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Commit fe5789a9 authored by Matteo Barcella's avatar Matteo Barcella
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BulkRNAseq/00-Interaction.R 0 → 100644
View file @ fe5789a9
# Interaction analysis
library(DESeq2)
library(ggplot2)
library(openxlsx)
library(pheatmap)
library(RColorBrewer)
library(clusterProfiler)
library(DOSE)
library(AnnotationHub)
library(org.Hs.eg.db)
out_dir <- "../00-Interaction/"
read.counts <- read.table(file = "../04-counts/featureCounts_results_corrected.txt", header = T)
rownames(read.counts) <- read.counts$Geneid
read.counts <- read.counts[,c(7:ncol(read.counts))]
colnames(read.counts) <- unlist(strsplit(x = colnames(read.counts),split = "\\.", ))[seq.int(2, 42, by = 3)]
sample_info <- read.table(file = "SampleInfo.txt", header = T)
sample_info$OFP <- substr(x = sample_info$Group, start = 1, stop = 4)
sample_info$Treatment <- substr(x = sample_info$Group, start = 5, stop = length(sample_info$Group))
rownames(sample_info) <- sample_info$SampleID
dds <- DESeqDataSetFromMatrix(countData = read.counts,
colData = sample_info,
design = as.formula("~ OFP + Treatment + OFP:Treatment"))
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
dds$OFP <- relevel(dds$OFP, ref = "OFPn")
dds$Treatment <- relevel(dds$Treatment, ref = "NT")
dds <- DESeq(dds)
sizeFactors(dds)
resultsNames(dds)
rlog <- rlog(dds)
for (contr in c("OFPOFPp.TreatmentT")) {
res <- results(dds, name = contr)
res <- res[!is.na(res$padj), ]
res$padj <- as.numeric(res$padj)
res <- res[res$padj < 0.05 & res$baseMean > 5 & abs(res$log2FoldChange) > 1, ]
write.table(x = as.data.frame(res), file = paste(out_dir, paste0("DESeq2_", contr, "_interaction.tsv"), sep = "/"),
sep = "\t", quote = FALSE, col.names = NA)
res$Gene <- rownames(res)
write.xlsx(x = res, file = paste(out_dir, paste0("DESeq2_", contr, "_interaction.xlsx"), sep = "/"))
}
sample_info$SampleID <- NULL
pheatmap(mat = as.matrix(assay(rlog)[res[res$log2FoldChange > 0,]$Gene,]),
color = rev(brewer.pal(9, "RdBu")),
scale = "row", annotation_col = sample_info,
show_rownames = F, cluster_cols = F)
saveRDS(object = list(dds = dds,
read.counts = read.counts,
res = res,
rlog = rlog,
sample_info = sample_info), file = "00-Interaction.rds")
### Performing downstream analysis
interaction_genes <- list()
interaction_genes[["up"]] <- subset(res, log2FoldChange > 0)
interaction_genes[["down"]] <- subset(res, log2FoldChange < 0)
gene_list <- list()
gene_list[["up"]] <- interaction_genes$up$log2FoldChange
names(gene_list[["up"]]) <- interaction_genes$up$Gene
gene_list[["down"]] <- interaction_genes$down$log2FoldChange
names(gene_list[["down"]]) <- interaction_genes$down$Gene
contr.enrich_go_up <- enrichGO(gene = names(gene_list[["up"]]),
universe = rownames(dds),
OrgDb = 'org.Hs.eg.db',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
keyType = 'SYMBOL')
contr.enrich_go_down <- enrichGO(gene = names(gene_list[["down"]]),
universe = rownames(dds),
OrgDb = 'org.Hs.eg.db',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
keyType = 'SYMBOL')
contr.enrich_go <- list(UP = contr.enrich_go_up@result,
DOWN = contr.enrich_go_down@result)
write.xlsx(contr.enrich_go,file = "GO_BP_Enrichment_interaction_OFP_Treatment.xlsx",
asTable = T, firstRow = T, colWidths = "auto")
# GSEA
for (contr in c("OFPOFPp.TreatmentT")) {
res_all <- results(dds, name = contr)
res_all <- res_all[!is.na(res_all$padj), ]
res_all$padj <- as.numeric(res_all$padj)
# res_all <- res[res$padj < 0.05 & res$baseMean > 5 & abs(res$log2FoldChange) > 1, ]
write.table(x = as.data.frame(res_all), file = paste(out_dir, paste0("DESeq2_", contr, "_interaction_full.tsv"), sep = "/"),
sep = "\t", quote = FALSE, col.names = NA)
res_all$Gene <- rownames(res_all)
write.xlsx(x = res_all, file = paste(out_dir, paste0("DESeq2_", contr, "_interaction_full.xlsx"), sep = "/"))
}
gene_list[["full"]] <- res_all$log2FoldChange
names(gene_list[["full"]]) <- res_all$Gene
gene_list[["full"]] <- sort(gene_list[["full"]], decreasing = T)
t2gene <- read.gmt(gmtfile = paste0("/opt/common/tools/ric.tiget/GSEA/GSEA_human_v7.4/h.all.v7.4.symbols.gmt"))
gsea_res <- GSEA(gene_list[["full"]], TERM2GENE = t2gene,
verbose = FALSE,minGSSize = 10,maxGSSize = 1000,
pvalueCutoff = 0.05)
gseaplot2(gsea_res, geneSetID = 1:3, pvalue_table = TRUE,
color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot")
a <- ridgeplot(gsea_res, fill = "NES", showCategory = 50) + scale_fill_gradient2()
a$theme$axis.text.y$size <- 10
a$theme$axis.text.x$size <- 10
png(filename = "GSEA_ridges_enrich_core_distribution.png",width = 9, height = 6, units = "in", res = 200)
a
dev.off()
BulkRNAseq/GGRidgesPaperPanel.R 0 → 100644
View file @ fe5789a9
# GGridges
library(DESeq2)
library(ggplot2)
library(openxlsx)
library(pheatmap)
library(RColorBrewer)
library(clusterProfiler)
library(DOSE)
library(AnnotationHub)
library(org.Hs.eg.db)
library(gridExtra)
load(file = "../.RData")
gsea_res_interaction <- gsea_res
gsea_bulk <- readRDS(file = "/beegfs/scratch/ric.gentner/ric.gentner/BALL/BulkRNA_BALL_Integration/results/GSEA_All_Experiments/GG_ridges/Hallmark_GSEA.rds")
gsea_res <- gsea_bulk
a <- ridgeplot(gsea_res$GFP_L_vs_GFP_H, fill = "NES",
showCategory = nrow(gsea_res$GFP_L_vs_GFP_H@result[gsea_res$GFP_L_vs_GFP_H@result$p.adjust < 0.2,])) +
scale_fill_gradient2(low = "#0066E7", high= "#D4070F") +
ggtitle("miR-126 High vs miR-126 Low") + #, subtitle = "Human dataset") +
xlab("logFC") +
theme(legend.position = "none", plot.margin=unit(c(0.5,0.5,0.5,0.5), 'cm'))
a$theme$axis.text.y$size <- 8
a$theme$axis.text.x$size <- 8
a$theme$axis.title.x$size <- 10
a_2 <- a
a_2$data$category <- gsub(a$data$category, pattern = "HALLMARK_", replacement = "")
a_2$data$category <- factor(x = a_2$data$category,
levels = gsub(x = levels(a$data$category), replacement = "", pattern = "HALLMARK_"))
b <- ridgeplot(gsea_res$data_OE_vs_SM, fill = "NES",
showCategory = nrow(gsea_res$data_OE_vs_SM@result[gsea_res$data_OE_vs_SM@result$p.adjust < 0.2,])) +
scale_fill_gradient2(low = "#0066E7", high= "#D4070F") +
ggtitle("miR-126 OE vs miR-126 SM") + #, subtitle = "Murine dataset") +
xlab("logFC") +
theme(legend.position = "none", plot.margin=unit(c(0.5,0.5,0.5,0.5), 'cm'))
b$theme$axis.text.y$size <- 8
b$theme$axis.text.x$size <- 8
b$theme$axis.title.x$size <- 10
b_2 <- b
b_2$data$category <- gsub(b$data$category, pattern = "HALLMARK_", replacement = "")
b_2$data$category <- factor(x = b_2$data$category,
levels = gsub(x = levels(b$data$category), replacement = "", pattern = "HALLMARK_"))
c <- ridgeplot(gsea_res_interaction, fill = "NES",
showCategory = nrow(gsea_res_interaction@result[gsea_res_interaction@result$p.adjust < 0.2,])) +
scale_fill_gradient2(low = "#0066E7", high= "#D4070F") +
ggtitle("Interaction miR-126 lvl - Treatment") +
xlab("logFC") +
theme(legend.position = "none", plot.margin=unit(c(0.5,0.5,0.5,0.5), 'cm'))
c$theme$axis.text.y$size <- 8
c$theme$axis.text.x$size <- 8
c$theme$axis.title.x$size <- 10
c_2 <- c
c_2$data$category <- gsub(c$data$category, pattern = "HALLMARK_", replacement = "")
c_2$data$category <- factor(x = c_2$data$category,
levels = gsub(x = levels(c$data$category), replacement = "", pattern = "HALLMARK_"))
# Panel
png(filename = "Panel_gsea_hallmakrs_nes_distribution.png", width = 18, height = 6, units = "in", res = 300)
grid.arrange(a_2,b_2,c_2,widths = c(0.30,0.30,0.35), ncol = 3)
dev.off()
svg(filename = "Panel_gsea_hallmakrs_nes_distribution.svg", width = 18, height = 6)
grid.arrange(a_2,b_2,c_2,widths = c(0.30,0.30,0.35), ncol = 3)
dev.off()
BulkRNAseq/README.md 0 → 100644
View file @ fe5789a9
## Introduction.
BulkRNAseq analysis was performed both for single-diseases separately and the by combining all samples in an integrated analysis (GFP_L vs GFP_H).
