, the number of false positives for each test. Coming back to the previous example, if \(3000\) genes were found to be differentially expressed with an FDR of \(5\%\), only \(150\) of them would be expected to be false positives.


Prior to performing the multiple testing adjustment, \(\bf{\texttt{DESeq2}}\) applies, in order, two filtering operations:

  • an automatic outlier detection, based on the Cook’s distance, to remove from the analysis genes that possess isolated instances of very large counts (for instance a gene with single-digit counts for all samples, except one sample with a count in the thousands),

  • an automatic independent filtering, which aim at improving the power of the multiple testing adjustment by the BH procedure.

These two operations are automatically applied when calling the results function. They are briefly reviewed hereafter.

Outliers detection

The outlier detection relies on a statistic called Cook’s distance(Cook, 1977) that measures how much a single sample \(j\) is influencing the log2 fold change coefficients \(\hat{\bm{\beta_i}}\) for a gene \(i\). The method operates differently with regard to the number of replicates per sample:

  • if a gene has a Cook’s distance above a threshold for a sample that has \(3\) or more replicates, it is flagged as an outlier. This flagging can be turned-off with the argument cooksCutoff:

      results(dds, cooksCutoff=FALSE)

  • if there are \(7\) or more replicates for a given sample, outliers will be replaced with the trimmed mean over all other samples, scaled up by the size factor for that sample. This replacement can also be turned-off by setting the argument minReplicatesForReplace of DESeq to infinity2:

      DESeq(dds, minReplicatesForReplace=Inf)

Given the GLM model introduced in Section 4.1, the Cook’s distance for gene \(i\) and sample \(j\) reads,

\[D_{i,j} = \dfrac{R_{ij}^2}{p}\dfrac{h_{ij}}{(1-h_{ij})^2} \ ,\]

where \(p\) is the number of GLM’s parameters (including the intercept), \(R_{ij}\) is the Pearson residual of sample \(j\),

\[R_{ij} = \dfrac{K_{ij} - \mathbb{E}[K_{i,j}\vert\bm{X}_j]}{\mathbb{Var}[K_{i,j}\vert\bm{X}_j]} \ ,\]

and \(h_{ij}\) is called the leverage of sample \(j\) which measure how far away the number of counts for sample \(j\) is from the other count values. Count values with high leverages can severely shift the estimation of log2 fold changes.

The Cook distance values can be inspected with the assays function of \(\bf{\texttt{DESeq2}}\) which returns a list of \(4\) attributes:

  • counts which contains the count matrix (as a DataFrame),

  • mu which contains the \(\mu_{i,j}\) values (see Section 4.1),

  • H, the so-called hat matrix containing the leverages \(h_{ij}\),

  • cooks containing values of the Cook’s distance.

Boxplots of the Cook’s distances can be plotted to see if one sample is consistently higher than others. For the present case study, all the sample contain almost no outliers:

# retrieve the cooks attribute and format it
cooks <- stack(as.data.frame(assays(dds)[["cooks"]]))
# compute manually the default Cook's threshold
Fparams <- c(ncol(attr(dds, "dispModelMatrix")),
             nrow(attr(dds, "dispModelMatrix")))
# draw boxplots of the Cook's distance per sample
ggplot(cooks, aes(x=ind, y=values)) + geom_boxplot() +
  geom_hline(yintercept=qf(0.99, Fparams[1], Fparams[2]-Fparams[1]),
             color="purple", linetype="dashed") +
  scale_y_log10() + 
  labs(x="samples", y=TeX(paste0("Cook's distance: ", r"($D_{ij}$)"))) +
  theme_custom() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) # vertical labels

**Boxplots of the Cook's distance per sample.** The purple dashed horizontal line marks the threshold.

Boxplots of the Cook’s distance per sample. The purple dashed horizontal line marks the threshold.

Independent filtering

For weakly expressed genes, there is little to no chance to observe a differential expression between two samples simply due to small read counts. While the Wald test will most certainly mark these genes as non-significant, they might impact the multiple testing adjustment. It is therefore relevant to filter out low counts genes before applying the BH procedure. If needed, the independent filtering can be turned-off through the argument independentFiltering of the results function:

results(dds, independentFiltering=FALSE)

The filter statistic used to perform independent filtering is the mean of normalized counts. The filter statistic and a set of its associated empirical quantiles are then used by the BH procedure to select a filtering threshold. This threshold is selected to maximize the number of genes found at a user-specified target FDR. Once set, all genes with a mean normalized counts lower than the threshold will be omitted (not tested).