For the latter, the starting point of the analysis is the genes expression counts matrix deposited at [GSE236138](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE236138).
Moreover, for the second dataset present at GEO id [GSE236141](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE236141), we performed an additional analysis focus on interaction analysis between condition and treatment.
## Workflow and steps.
Below the most important steps:
1. Quality control by [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
2. Trimming of bad quality reads with [TrimGalore](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)<details><summary>Running command</summary>trim_galore --quality 20 --fastqc --length 25 --output_dir {outdir} --paired {input.r1} {inout.r2}</details>
3. Alignment with [STAR](https://github.com/alexdobin/STAR)
<details><summary>Running command</summary>
"STAR " +
"--runThreadN {threads} " +
"--genomeDir {input.genome} " +
"--readFilesIn {params.trim_seq} " +
"--outSAMstrandField intronMotif " +
"--outFileNamePrefix {params.aln_seq_prefix} " +
"--outSAMtype BAM SortedByCoordinate " +
"--outSAMmultNmax 1 " +
"--outFilterMismatchNmax 10 " +
"--outReadsUnmapped Fastx " +
"--readFilesCommand zcat "
</details>
4. Gene expression quantification with [FeatureCounts](https://academic.oup.com/bioinformatics/article/30/7/923/232889)
<details><summary>Running command</summary>
"featureCounts " +
"-a {input.annot} " +
"-o {output.fcount} " +
"-g gene_name " +
"-p -B -C " +
"-s {params.strand} " +
"--minOverlap 10 " +
"-T {threads} " +
"{input.bams} "
</details>
5. Differential Expression analysis with [Deseq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html).
For Differential Gene Expression analysis we followed the standard workflow provided by package.
<details><summary>Detail</summary>
results(DESeq.ds, pAdjustMethod = "BH", independentFiltering = TRUE, contrast = c("groups", Group1, Group2), alpha = 0.05)
</details>
For interaction analysis we apply the design according to Deseq2 vignette:
<details><summary>Interaction</summary>
design = as.formula("~ Condition + Treatment + Condition:Treatment")
</details>
6. Dowstream functional Analysis with [ClusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html).
In order to retrieve functional annotation from DE analysis, we performed **O**ver **R**epresentation **A**nalysis and **G**ene **S**et **E**nrichment **A**nalysis by using the functions EnrichGO and GSEA provided by the package.
BulkRNAseq/config_integration.yaml 0 → 100644
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resources:
directories:
root: /XXX/XXX/XXX/XXXX/BulkRNA_BALL_Integration
conda_dir: . # if not required set to "."
reference: reference
data: input-fastq
progs: progs
results: results
qual_check: 01-qcheck
trimming: 02-trim
alignments: 03-aln
counts: 04-counts_bypatient
dge: 05-DGE_bypatient_0.1
post: 06-post-analysis_bypatient_0.1
cluster: 07-cluster-analysis_bypatient_0.1
organism: human # choices: human mouse ...
genome: GRCh38.primary_assembly.genome.fa #
genome_annot: gencode.v34.primary_assembly.gtf # skipped - counts already present
design: patient,condition # cov1,cov2,...,condition
seq_type: single-end # choices: single-end paired-end # skipped - counts already present
strand_type: unstranded # choices: unstranded stranded reverse # skipped - counts already present
fdr_thr: 0.1
logfc_thr: 0
rand_walk: 1 # choices: 0 (do not print random walks) 1 (print random walks)
post_analysis_tool: DESeq # choices: edgeR DESeq limma
fastqfiles:
- PT10_395C0H: C0_HIGH_S13
- PT10_395C0L: C0_LOW_S14
- PT10_395C1H: C1_HIGH_S9
- PT10_395C1L: C1_LOW_S10
- PT10_395C3H: C3_HIGH_S11
- PT10_395C3L: C3_LOW_S12
- PT12_27C3R3L: C3_R3_S1
- PT12_27C3R4H: C3_R4_S2
- PT12_27D1R3L: D1_R3_S7
- PT12_27D1R4H: D1_R4_S8
- PT12_27D2R3L: D2_R3_S9
- PT12_27D2R4H: D2_R4_S10
- PT13_37C0R4H: 1_S1_L001,1_S1_L002
- PT13_37C0R5L: 2_S2_L001,2_S2_L002
- PT13_37C1R4H: 3_S3_L001,3_S3_L002
- PT13_37C1R5L: 4_S4_L001,4_S4_L002
#NON USARE TRATTINI
samples:
- PT10_395C0H: PT10_395,GFP_HIGH
- PT10_395C0L: PT10_395,GFP_LOW
- PT10_395C1H: PT10_395,GFP_HIGH
- PT10_395C1L: PT10_395,GFP_LOW
- PT10_395C3H: PT10_395,GFP_HIGH
- PT10_395C3L: PT10_395,GFP_LOW
- PT12_27C3R3L: PT12_27,GFP_LOW
- PT12_27C3R4H: PT12_27,GFP_HIGH
- PT12_27D1R3L: PT12_27,GFP_LOW
- PT12_27D1R4H: PT12_27,GFP_HIGH
- PT12_27D2R3L: PT12_27,GFP_LOW
- PT12_27D2R4H: PT12_27,GFP_HIGH
- PT13_37C0R4H: PT13_37,GFP_HIGH
- PT13_37C0R5L: PT13_37,GFP_LOW
- PT13_37C1R4H: PT13_37,GFP_HIGH
- PT13_37C1R5L: PT13_37,GFP_LOW
contrasts:
- GFP_LOW:GFP_HIGH
enrichergmtfiles:
- c1.all.v7.0.symbols.gmt
- c2.all.v7.0.symbols.gmt
- c3.all.v7.0.symbols.gmt
- c4.all.v7.0.symbols.gmt
- c5.all.v7.0.symbols.gmt
- c6.all.v7.0.symbols.gmt
- c7.all.v7.0.symbols.gmt
- h.all.v7.0.symbols.gmt
read1_mask: "_R1_001.fastq.gz"
read2_mask: "_R2_001.fastq.gz"
read_mask: "_R1_001.fastq.gz"
Methylation/MethylAnalysis_Adults_samples.R 0 → 100644
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MethylAnalysis <- function(methcall.folder = NULL, # input folder with .cov files (bismark)
outfolder = NULL, # output folder (if not exist it will be created)
proj.id = "myproject", # project id to prefix in putput files
samplesheet = NULL,
sheetnum = 1,
design.var = "Disease", # variable to subset samplesheet
case.var = "Case", # 1 in treatment vector
control.var = "Control", # 0 in treatment vector
idcol = "SampleID",
mincoverage = 10,
lowperc = 5,
lowcount = 10,
assem="hg38",
do.subset = T,
chr.subset = "chr9",
start.subset = 136668000,
end.subset = 136671000,
pipeline.meth = "bismarkCoverage",
plot.covariates = c("Condition","Batch","MRD",
"Torelapse","Chemorefractory",
"Sex","CytoA","Tissue"),
idstoremove = NULL,
delta.meth = 10,
plot.categorical.vars = c("Condition","Batch","MRD","Torelapse","Chemorefractory","Sex","CytoA"),
plot.continuous.vars = c("miR-126"),
plot.height = 9,
plot.width = 12,
cellw = 15,
cellh = 15,
fontnumsize = 5,
fontsize = 8
){
# load libraries
require(methylKit)
require(ggplot2)
require(reshape2)
require(plyr)
require(ggrepel)
library(GenomicRanges)
library(openxlsx)
library(pheatmap)
# check vars
if(is.null(methcall.folder)){
stop("Please set methcall.folder")
}
if(is.null(outfolder)){
stop("Please set outfolder")
}
if(is.null(samplesheet)){
stop("Please set samplesheet")
}
# Setup variables
infolder = paste0(methcall.folder, "/")
dir.create(infolder, showWarnings = F)
outfolder = paste0(outfolder, "/")
dir.create(outfolder, showWarnings = F)
qcfolder <- paste0(outfolder, "QC/")
dir.create(qcfolder, showWarnings = F)
# reading sample sheet with metadata
ssheet <- read.xlsx(samplesheet, sheet = sheetnum)
if(!is.null(idstoremove)){
ssheet <- subset.data.frame(x = ssheet, subset = !ssheet[[idcol]] %in% idstoremove)
}
# defining design vector according to variable
ssheet <- subset.data.frame(x = ssheet, subset = ssheet[[design.var]] %in% c(case.var, control.var))
ssheet$mir126 <- as.numeric(ssheet$mir126)
d.vec <- ssheet[[design.var]]
d.vector <- ifelse(d.vec == case.var, 1, 0)
# initialzing coverage .cov files list
covs <- list()
sampleids <- as.list(ssheet[[idcol]])
names(sampleids) <- ssheet[[idcol]]
for (i in ssheet[[idcol]]) {
covs[[i]] <- paste0(infolder, "/", i, ".bismark.cov")
}
saveRDS(object = covs, file = paste0(outfolder,"covs_object.rds"))
# creating object
myobj <- methRead(location = covs,
sample.id = sampleids,
assembly = assem,
pipeline = pipeline.meth,
treatment = d.vector,
context="CpG",
mincov = mincoverage)
saveRDS(myobj, file = paste0(outfolder,"Myobject.rds"))
names(myobj) <- ssheet[[idcol]]
saveRDS(myobj, file = paste0(outfolder,"Myobject_with_names.rds"))
# subsetting if declared
if(isTRUE(do.subset)){
my.win = GRanges(seqnames = chr.subset, ranges = IRanges(start = start.subset, end = end.subset))
myobj <- selectByOverlap(myobj,my.win)
}
saveRDS(myobj, file = paste0(outfolder,"Myobject_with_names_aftersubset.rds"))
# filtering on minimum coverage
myobj <- filterByCoverage(methylObj = myobj, lo.count=lowcount, lo.perc = lowperc)
# Normalization
myobj <- normalizeCoverage(obj = myobj)
# saveRDS(object = myobj, file = "Initial_object.rds")
# Calculate basic stats and PCs
metricsfolder <- paste0(qcfolder, "Metrics/")
dir.create(path = metricsfolder, showWarnings = F)
for (id in ssheet[[idcol]]) {
png(filename = paste0(metricsfolder,proj.id,"_CpG_pct_methylation_sample_", id, ".png"),
width = 9, height = 6, units = "in", res = 96)
print(getMethylationStats(myobj[[id]],plot=TRUE,both.strands=FALSE))
dev.off()
png(filename = paste0(metricsfolder, proj.id,"_Coverage_stats_sample_", id, ".png"),
width = 9, height = 6, units = "in", res = 96)
print(getCoverageStats(myobj[[id]],plot=TRUE,both.strands=FALSE))
dev.off()
}
# create meth obj
#meth <- unite(object = myobj, destrand=FALSE)
meth <- unite(object = myobj, destrand=FALSE) # for debugging
saveRDS(meth, paste0(outfolder,"savemeth.tmp.rds"))
# Perform correlation
sink(paste0(qcfolder, proj.id, "_Correlations.txt"))
getCorrelation(meth,plot=FALSE)
sink()
if(length(ssheet[[idcol]]) < 15){
png(filename = paste0(qcfolder,proj.id, "_Correlations_pearson_pairwise.png"),
width = 9, height = 6, units = "in", res = 96)
print(getCorrelation(meth,plot=TRUE))
dev.off()
}
png(filename = paste0(qcfolder, proj.id, "_Clustering.png"),
width = 9, height = 6, units = "in", res = 96)
clusterSamples(meth, dist="euclidean", plot=TRUE, method = "ward.D2")
dev.off()
# Re-plotting PCs (custom chart)
# compute PCs and store in object
pca_compt <- PCASamples(meth, obj.return = T, screeplot = F)
# extract PCs components
pcafolder <- paste0(qcfolder, "PCA/")
dir.create(path = pcafolder, showWarnings = F)
pca_pc1_2 <- as.data.frame(x = pca_compt$x[,1:2])
for(myvars in plot.covariates){
pca_pc1_2$condition <- as.factor(ssheet[[myvars]])
png(filename = paste0(pcafolder, proj.id, "_PCA_",myvars,".png"),
width = 9, height = 6, units = "in",res=96)
print(ggplot(data = pca_pc1_2,
mapping = aes(x = PC1, y=PC2, col=condition, label=rownames(pca_pc1_2))) +
geom_point(size=3) + geom_text_repel(size=3) + ggtitle(label = "Principal component analysis", subtitle = myvars) +
theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5)) +
theme(plot.subtitle=element_text(size=12, hjust=0.5, face="italic", color="black")) +
theme(axis.title = element_text(size=12, hjust=0.5, face="bold", color="black")) +
theme(legend.text = element_text(size=8, hjust=0.5)) +
theme(legend.title = element_blank()) +
theme(axis.text = element_text(size=12, hjust=0.5, color="black")))
dev.off()
}
# retrieve and store % of methylation
perc.meth <- percMethylation(meth)
saveRDS(perc.meth,paste0(outfolder,"pctmethly.rds"))
base::rownames(perc.meth) <- paste0(meth$chr, "_", meth$start)
# Perform diff methylation
myDiff=calculateDiffMeth(meth)
write.table(myDiff,paste0(outfolder,proj.id,"_DiffMeth_single_CpG.txt"), row.names = F)
difftest <- read.table(paste0(outfolder,proj.id,"_DiffMeth_single_CpG.txt"), header = T)
difftest$comparison <- proj.id
difftest$qvalue_r <- as.character(cut(x = difftest$qvalue,
breaks = c(-1, 1e-100, 1e-10, 1e-02, 1),
labels = c("***","**","*","ns")))
myindex <- abs(difftest$meth.diff) < delta.meth
difftest$meth.diff <- abs(difftest$meth.diff)
difftest$qvalue_r[myindex] <- "ns"
write.table(difftest,paste0(outfolder,proj.id,"_DiffMeth_single_CpG.txt"), row.names = F)
write.table(difftest,paste0(outfolder,proj.id,"_DiffMeth_single_CpG.txt"), row.names = F)
# Adding color list and annotations
library(RColorBrewer)
color_list <- list()
annrows <- subset.data.frame(x = ssheet, select = c("Risk", "relapse","immunophenotype","sex","mir126"))
rownames(annrows) <- ssheet$SampleID
print(ssheet)
print(annrows)
anncols <- subset.data.frame(difftest, select = c("qvalue_r","meth.diff"))
base::rownames(anncols) <- difftest$start
print(anncols)
color_list[["qvalue_r"]] = c("*" = "#6497b1", "**" = "#03396c", "***" = "#011f4b", ns= "#c4cacf")
color_list[["meth.diff"]] = colorRampPalette(brewer.pal(n = 9, "Reds"))(100)
color_list[["mir126"]] <- brewer.pal(n = 9, "PuRd")
set1cols <- alpha(colour = RColorBrewer::brewer.pal(n = 9, "Set1"), alpha = 0.6)
color_list[["Risk"]] = c(VHR = set1cols[1], SR = set1cols[2], HR = set1cols[3], 'NA' = "white")
color_list[["immunophenotype"]] = c('B-I' = set1cols[4], 'B-II' = set1cols[5], 'B-III' = set1cols[6], 'NA' = "white")
color_list[["relapse"]] = c('yes' = set1cols[7], 'no' = set1cols[8], 'NA' = "white")
color_list[["sex"]] = c('M' = set1cols[9], 'F' = set1cols[1])
print(color_list)
# Heatmap
pctmeth_matrix <- t(perc.meth)
base::colnames(pctmeth_matrix) <- gsub(x = base::colnames(pctmeth_matrix), pattern = "chr[0-9]+_", replacement = "")
pheatmap(mat = pctmeth_matrix, main = gsub(x = proj.id, pattern = "_", replacement = " "),
filename = paste0(outfolder, proj.id,"_CpG_percent_methylation_matrix_pheatmap.pdf"),
width = plot.width, height = plot.height,
na_col = "pink",
cluster_cols = FALSE,
cluster_rows = TRUE,
annotation_row = annrows,
cellwidth = cellw,
cellheight = cellh,
display_numbers = T,
fontsize = fontsize,
fontsize_number = fontnumsize,
number_format = "%.0f",
border_color = NA,
annotation_col = anncols,
annotation_colors = color_list,
color = c("#F5F5F5","#EEEEEE","#CCCCCC","#999999", "#666666","#333333","#000000"), breaks = c(0,10,20,30,50,70,90,100)
)
saveRDS(object = pctmeth_matrix, paste0(outfolder, proj.id,"_CpG_percent_methylation_matrix_pheatmap.rds"))
saveRDS(object = list(anncol = anncols, annrow = annrows, anncolors = color_list), paste0(outfolder, proj.id,"_annotations_matrix_pheatmap.rds"))
saveRDS(object = difftest, paste0(outfolder, proj.id,"_CpG_differential_methylation.rds"))
saveRDS(object = perc.meth, paste0(outfolder, proj.id,"_CpG_percent_methylation.rds"))
write.table(x = perc.meth, file = paste0(outfolder, proj.id,"_CpG_percent_methylation.txt"))
write.table(x = difftest, file = paste0(outfolder, proj.id,"_CpG_differential_methylation.txt"))
saveRDS(myobj, file = paste0(outfolder,proj.id,"_methylkit.rds"))
saveRDS(myobj, file = paste0(outfolder,proj.id,"_methylkit_meth.rds"))
}
\ No newline at end of file
Methylation/README.md 0 → 100644
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# Introduction
1-2ug of DNA was converted using EpiTect Bisulfite Kit (Qiagen, ID 59104) and following manufacturer’s instructions. After purification, 100ng or 11ul of each sample was amplified, by PCR, for the TSS region and the EGFL7 intron 7. A High Fidelity DNA polymerase, KAPA HiFi HotStart Uracil+ Kit (Roche, Basel, CH), was used, and primer sequences and thermocycler settings are shown below.
Methylation profile analysis and differential methylation analysis (according to groups of interest) was performed only in the PCR amplified region.
# Data analysis workflow
The data analysis consists in 3 main steps:
- QC
- Mapping and CpG coverage quantification
- Differential methylation analysis
## QC
Raw sequences were quality checked using fastqc (v.0.11.8) and trimmed for adapters and base quality using cutadapt v.1.16 (minimum base quality 20; minimum length 25bp).
Both tails of the reads were subjected to trimming if necessary by using different clipping parameters settings according to runs batches.
Very bad quality samples were discarded from downstream analyses.
## Mapping and CpG coverage quantification
Passing quality checks reads were then mapped to the human reference genome GRCh38 using bismark v.0.22.1 (--local mode).
Coverage files (.cov) obtained from bismark output were then used as input for differential methylation analysis by using the MethylKit R package (v1.10.0).
## Differential methylation analysis
Differential methylation analysis was performed primarly at single CpG considering the small length of the region.
To get rid of CpGs not included in the targeted regions, we tailored the analysis only to genomic coordinates (chr9: 136668000-136671000) mapping to (TSS, 5’ and 3’ intron-7 subregions) with selectByOverlap function.
Hence, we discarded reads with less than 10 counts and reads with coverage lower than the 5th percentile. In this way we were able to evaluate methylation percentage in most of the samples under analysis.