Quantities used to compute the filtering threshold are stored as attributes of the object returned by the call to results and can be accessed via the metadata function:

  • alpha is the user-specified target FDR,

      metadata(res)$alpha

  • filterTreshold is the threshold value resulting from the whole procedure,

      metadata(res)$filterThreshold

  • filterNumRej is the vector containing the numbers of rejections (or equivalently the number of genes that pass the Wald test) at each quantile value of the filter statistic.

      metadata(res)$filterNumRej

  • filterTheta is the lowest quantile of the filter associated to the selected threshold value.

      metadata(res)$filterTheta

Examples

As an illustration, let us find the genes that are differential expressed due to the drug condition Drug3 compared to the control condition Ctrl:

contrast <- c("condition", "Drug3", "Ctrl")
resDrug3 <- results(dds, contrast=contrast)

The results table can be sorted according to the increasing order the adjusted p-values:

resDrug3 %>% data.frame() %>% dplyr::arrange(., padj)

The \(\bf{\texttt{DESeq2}}\) package provides a useful function, called summary, that gives a brief overview of the results table:

summary(resDrug3)

## 
## out of 966 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 134, 14%
## LFC < 0 (down)     : 85, 8.8%
## outliers [1]       : 0, 0%
## low counts [2]     : 75, 7.8%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results

The function reports:

  • the total number of genes that were tested, i.e. genes with a non-zero total reads count (although here, genes with a total reads count of zero were already handled by the pre-filtering step),

  • the number of genes that were found to be either positively (LFC>0) or negatively (LFC<0) differentially expressed,

  • the number of genes flagged as outliers by the Cook’s distance,

  • the number of genes that were omitted from the test by the independent filtering.

There are two options left at the user’s discretion to be more strict about the selection of significant genes:

  • either lower the false discovery rate alpha:

      resDrug3_05 <- results(dds, contrast=contrast, alpha=0.05)
  summary(resDrug3_05)

      ## 
  ## out of 966 with nonzero total read count
  ## adjusted p-value < 0.05
  ## LFC > 0 (up)       : 90, 9.3%
  ## LFC < 0 (down)     : 60, 6.2%
  ## outliers [1]       : 0, 0%
  ## low counts [2]     : 75, 7.8%
  ## (mean count < 3)
  ## [1] see 'cooksCutoff' argument of ?results
  ## [2] see 'independentFiltering' argument of ?results

  • or raise the log2 fold change threshold lfcThreshold:

      resDrug3_lfc1 <- results(dds, contrast=contrast, lfcThreshold=1)
  summary(resDrug3_lfc1)

      ## 
  ## out of 966 with nonzero total read count
  ## adjusted p-value < 0.1
  ## LFC > 1.00 (up)    : 9, 0.93%
  ## LFC < -1.00 (down) : 5, 0.52%
  ## outliers [1]       : 0, 0%
  ## low counts [2]     : 19, 2%
  ## (mean count < 1)
  ## [1] see 'cooksCutoff' argument of ?results
  ## [2] see 'independentFiltering' argument of ?results

    here genes are found significant only if their counts are more than twice higher or more than twice smaller (because \(\log_2(2)=1\)).

  • or both.

The results function also provides an argument altHypothesis to specify a different kind of test:

  • altHypothesis="greaterAbs" - \(\lvert\beta>\text{lfc}\rvert\) is the default test to identify the genes that are differentially expressed regardless of the sign, \(\text{lfc}\) corresponds to the log2 fold change threshold argument lfcThreshold.

  • altHypothesis="greater" or altHypothesis="less" is the one-sided version of the previous test. It can be used to further constrain the research of significant genes to either only positively (altHypothesis="greater") or negatively (altHypothesis="less") differentially expressed genes,

      resDrug3_05_less <- results(dds, contrast=contrast, alpha=0.05, altHypothesis="less")

  • altHypothesis="lessAbs" - \(\lvert\beta<\text{lfc}\rvert\) can be used to find genes that are not, or only very weakly, affected by the experimental condition. A positive value is required for the log2 fold change threshold argument lfcThreshold. Only MLE estimates can be obtained with this test (e.g. the lfcShrink function cannot be used),

      resDrug3_05_lessAbs <- results(dds, contrast=contrast, alpha=0.05, lfcThreshold=1,
                                 altHypothesis="lessAbs")

Tip 

There are three main instances which results in a gene's p-value being set to NA:

  • if a gene has zero counts across all samples (in this case all the fields are set to NA),
  • if an outlier is detected by the Cook's distance,
  • if the gene is flagged by the independent filtering.

Volcano plots

Volcano plots offer a useful way to visualize the table produced by the results function. The function EnhancedVolcano of the \(\bf{\texttt{EnhancedVolcano}}\) package can be used to produce such graphs. Here, a custom function volcanoPlot (loaded from volcanoPlot.R) is used that adapts the graph given the alternative hypothesis. The following arguments are expected:

  • toptable: the results table produced by the results function,
  • pCutoff: the adjusted p-value cut-off for statistical significance. This should be the same value as the parameter alpha given to the results function (default to \(0.1\)),
  • FCcutoff: the cut-off for the log2 fold-change (default to \(0\)),
  • altHypothesis: the alternative hypothesis (defaults to “greaterAbs”).

The volcano plot for the results table ressDrug3_05_less can be drawn with the lines:

# draw the Volcano plot
if (use_bokeh) { # interactive version through bokeh
  volcanoPlot(df=as.data.frame(resDrug3_05_less), pCutoff=resDrug3_05_less@metadata$alpha,
  FCcutoff=1, altHypothesis="less", filename="resDrug3_05_less1",
  title=resDrug3_05_less@elementMetadata@listData$description[2], render=FALSE)
  graph <- htmltools::includeHTML("resDrug3_05_less1.html")
  print(graph, browse = TRUE)
} else { # static version through ggplot2
  graph <- volcanoPlot(
  toptable=resDrug3_05_less, pCutoff=resDrug3_05_less@metadata$alpha,
  FCcutoff=1, altHypothesis="less")
graph + theme_custom() +
  theme(legend.position = "top", legend.title = element_blank())
}
Bokeh Plot

Note that here the log2 fold-change cut-off line was specified a posteriori in that its value was not used during the Wald test (lfcThreshold=0). The green points are genes that both pass the adjusted p-value and log2 fold change cut-offs. Points in orange are genes that pass the adjusted p-value cut-off but fail the log2 fold change cut-off, points in red are genes that do the opposite. Black points are genes that fail both cut-offs.

The following graph highlights how strict the Wald test becomes when both the parameters alpha and lfcThreshold are passed to the results function.

# stricter test
resDrug3_05_less <- results(dds, contrast=contrast, alpha=0.05, lfcThreshold=1, altHypothesis="less")
# draw the Volcano plot
if (use_bokeh) { # interactive version through bokeh
  volcanoPlot(df=as.data.frame(resDrug3_05_less), pCutoff=resDrug3_05_less@metadata$alpha,
  FCcutoff=1, altHypothesis="less", filename="resDrug3_05_less2",
  title=resDrug3_05_less@elementMetadata@listData$description[2], render=FALSE)
  graph <- htmltools::includeHTML("resDrug3_05_less2.html")
  print(graph, browse = TRUE)
} else { # static version through ggplot2
  graph <- volcanoPlot(
    toptable=resDrug3_05_less, pCutoff=resDrug3_05_less@metadata$alpha,
    FCcutoff=resDrug3_05_less@metadata$lfcThreshold, altHypothesis="less")
  graph + theme_custom() +
    theme(legend.position = "top", legend.title = element_blank())
}
Bokeh Plot

On can remark that genes \(\text{IL15}\), \(\text{MAPKAPK2}\) and \(\text{TMEM102}\) are found significant only when the parameter lfcThreshold=1 is explicitly passed to the results function. That is because in the default case, when lfcThreshold=0, those genes are flagged by the independent filtering.
As a final note, the Bioconductor package ReportingTools provides functionality to generate html document from the output of the results function. More can be found out here.

  1. The variance (\(\sigma^2\)) quantify the relative importance of each factor, but there is no straightforward way to quantify the contributions of each component on an absolute scale. ↩

  2. The actual computation (and so the outliers replacement) of the Cook’s distance takes place when calling DESeq. ↩

References

  1. Cook, R.D., 1977. Detection of Influential Observation in Linear Regression. Technometrics 19. 10.1080/00401706.1977.10489493
  2. Benjamini, Y., Hochberg, Y., 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological) 57. 10.1111/j.2517-6161.1995.tb02031.x
  3. Jolliffe, I.T., 2002. Principal Component Analysis. Springer-Verlag. 10.1007/b98835
  4. van der Maaten, L., Hinton, G., 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9. http://jmlr.org/papers/v9/vandermaaten08a.html
  5. Di, Y., Schafer, D., Cumbie, J., Chang, J., 2011. The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq. Statistical Applications in Genetics and Molecular Biology 10. 10.2202/1544-6115.1637
  6. Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., 2013. Bayesian Data Analysis (3rd ed.). Chapman and Hall/CRC. 10.1201/b16018
  7. Love, M.I., Huber, W., Anders, S., 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15. 10.1186/s13059-014-0550-8
  8. Dunn, P.K., Smyth, G.K., 2018. Generalized Linear Models With Examples in R. Springer New York. 10.1007/978-1-4419-0118-7
  9. McInnes, L., Healy, J., Melville, J., 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. 10.48550/ARXIV.1802.03426
  10. Townes, F.W., Hicks, S.C., Aryee, M.J., Irizarry, R.A., 2019. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biology 20. 10.1186/s13059-019-1861-6