We then normalized coverage by using the normalizeCoverage function.
Exploratory data analysis was performed by using built-in functions provided by the package, including PCA, clustering, and correlations.
CpGs with q.value < 0.01 (logistic regression test) and absolute delta methylation (negative or positive) percentage of at least 10 between the compared groups, were considered differentially methylated.
Percent methylation matrix was used as input to produce heatmaps (pheatmap R package).
Color palette presets from RColorBrewer package were used for % methylation and row annotation tiles coloring.
README.md 0 → 100644
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# miR-126 and minimal residual disease in B-ALL
Complete elimination of B-cell acute lymphoblastic leukemia (B-ALL) by a risk-adapted primary treatment approach remains a clinical key objective, which fails in up to a third of patients. Recent evidence has implicated subpopulations of B-ALL cells with stem-like features in disease persistence. We hypothesized that microRNA-126, a core regulator of hematopoietic and leukemic stem cells, may resolve intra-tumor heterogeneity in B-ALL and uncover therapy-resistant subpopulations. We exploited patient-derived xenograft (PDX) models with B-ALL cells transduced with a miR-126 reporter allowing the prospective isolation of miR-126(high) cells for their functional and transcriptional characterization. Discrete miR-126(high) populations, often characterized by MIR126 locus de-methylation, were identified in 8/9 PDX models and showed increased repopulation potential, in vivo chemotherapy resistance and hallmarks of quiescence, inflammation and stress-response pathway activation. Cells with a miR-126(high) transcriptional profile were identified as distinct disease subpopulations by single cell RNA sequencing in diagnosis samples from adult and pediatric B-ALL. Expression of miR-126 and locus methylation were tested in several pediatric and adult B-ALL cohorts, which received standardized treatment. High microRNA-126 levels and locus de-methylation at diagnosis associate with sub-optimal response to induction chemotherapy (MRD > 0.05% at day +33 or MRD+ at day +78).
# Code and workflows
This repository is structured in different folders according to the different omics covered in the paper.
The main categories are:
- scRNAseq
- BulkRNAseq
- Methylation
- Whole Exome Sequencing
Each folder will include a bried description of the main data analysis steps and the most important scripts used for producing data present in the
manuscript.
scRNAseq/PaperPanels.R 0 → 100644
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# Immagini singole malattie:
# 1- Clusters più accesi
# 2- Vlns con singolo colore e diverse sfumature
# 3- miRNAh ----> low solo riga continua e blocco sulla malattia.
# 4. Unico pannello
#
library(Seurat)
library(ggplot2)
library(gridExtra)
library(Seurat)
library(ggplot2)
library(scales)
library(dplyr)
# Fix immagini singole - colori più accesi
objs <- list()
data2plot <- list()
for(i in c("L1","L2","L3","L5","L7")){
objs[[i]] <- readRDS(file = paste0("/beegfs/scratch/ric.gentner/ric.gentner/scRNA_BALL/resultswithpediatric/",i,"/01-seurat/",i,"_final.rds"))
sigs <- readRDS(file = paste0("/beegfs/scratch/ric.gentner/ric.gentner/scRNA_BALL/resultswithpediatric/",i,"/00-Signatures/",i,"_ModScores.rds"))
objs[[i]] <- AddMetaData(objs[[i]], sigs)
data2plot[[i]] <- FetchData(object = objs[[i]], vars = c("UMAP_1","UMAP_2","tSNE_1","tSNE_2",colnames(objs[[i]]@meta.data)))
}
# Vlnplots
# Verde scuro: #64921E
# Verde chiaro: #EFF4E8
mypalette <- c("#FFE599", "#cc0000")
scaleFUN <- function(x) sprintf("%.1f", x)
colfunc <- list()
dimplots <- list()
diseases <- list(L1 = "PZ1227",
L2 = "PZ13000037",
L3 = "PZ114966",
L5 = "PZ170160",
L7 = "PZ5114")
vlnplots <- list()
for(i in c("L1","L2","L3","L5","L7")){
if(i == "L2"){
objs[[i]] <- SetIdent(object = objs[[i]], value = "RNA_snn_res.1.2")
colfunc[[i]] <- rev(colorRampPalette(mypalette)(length(unique(Idents(objs[[i]]))) - 1))
vlnplots[[i]] <- VlnPlot(object = objs[[i]], features = "dIntegrated_up", sort = T, cols = colfunc[[i]],
pt.size = 0, idents = setdiff(levels(objs[[i]]$RNA_snn_res.1.2), "9")) + NoLegend() +
geom_boxplot(width=0.15, color="black",outlier.size = 0) +
ggtitle(label = paste0(diseases[[i]], " - signature score")) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), #plot.title = element_text(hjust = 0.5, size = 12),
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
names(colfunc[[i]]) <- levels(vlnplots[[i]]$data$ident)
colfunc[[i]][10] <- "lightgrey"
names(colfunc[[i]])[10] <- "9"
dimplots[[i]] <- DimPlot(object = objs[[i]], pt.size = 0.3, #group.by = "RNA_snn_res.1.2",
label = T, cols = colfunc[[i]], order = T) + NoLegend() +
ggtitle(paste0(diseases[[i]], " - res 1.2")) +
scale_y_continuous(labels = scaleFUN) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), #plot.title = element_text(hjust = 0.5, size = 12)
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
}
if(i == "L7"){
objs[[i]] <- SetIdent(object = objs[[i]], value = "RNA_snn_res.0.6")
colfunc[[i]] <- rev(colorRampPalette(mypalette)(length(unique(Idents(objs[[i]]))) - 3))
vlnplots[[i]] <- VlnPlot(object = objs[[i]], features = "dIntegrated_up", sort = T, cols = colfunc[[i]],
pt.size = 0, idents = setdiff(levels(objs[[i]]$RNA_snn_res.0.6), c("8","9","10"))) + NoLegend() +
geom_boxplot(width=0.15, color="black",outlier.size = 0) +
ggtitle(label = paste0(diseases[[i]], " - signature score")) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), #plot.title = element_text(hjust = 0.5, size = 12),
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
names(colfunc[[i]]) <- levels(vlnplots[[i]]$data$ident)
colfunc[[i]][9:11] <- "lightgrey"
names(colfunc[[i]])[9:11] <- c("8","9","10")
dimplots[[i]] <- DimPlot(object = objs[[i]], pt.size = 0.3, group.by = "RNA_snn_res.0.6",
label = T, cols = colfunc[[i]], order = T) + NoLegend() +
ggtitle(paste0(diseases[[i]], " - res 0.6")) +
scale_y_continuous(labels = scaleFUN) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), # plot.title = element_text(hjust = 0.5, size = 12),
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
}
if(i == "L1"){
objs[[i]] <- SetIdent(object = objs[[i]], value = "RNA_snn_res.0.6")
colfunc[[i]] <- c("#CC0000", "#DA412B", "#E16241", "#E98257","#F7C483" , "#FFE599")
vlnplots[[i]] <- VlnPlot(object = objs[[i]], features = "dIntegrated_up", sort = T, cols = colfunc[[i]],
pt.size = 0, idents = setdiff(levels(objs[[i]]$RNA_snn_res.0.6), c("6","7"))) + NoLegend() +
geom_boxplot(width=0.15, color="black",outlier.size = 0) +
ggtitle(label = paste0(diseases[[i]], " - signature score")) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), #plot.title = element_text(hjust = 0.5, size = 12),
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
names(colfunc[[i]]) <- levels(vlnplots[[i]]$data$ident)
colfunc[[i]][7:8] <- "lightgrey"
names(colfunc[[i]])[7:8] <- c("6","7")
dimplots[[i]] <- DimPlot(object = objs[[i]], pt.size = 0.3, group.by = "RNA_snn_res.0.6",
label = T, cols = colfunc[[i]], order = T) + NoLegend() +
ggtitle(paste0(diseases[[i]], " - res 0.6")) +
scale_y_continuous(labels = scaleFUN) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), # plot.title = element_text(hjust = 0.5, size = 12),
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
}
if(i %in% c("L3","L5")){
if(i == "L3"){
colfunc[[i]] <- c("#CC0000","#DA412B","#E98257","#F7C483","#FFE599")
}else{
colfunc[[i]] <- rev(colorRampPalette(mypalette)(length(unique(Idents(objs[[i]])))))
}
objs[[i]] <- SetIdent(object = objs[[i]], value = "RNA_snn_res.0.6")
vlnplots[[i]] <- VlnPlot(object = objs[[i]], features = "dIntegrated_up", sort = T, cols = colfunc[[i]], pt.size = 0) + NoLegend() +
geom_boxplot(width=0.15, color="black",outlier.size = 0) +
ggtitle(label = paste0(diseases[[i]], " - signature score")) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), #plot.title = element_text(hjust = 0.5, size = 12),
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
names(colfunc[[i]]) <- levels(vlnplots[[i]]$data$ident)
dimplots[[i]] <- DimPlot(object = objs[[i]], pt.size = 0.3, group.by = "RNA_snn_res.0.6",
label = T, cols = colfunc[[i]], order = T) + NoLegend() +
ggtitle(paste0(diseases[[i]], " - res 0.6")) +
scale_y_continuous(labels = scaleFUN) +
theme(axis.title = element_blank(),
axis.line = element_line(size = 0.2, color = "black"),
axis.text = element_text(size = 8), # plot.title = element_text(hjust = 0.5, size = 12),
plot.title = element_blank(),
axis.ticks = element_line(size = 0.2, color = "black")
)
}
}
png(filename = "Clusters_signature_distribution_res300.png", width = 6, height = 10, units = "in", res = 300)
grid.arrange(dimplots[["L1"]], vlnplots[["L1"]],
dimplots[["L2"]], vlnplots[["L2"]],
dimplots[["L3"]], vlnplots[["L3"]],
dimplots[["L5"]], vlnplots[["L5"]],
dimplots[["L7"]], vlnplots[["L7"]], nrow=5, ncol=2)
dev.off()
png(filename = "Clusters_signature_distribution_res200.png", width = 6, height = 10, units = "in", res = 200)
grid.arrange(dimplots[["L1"]], vlnplots[["L1"]],
dimplots[["L2"]], vlnplots[["L2"]],
dimplots[["L3"]], vlnplots[["L3"]],
dimplots[["L5"]], vlnplots[["L5"]],
dimplots[["L7"]], vlnplots[["L7"]], nrow=5, ncol=2)
dev.off()
save.image(file = "PaperPanels.RData")
##### UMAP - IMAGES #########
a <- sample(rainbow(50),5)
dgns <- readRDS("/beegfs/scratch/ric.gentner/ric.gentner/scRNA_BALL/resultswithpediatric/Diagnosis/01-seurat/Diagnosis_final.rds")
dgns <- SetIdent(object = dgns, value = "RNA_DonorID")
mydata <- FetchData(object = dgns, vars = c("UMAPh_1","UMAPh_2", "orig.ident"))
plotdata <- mydata
patients <- c("DIAGNOSIS","PZ1227","PZ13000037","PZ114966","PZ170160","PZ5114")
pn <- 1
plotdata$plotnum <- patients[[(pn)]]
for (id in unique(mydata$orig.ident)) {
pn <- pn + 1
pdata <- subset(mydata, orig.ident == id)
pdata$plotnum <- patients[[(pn)]]
plotdata <- rbind(plotdata, pdata)
}
png("Panel_DGNs_singleDiseases_distribution.png", width = 9,height = 6, units = "in", res = 300)
ggplot(plotdata, aes(x = UMAPh_1, y = UMAPh_2, col = orig.ident)) +
#theme_bw() +
theme(legend.position = "none",
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
#panel.background = element_rect(fill = "#FAFAFA"),
panel.background = element_rect(fill = "white"),
strip.background = element_rect(fill = "#FAFAFA"),
axis.line = element_line(colour = "black", size = 0.2),
axis.ticks = element_line(size = 0.2),
axis.text = element_text(size = 8)
#strip.text = element_blank()
) +
geom_point(size = .1, alpha = .5) +
xlab("") +
ylab("") +
facet_wrap(.~plotnum, ncol = 3)
dev.off()
#### PLOTTING SINGLE-R SIGNATURES DISTRIBUTION ####
mydata.full <- FetchData(object = dgns, vars = c("UMAPh_1","UMAPh_2", colnames(dgns@meta.data)))
singler.sig <- grep("Database", x = grep("SingleR", x = colnames(dgns@meta.data), value = T), invert = T, value = T)
u <- as.data.frame(prop.table(table(dgns$RNA_DonorID, dgns$SingleR_BlueprintEncodeData_labels), 1))
u$dataset <- "SingleR_BlueprintEncodeData_labels"
for (id in singler.sig[2:length(singler.sig)]) {
g <- as.data.frame(prop.table(table(dgns@meta.data[,"RNA_DonorID"],
dgns@meta.data[,id]), 1))
g$dataset <- id
u <- rbind(u,g)
}
u$dataset <- gsub(u$dataset, pattern = "SingleR_", replacement = "")
u$dataset <- gsub(u$dataset, pattern = "_labels", replacement = "")
u$dataset <- gsub(u$dataset, pattern = "BlueprintEncodeData", replacement = "BPE")
u$dataset <- gsub(u$dataset, pattern = "HumanPrimaryCellAtlasData", replacement = "HPCA")
u$dataset <- gsub(u$dataset, pattern = "SingleRrefined_", replacement = "r")
u$dataset <- gsub(u$dataset, pattern = "MonacoImmuneData", replacement = "Monaco")
u$dataset <- gsub(u$dataset, pattern = "NovershternHematopoieticData", replacement = "Nover")
# re-classify labels with % < 2% as "<2%"
u_filt <- u
u_filt$Var2 <- as.character(u_filt$Var2)
u_filt$Var2[u_filt$Freq < 0.02] <- "<2%"
u_filt$Var2 <- as.factor(u_filt$Var2)
disease_distr_list <- split(u_filt, u$dataset)
dname <- list(BPE = "Blueprint Encode",
HPCA = "Human primary cell atlas",
Monaco = "Monaco Immune Data",
Nover = "Novershtern Hematopoietic Data")
plot_dat_dist <- list()
for(myset in c("BPE","HPCA","Monaco","Nover")){
if(myset %in% c("BPE","HPCA")){
plot_dat_dist[[myset]] <- ggplot(data = disease_distr_list[[myset]],
mapping = aes(Var1, Freq*100, fill = Var2)) +
scale_fill_brewer(palette = "Paired") +
ggtitle(label = dname[[myset]]) +
geom_bar(stat = "identity", width = 0.5) +
guides(fill = guide_legend(nrow = 2)) +
#guides(colour = guide_legend(title = "", override.aes = list(size=20))) +
theme(legend.position = "bottom",
plot.title = element_text(size = 7, hjust = 0.5, face = "plain"),
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black", size = 0.2),
axis.title = element_blank(),
plot.margin = margin(0.2, 0.2,0.2,0.2, unit = "cm"),
axis.text = element_text(size = 5),
legend.title = element_blank(),
legend.text = element_text(size = 4),
legend.spacing.y = unit(0.1, 'cm'),
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size = unit(0.2, 'cm'),
legend.key = element_rect(fill = "white"),
legend.background = element_blank(),
#legend.key.height = unit(0, 'cm'),
legend.key.width = unit(0.2, 'cm'),
legend.box.margin=margin(-10,0,0,0)
) #+ facet_wrap(.~Var1, ncol = 5)
}
else{
plot_dat_dist[[myset]] <- ggplot(data = disease_distr_list[[myset]],
mapping = aes(Var1, Freq*100, fill = Var2)) +
scale_fill_brewer(palette = "Paired") +
ggtitle(label = dname[[myset]]) +
geom_bar(stat = "identity", width = 0.5) +
guides(fill = guide_legend(nrow = 2)) +
#guides(colour = guide_legend(title = "", override.aes = list(size=20))) +
theme(legend.position = "bottom",
plot.title = element_text(size = 7, hjust = 0.5, face = "plain"),
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black", size = 0.2),
axis.title = element_blank(),
plot.margin = margin(0, 0.2,0.2,0.2, unit = "cm"),
axis.text = element_text(size = 5),
legend.title = element_blank(),
legend.key = element_rect(fill = "white"),
legend.text = element_text(size = 4),
legend.spacing.y = unit(0.1, 'cm'),
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size = unit(0.2, 'cm'),
legend.background = element_blank(),
#legend.key.height = unit(0, 'cm'),
legend.key.width = unit(0.2, 'cm'),
legend.box.margin=margin(-10,0,0,0)
) #+ facet_wrap(.~Var1, ncol = 5)
}
}
png(filename = "Panel_cell_type_distribution_across_diseases_res_200.png",
width = 6, height = 4.5, units = "in", res = 200)
grid.arrange(plot_dat_dist[["BPE"]], plot_dat_dist[["HPCA"]], plot_dat_dist[["Monaco"]], plot_dat_dist[["Nover"]])
dev.off()
png(filename = "Panel_cell_type_distribution_across_diseases_res_400.png",
width = 6, height = 4.5, units = "in", res = 400)
grid.arrange(plot_dat_dist[["BPE"]], plot_dat_dist[["HPCA"]], plot_dat_dist[["Monaco"]], plot_dat_dist[["Nover"]])
dev.off()
############## SHINYGO - PLOTS #############
hallmarks.sgo.high <- read.csv(file = "shinyGOinputs/high_hallmarks.csv")
hallmarks.sgo.high$condition <- "High"
hallmarks.sgo.high$Pathway <- gsub(x = hallmarks.sgo.high$Pathway, pattern = "HALLMARK ", replacement = "")
hallmarks.sgo.low <- read.csv(file = "shinyGOinputs/low_hallmark.csv")
hallmarks.sgo.low$condition <- "Low"
hallmarks.sgo.low$Pathway <- gsub(x = hallmarks.sgo.low$Pathway, pattern = "HALLMARK ", replacement = "")
order_h_l <- hallmarks.sgo.high[order(x = hallmarks.sgo.high$Fold.Enrichment, decreasing = T),]
order_l_h <- hallmarks.sgo.low[order(x = hallmarks.sgo.low$Fold.Enrichment, decreasing = F),]
hallmarks.sgo <- rbind(hallmarks.sgo.high, hallmarks.sgo.low)
df <- transform(hallmarks.sgo, Score=ifelse(as.character(condition) %in% c("Low"), -Fold.Enrichment, Fold.Enrichment))
df <- droplevels.data.frame(df)
df$Pathway <- factor(x = df$Pathway, levels = rev(c(order_h_l$Pathway, c(setdiff(y = order_h_l$Pathway, x =order_l_h$Pathway)))))
colnames(df)[which(colnames(df) == "Score")] <- "Fold Enrichment"
hallmarks_plot <- ggplot() +
geom_bar(data=df,aes(x=Pathway, y=`Fold Enrichment`, fill=condition), stat="identity", width = 0.2) +
scale_fill_manual(values = c("#D4070F","#0066E7")) +
geom_point(data=df,aes(x=Pathway, y=`Fold Enrichment`, colour=condition, size = nGenes), stat="identity") +
scale_color_manual(values = alpha(c("#D4070F","#0066E7"), alpha = 1)) +
geom_hline(yintercept=0) +
coord_flip() +
scale_y_continuous(labels=abs,limits=c(-7.5,7.5)) +
theme(legend.position = "right",
plot.title = element_text(size = 7, hjust = 0.5, face = "plain"),
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black", size = 0.3),
axis.title = element_blank(),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 10)#,
#axis.text = element_text(size = 7)
)
#### KEGGS
kegg.sgo.high <- read.csv(file = "shinyGOinputs/high_kegg.csv")
kegg.sgo.high$condition <- "High"
kegg.sgo.low <- read.csv(file = "shinyGOinputs/low_kegg.csv")
kegg.sgo.low$condition <- "Low"
order_h_l <- kegg.sgo.high[order(x = kegg.sgo.high$Fold.Enrichment, decreasing = T),]
order_l_h <- kegg.sgo.low[order(x = kegg.sgo.low$Fold.Enrichment, decreasing = F),]
kegg.sgo <- rbind(kegg.sgo.high, kegg.sgo.low)
df <- transform(kegg.sgo, Score=ifelse(as.character(condition) %in% c("Low"), -Fold.Enrichment, Fold.Enrichment))
df <- droplevels.data.frame(df)
df$Pathway <- factor(x = df$Pathway, levels = rev(c(order_h_l$Pathway, c(setdiff(y = order_h_l$Pathway, x =order_l_h$Pathway)))))
colnames(df)[which(colnames(df) == "Score")] <- "Fold Enrichment"
kegg_plot <- ggplot() +
geom_bar(data=df,aes(x=Pathway, y=`Fold Enrichment`, fill=condition), stat="identity", width = 0.2) +
scale_fill_manual(values = c("#D4070F","#0066E7")) +
geom_point(data=df,aes(x=Pathway, y=`Fold Enrichment`, colour=condition, size = nGenes), stat="identity") +
scale_color_manual(values = alpha(c("#D4070F","#0066E7"), alpha = 0.8)) +
geom_hline(yintercept=0) +
coord_flip() +
scale_y_continuous(labels=abs,limits=c(-10,10)) +
theme(legend.position = "right",
plot.title = element_text(size = 7, hjust = 0.5, face = "plain"),
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black", size = 0.3),
axis.title = element_blank(),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 10)#,
#axis.text = element_text(size = 7)
)
### TF ####
TFfact.sgo.high <- read.csv(file = "shinyGOinputs/high_TFfact.csv")
TFfact.sgo.high$condition <- "High"
TFfact.sgo.low <- read.csv(file = "shinyGOinputs/low_TFfact.csv")
TFfact.sgo.low$condition <- "Low"
order_h_l <- TFfact.sgo.high[order(x = TFfact.sgo.high$Fold.Enrichment, decreasing = T),]
order_l_h <- TFfact.sgo.low[order(x = TFfact.sgo.low$Fold.Enrichment, decreasing = F),]
TFfact.sgo <- rbind(TFfact.sgo.high, TFfact.sgo.low)
df <- transform(TFfact.sgo, Score=ifelse(as.character(condition) %in% c("Low"), -Fold.Enrichment, Fold.Enrichment))
df <- droplevels.data.frame(df)
df$Pathway <- factor(x = df$Pathway, levels = rev(c(order_h_l$Pathway, c(setdiff(y = order_h_l$Pathway, x =order_l_h$Pathway)))))
colnames(df)[which(colnames(df) == "Score")] <- "Fold Enrichment"
TFfact_plot <- ggplot() +
geom_bar(data=df,aes(x=Pathway, y=`Fold Enrichment`, fill=condition), stat="identity", width = 0.2) +
scale_fill_manual(values = c("#D4070F","#0066E7")) +
geom_point(data=df,aes(x=Pathway, y=`Fold Enrichment`, colour=condition, size = nGenes), stat="identity") +
scale_color_manual(values = alpha(c("#D4070F","#0066E7"), alpha = 1)) +
geom_hline(yintercept=0) +
coord_flip() +
scale_y_continuous(labels=abs,limits=c(-12.5,12.5)) +
theme(legend.position = "right",
plot.title = element_text(size = 7, hjust = 0.5, face = "plain"),
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black", size = 0.3),
axis.title = element_blank(),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 10)#,
#axis.text = element_text(size = 7)
)
# GOs
GObiopro.sgo.high <- read.csv(file = "shinyGOinputs/high_GObiopro.csv")
GObiopro.sgo.high$condition <- "High"
GObiopro.sgo.low <- read.csv(file = "shinyGOinputs/low_GObiopro.csv")
GObiopro.sgo.low$condition <- "Low"
order_h_l <- GObiopro.sgo.high[order(x = GObiopro.sgo.high$Fold.Enrichment, decreasing = T),]
order_l_h <- GObiopro.sgo.low[order(x = GObiopro.sgo.low$Fold.Enrichment, decreasing = F),]
GObiopro.sgo <- rbind(GObiopro.sgo.high, GObiopro.sgo.low)
df <- transform(GObiopro.sgo, Score=ifelse(as.character(condition) %in% c("Low"), -Fold.Enrichment, Fold.Enrichment))
df <- droplevels.data.frame(df)
df$Pathway <- factor(x = df$Pathway, levels = rev(c(order_h_l$Pathway, c(setdiff(y = order_h_l$Pathway, x =order_l_h$Pathway)))))
colnames(df)[which(colnames(df) == "Score")] <- "Fold Enrichment"
GObiopro_plot <- ggplot() +
geom_bar(data=df,aes(x=Pathway, y=`Fold Enrichment`, fill=condition), stat="identity", width = 0.2) +
scale_fill_manual(values = c("#D4070F","#0066E7")) +
geom_point(data=df,aes(x=Pathway, y=`Fold Enrichment`, colour=condition, size = nGenes), stat="identity") +
scale_color_manual(values = alpha(c("#D4070F","#0066E7"), alpha = 1)) +
geom_hline(yintercept=0) +
coord_flip() +
scale_y_continuous(labels=abs,limits=c(-4,4)) +
theme(legend.position = "right",
plot.title = element_text(size = 7, hjust = 0.5, face = "plain"),
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black", size = 0.3),
axis.title = element_blank(),
axis.text.y = element_text(size = 8),
axis.title.x = element_text(size = 7)#,
#axis.text = element_text(size = 7)
)
# png(filename = "ShinyGO_enrichments_miRNA_high_low_singlecell_markers.png", width = 12, height = 8, units = "in", res = 300)
# grid.arrange(kegg_plot, hallmarks_plot , GObiopro_plot, TFfact_plot, ncol = 2, nrow = 2)
# dev.off()
# Single plots
png(filename = "GObiopro_enrichments_miRNA_high_low_singlecell_markers.png", width = 12, height = 8, units = "in", res = 300)
grid.arrange(GObiopro_plot, ncol = 1, nrow = 1)
dev.off()
png(filename = "hallmarks_enrichments_miRNA_high_low_singlecell_markers.png", width = 12, height = 8, units = "in", res = 300)
grid.arrange(hallmarks_plot, ncol = 1, nrow = 1)
dev.off()
png(filename = "kegg_enrichments_miRNA_high_low_singlecell_markers.png", width = 12, height = 8, units = "in", res = 300)
grid.arrange(kegg_plot, ncol = 1, nrow = 1)
dev.off()
png(filename = "TF_enrichments_miRNA_high_low_singlecell_markers.png", width = 12, height = 8, units = "in", res = 300)
grid.arrange(TFfact_plot, ncol = 1, nrow = 1)
dev.off()
scRNAseq/README.md 0 → 100644
View file @ fe5789a9
## Introduction
**scRNAseq** analysis was performed using a standard pipeline that includes the following steps:
Most of the single-cell RNAseq analyses were performed with [Seurat](https://satijalab.org/seurat/).
Below the main steps of the basic data analysis workflow that start from a minimal object after loading of 10X data to markers identification:
1. Normalization (default seurat settings)
2. Scaling (with following variables to regress out: percent.mt + nCount_RNA and CC.Difference calculated as show in [vignette](https://satijalab.org/seurat/articles/cell_cycle_vignette.html#alternate-workflow-1))
3. Dimensionality reduction: PCA
4. Harmony batch removal (patientID - only for integrated analysis with all Diagnoses)
5. Clustering
6. Markers identification (different resolution according to datasets / subsets)
7. Evaluation of signatures according to module score (AddModuleScore function - default settings)
Data analysis was performed both at single and integrated level.
Parameters and metrics are described in supplementary tables.
Raw counts and complete metadata can be found on GEO repository [GSE236136](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE236136).
Seurat objects for each disease are also available in seurat_objects folder for convenience.
scRNAseq/high_GObiopro.csv 0 → 100644
View file @ fe5789a9
"Enrichment FDR","nGenes","Pathway Genes","Fold Enrichment","Pathway","URL","Genes"
2.33011874368253e-27,229,2282,2.06832770594193,"Immune system process "," http://amigo.geneontology.org/amigo/term/GO:0002376"," CD99 SLC25A5 CD79B CD9 BTK WAS DPEP1 CD74 VIM RNASET2 TNFRSF1B CYBA CYFIP2 GDI2 CNN2 SPI1 KLF6 PKM RHOA RASGRP2 CLEC2D ATP1B3 ATP6AP1 TCF3 ACTB PAG1 CAPZB HSP90AA1 MEF2C LAT2 FTL FKBP1A DYNLL1 IGBP1 FLT3LG EFNB1 ITGA6 EZR HSP90AB1 DDX17 XBP1 AP1B1 CTSG COCH HIF1A PSMA6 NFKBIA MYL9 ARHGEF7 RGCC CORO1A CCL17 PYCARD PRKD2 CD79A NOP53 CDK6 SERPINE1 ICAM2 RAB5C FBXW7 HSPA8 MDK POU2AF1 ARPC3 GAPDH RIPOR2 CCR6 CCND3 LY86 FOXP1 ITGA4 IGFBP2 ID2 SUMO1 NCF2 PRDX1 TNFAIP3 MYB YPEL5 DUSP1 ELF1 CXCR4 SASH3 PRXL2A HVCN1 ADGRE5 OPTN H2BC11 DEK S1PR4 HCST PRMT1 CFP KLF2 ZFP36 VPREB3 SEC14L1 BST2 ARPC1B GMFG MPP1 CAP1 JCHAIN H3-3B IL2RA OSTF1 ANXA1 GYPC KLF4 CCN3 LEF1 CD27 CMTM3 ARRB2 PMAIP1 IFITM3 CD53 ECM1 PARP1 HSPD1 NFKBIZ PLAC8 MSN IL2RG CDKN2A GSN UBC FLI1 ZFP36L2 BANK1 CMTM7 TSC22D3 MX1 WASF2 HMHB1 RUNX1 G6PD S100A1 LAPTM5 GBP4 FCMR REL IFI16 SERINC5 CITED2 YWHAZ CLEC4E YWHAB PHB FTH1 PTGDR LGALS9 VPREB1 UBB FOS MZB1 JUNB BCL2L1 CTPS1 ISG20 MYD88 JUP CD34 TBC1D10C CD19 JUN FUCA1 CALR HCLS1 FUT7 H2BC4 ATP6AP2 AP1S2 PRKX SOCS3 SIVA1 IFITM2 IKZF1 BCAP31 EVI2B IFITM1 LAMP1 GNG2 SELL S100A13 PPIA MME ADA ARID5A SPTAN1 H2BC12 HLA-DRB5 CALM1 PNP HMGN2 RPL39 HLA-DQB1 HLA-C HLA-B HLA-E DDX3X IFI30 VAMP2 LTB HLA-A HLA-DRB1 HLA-DPA1 PSMB8 AIF1 LST1 HLA-DQA1 HLA-DPB1 HLA-DMA ARPC4 PSMB9 IKBKG MILR1 CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 LAIR1 HLA-DRA PSME2 PSME1 IRF9"
2.39379240446075e-25,226,2315,2.01213423833711,"Response to organic substance "," http://amigo.geneontology.org/amigo/term/GO:0010033"," SLC25A5 CD9 BTK WAS CD74 VIM TNFRSF1B CYBA CYFIP2 YBX3 GNA15 CNN2 SPI1 PKM RHOA ATP6AP1 PTPN18 ACTB SESN1 HSP90AA1 MEF2C P2RX5 ATRX AKR1B1 PPP1R15A FKBP1A DYNLL1 RPLP0 IGBP1 FLT3LG ITGA6 SEMA6A EZR HSP90AB1 RANBP1 DDX17 XBP1 RPL3 CTSG HIF1A SRSF5 TCL1A PSMA6 NFKBIA RBBP7 TIMP1 CORO1A CCL17 PYCARD TLE5 PRKD2 BBC3 HSPB1 SERPINE1 TLE4 ENG DNTT SPOCK2 DDX5 GAB1 WFS1 HSPA8 MDK PTGES3 ARPC3 GAPDH MAN1A1 RIPOR2 SOD2 CCR6 CCND3 LY86 NR3C1 ATP6V0E1 FOXP1 ITGA4 IGFBP2 SUMO1 KDM5B TNFAIP3 MYB CCN2 DUSP1 ELF1 SOCS2 CXCR4 HNRNPA2B1 OPTN NR4A1 H2BC11 RNF113A FOSB ID1 KLF2 SMARCA4 GNAI1 ZFP36 CDKN2D SNX6 BST2 ARPC1B DNMT1 AKAP12 PTPRE H3-3B PCNA NASP PEMT BTG1 EDEM1 IL2RA ANXA1 HNRNPA1 MDM2 TRA2B KLF4 ATP6V1G1 ATIC LEF1 CD27 DUSP6 ARRB2 PMAIP1 IFITM3 ECM1 PARP1 HSPD1 MSN IL2RG MTDH GSN ARID5B FOXO1 UBC ZFP36L2 MX1 HMHB1 RUNX1 CALM3 G6PD IER2 UBXN1 LAPTM5 GBP4 REL STT3B IFI16 SPRY1 CITED2 YWHAZ TP53INP1 CCDC186 MCM7 PHB CDT1 LDLRAD4 LGALS9 LIMS1 HNRNPF UBB FOS SMAD1 MGMT MZB1 JUNB BCL2L1 RGS19 ISG20 MYD88 JUP LEMD3 RPL15 UCP2 TYMS JUN AGTRAP CALR HCLS1 NRIP1 FUT7 SHISA2 NPM1 SOCS3 IFITM2 IL3RA BCAP31 IFITM1 GNG2 MT1X LAMTOR4 LITAF PPIA MME ADA ARID5A HLA-DRB5 CALM1 MT-CYB RPL10A MT-ND3 MT-ND4 MT-ND1 TMSB4X HLA-E IFI30 VAMP2 LTB HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PEG10 PSMB9 TXNIP IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 SMARCB1 C1QTNF4 PSME2 PSME1 IRF9"
5.69171826512765e-23,218,2281,1.96983892730946,"Cellular response to chemical stimulus "," http://amigo.geneontology.org/amigo/term/GO:0070887"," SLC25A5 BTK WAS DPEP1 CD74 VIM TNFRSF1B CYBA CYFIP2 YBX3 GNA15 CNN2 SPI1 PKM RHOA RASGRP2 ATP6AP1 PTPN18 ENO1 ACTB SESN1 HSP90AA1 MEF2C ATRX AKR1B1 PPP1R15A FKBP1A DYNLL1 RPLP0 FUS FLT3LG ITGA6 SEMA6A CAPN3 EZR HSP90AB1 RANBP1 DDX17 XBP1 RPL3 CTSG HIF1A SRSF5 TCL1A PSMA6 NFKBIA PGK1 TIMP1 RGCC CORO1A CCL17 PYCARD PRKD2 BBC3 NOP53 HSPB1 SERPINE1 TLE4 ENG SPOCK2 DDX5 GAB1 WFS1 FBXW7 HSPA8 MDK PTGES3 ARPC3 GAPDH RIPOR2 SOD2 CCR6 CCND3 LY86 NR3C1 ATP6V0E1 FOXP1 ITGA4 IGFBP2 SUMO1 NCF2 KDM5B PRDX1 TNFAIP3 MYB CCN2 DUSP1 ELF1 SOCS2 CXCR4 PRXL2A HNRNPA2B1 HVCN1 OPTN NR4A1 H2BC11 RNF113A FOSB ID1 KLF2 SMARCA4 GNAI1 ZFP36 SNX6 BST2 DNMT1 MPP1 AKAP12 PTPRE H3-3B PCNA EDEM1 SLC38A2 IL2RA STK26 ANXA1 HNRNPA1 MDM2 TRA2B KLF4 ATP6V1G1 CCN3 ATIC LEF1 CD27 ARRB2 PMAIP1 IFITM3 ECM1 GUK1 PARP1 HSPD1 MSN IL2RG MTDH GSN ARID5B FOXO1 UBC ZFP36L2 MX1 HMHB1 RUNX1 G6PD LAPTM5 GBP4 IFI16 SPRY1 CITED2 YWHAZ TP53INP1 CCDC186 MCM7 PHB LDLRAD4 LGALS9 LIMS1 HNRNPF UBB FOS SMAD1 MGMT MZB1 JUNB BCL2L1 ISG20 MYD88 JUP CD34 LEMD3 UCP2 JUN AGTRAP CALR HCLS1 NRIP1 FUT7 SHISA2 NPM1 SOCS3 IFITM2 IL3RA IFITM1 GNG2 MT1X LAMTOR4 LITAF PPIA MME ARID5A HLA-DRB5 RCSD1 MT-ND3 TMSB4X HLA-E DDX3X IFI30 VAMP2 LTB HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PEG10 PSMB9 ETV5 IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 SMARCB1 C1QTNF4 PSME2 PSME1 IRF9"
8.97589262746867e-23,175,1635,2.20607259155725,"Immune response "," http://amigo.geneontology.org/amigo/term/GO:0006955"," CD99 CD79B BTK WAS DPEP1 CD74 VIM RNASET2 TNFRSF1B CYBA CYFIP2 GDI2 CNN2 SPI1 PKM RHOA RASGRP2 CLEC2D ACTB PAG1 HSP90AA1 MEF2C LAT2 FTL FKBP1A DYNLL1 EZR HSP90AB1 DDX17 XBP1 CTSG COCH PSMA6 NFKBIA RGCC CORO1A CCL17 PYCARD PRKD2 CD79A NOP53 SERPINE1 ICAM2 RAB5C HSPA8 MDK POU2AF1 ARPC3 GAPDH CCR6 LY86 FOXP1 ITGA4 SUMO1 NCF2 PRDX1 TNFAIP3 MYB YPEL5 ELF1 CXCR4 SASH3 HVCN1 ADGRE5 OPTN H2BC11 S1PR4 HCST CFP VPREB3 SEC14L1 BST2 ARPC1B GMFG CAP1 JCHAIN IL2RA OSTF1 ANXA1 LEF1 CD27 CMTM3 ARRB2 PMAIP1 IFITM3 CD53 ECM1 HSPD1 NFKBIZ PLAC8 IL2RG GSN UBC MX1 WASF2 HMHB1 RUNX1 S100A1 LAPTM5 GBP4 REL IFI16 SERINC5 YWHAZ CLEC4E YWHAB PHB FTH1 PTGDR LGALS9 VPREB1 UBB FOS BCL2L1 ISG20 MYD88 JUP CD34 TBC1D10C CD19 JUN FUCA1 FUT7 H2BC4 ATP6AP2 SOCS3 IFITM2 IFITM1 LAMP1 GNG2 SELL S100A13 PPIA MME ADA ARID5A SPTAN1 H2BC12 HLA-DRB5 CALM1 PNP HMGN2 RPL39 HLA-DQB1 HLA-C HLA-B HLA-E DDX3X IFI30 VAMP2 LTB HLA-A HLA-DRB1 HLA-DPA1 PSMB8 AIF1 LST1 HLA-DQA1 HLA-DPB1 HLA-DMA ARPC4 PSMB9 IKBKG MILR1 CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 LAIR1 HLA-DRA PSME2 PSME1 IRF9"
3.32217360885636e-22,191,1896,2.076321215354,"Cellular response to organic substance "," http://amigo.geneontology.org/amigo/term/GO:0071310"," SLC25A5 BTK WAS CD74 VIM TNFRSF1B CYBA CYFIP2 YBX3 GNA15 CNN2 SPI1 PKM RHOA ATP6AP1 PTPN18 ACTB SESN1 HSP90AA1 MEF2C AKR1B1 PPP1R15A FKBP1A DYNLL1 RPLP0 FLT3LG ITGA6 SEMA6A EZR HSP90AB1 RANBP1 DDX17 XBP1 RPL3 CTSG HIF1A SRSF5 TCL1A PSMA6 NFKBIA TIMP1 CORO1A CCL17 PYCARD PRKD2 BBC3 HSPB1 SERPINE1 TLE4 ENG SPOCK2 DDX5 GAB1 WFS1 HSPA8 PTGES3 ARPC3 GAPDH RIPOR2 SOD2 CCR6 CCND3 LY86 NR3C1 ATP6V0E1 FOXP1 ITGA4 IGFBP2 SUMO1 KDM5B TNFAIP3 MYB CCN2 DUSP1 ELF1 SOCS2 CXCR4 HNRNPA2B1 OPTN NR4A1 H2BC11 RNF113A FOSB ID1 KLF2 SMARCA4 GNAI1 ZFP36 SNX6 BST2 DNMT1 AKAP12 PTPRE H3-3B EDEM1 IL2RA ANXA1 HNRNPA1 MDM2 TRA2B KLF4 ATP6V1G1 ATIC LEF1 CD27 ARRB2 IFITM3 ECM1 PARP1 HSPD1 MSN IL2RG MTDH GSN ARID5B FOXO1 UBC ZFP36L2 MX1 HMHB1 RUNX1 LAPTM5 GBP4 IFI16 SPRY1 CITED2 YWHAZ TP53INP1 CCDC186 MCM7 PHB LDLRAD4 LGALS9 LIMS1 HNRNPF UBB FOS SMAD1 MGMT MZB1 JUNB BCL2L1 ISG20 MYD88 JUP LEMD3 UCP2 JUN AGTRAP CALR HCLS1 NRIP1 FUT7 SHISA2 NPM1 SOCS3 IFITM2 IL3RA IFITM1 GNG2 MT1X LAMTOR4 LITAF PPIA MME ARID5A HLA-DRB5 MT-ND3 TMSB4X HLA-E IFI30 VAMP2 LTB HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PEG10 PSMB9 IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 SMARCB1 C1QTNF4 PSME2 PSME1 IRF9"
3.34717499226776e-21,111,815,2.80714520090285,"Cellular response to cytokine stimulus "," http://amigo.geneontology.org/amigo/term/GO:0071345"," SLC25A5 BTK WAS CD74 VIM TNFRSF1B CYBA YBX3 CNN2 SPI1 RHOA PTPN18 HSP90AA1 RPLP0 FLT3LG HSP90AB1 XBP1 RPL3 CTSG HIF1A TCL1A PSMA6 NFKBIA TIMP1 CORO1A CCL17 PYCARD TLE4 SPOCK2 HSPA8 GAPDH RIPOR2 SOD2 CCR6 ITGA4 SUMO1 KDM5B TNFAIP3 DUSP1 ELF1 SOCS2 CXCR4 HNRNPA2B1 H2BC11 RNF113A KLF2 SMARCA4 ZFP36 BST2 AKAP12 IL2RA ANXA1 KLF4 ATIC LEF1 CD27 IFITM3 ECM1 HSPD1 MSN IL2RG GSN ARID5B FOXO1 UBC ZFP36L2 MX1 HMHB1 RUNX1 LAPTM5 GBP4 IFI16 YWHAZ PHB LGALS9 LIMS1 HNRNPF UBB FOS JUNB BCL2L1 ISG20 MYD88 HCLS1 SOCS3 IFITM2 IL3RA IFITM1 MT1X PPIA MME HLA-DRB5 TMSB4X HLA-E IFI30 LTB HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PSMB9 IKBKG CD24 B2M TNFRSF14 LILRB2 ALOX5 C1QTNF4 PSME2 PSME1 IRF9"
4.15795400402447e-21,116,881,2.71382343255877,"Response to cytokine "," http://amigo.geneontology.org/amigo/term/GO:0034097"," SLC25A5 BTK WAS CD74 VIM TNFRSF1B CYBA YBX3 CNN2 SPI1 RHOA PTPN18 HSP90AA1 RPLP0 IGBP1 FLT3LG HSP90AB1 XBP1 RPL3 CTSG HIF1A TCL1A PSMA6 NFKBIA TIMP1 CORO1A CCL17 PYCARD TLE5 TLE4 SPOCK2 HSPA8 GAPDH RIPOR2 SOD2 CCR6 ITGA4 SUMO1 KDM5B TNFAIP3 DUSP1 ELF1 SOCS2 CXCR4 HNRNPA2B1 H2BC11 RNF113A KLF2 SMARCA4 ZFP36 BST2 AKAP12 IL2RA ANXA1 KLF4 ATIC LEF1 CD27 IFITM3 ECM1 HSPD1 MSN IL2RG GSN ARID5B FOXO1 UBC ZFP36L2 MX1 HMHB1 RUNX1 LAPTM5 GBP4 REL IFI16 YWHAZ PHB LGALS9 LIMS1 HNRNPF UBB FOS JUNB BCL2L1 ISG20 MYD88 TYMS JUN HCLS1 SOCS3 IFITM2 IL3RA IFITM1 MT1X PPIA MME HLA-DRB5 TMSB4X HLA-E IFI30 LTB HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PSMB9 IKBKG CD24 B2M TNFRSF14 LILRB2 ALOX5 C1QTNF4 PSME2 PSME1 IRF9"
6.2297677237476e-18,177,1844,1.97838976645092,"Response to external stimulus "," http://amigo.geneontology.org/amigo/term/GO:0009605"," CD9 BTK WAS DPEP1 CD74 VIM RNASET2 TNFRSF1B CYBA CNN2 SPI1 RHOA ENO1 SESN1 HSP90AA1 MEF2C EFNB1 SEMA6A CAPN3 EZR HSP90AB1 DDX17 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA CORO1A CCL17 PYCARD TLE5 PRKD2 NOP53 SCN1B CDK6 HSPB1 NUDT1 SERPINE1 ENG ICAM2 GAB1 HSPA8 MDK POU2AF1 GAPDH RIPOR2 SOD2 CCR6 LY86 ATP6V0E1 FOXP1 ITGA4 SPTBN1 IGFBP2 ID2 SUMO1 NCF2 GADD45A PRDX1 STMN1 TNFAIP3 DUSP1 CXCR4 NPY OPTN NR4A1 H2BC11 FOSB VASP CFP ZFP36 CDKN2D SEC14L1 BST2 MPP1 AKAP12 JCHAIN PEMT TPT1 SLC38A2 IL2RA STK26 ANXA1 NT5E HNRNPA1 MDM2 KLF4 ATP6V1G1 CCN3 HMGA1 CREBZF LEF1 CMTM3 CMTM2 MAP1LC3B ARRB2 PMAIP1 IFITM3 HSPD1 NFKBIZ PLAC8 MTDH GSN FOXO1 UBC BANK1 CMTM7 MX1 G6PD GBP4 REL IFI16 SERINC5 CITED2 CCDC186 CLEC4E PHB PTGDR LGALS9 CRADD CMTM8 UBB FOS MGMT BCL2L1 ISG20 MYD88 JUP CD34 UCP2 TYMS JUN CALR FUT7 H2BC4 SOCS3 IFITM2 IFITM1 LAMP1 GNG2 LAMTOR4 LITAF PPIA EVL ADA ARID5A WDR45 SPTAN1 H2BC12 HLA-DRB5 CALM1 MT-CYB HMGN2 RPL39 TMSB4X HLA-E DDX3X IFI30 HLA-DPA1 PSMB8 GPSM3 AIF1 HLA-DPB1 PSMB9 EEF1G TXNIP IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 PSME2 PSME1 IRF9"
1.89930803718134e-17,242,2936,1.69886481568988,"Regulation of response to stimulus "," http://amigo.geneontology.org/amigo/term/GO:0048583"," CD99 SLC25A5 CD79B CD9 BTK WAS DPEP1 CD74 TNFRSF1B ARAP2 CYBA CYFIP2 YBX3 HDAC7 SPI1 RHOA CLEC2D ATP6AP1 PTPN18 ENO1 ACTB PAG1 SESN1 HSP90AA1 MEF2C P2RX5 AKR1B1 LAT2 PPP1R15A FKBP1A PEBP1 FUS IGBP1 ARHGAP4 YPEL3 FLT3LG ITGA6 SEMA6A CAPN3 EZR HSP90AB1 TSPAN15 PIK3IP1 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA SMCHD1 RBBP7 TIMP1 ARHGEF7 RGCC CCL17 PYCARD TLE5 PRKD2 BBC3 DENND3 CD79A NOP53 CDK6 HSPB1 SERPINE1 TLE4 ENG ICAM2 DDX5 GAB1 WFS1 FBXW7 HSPA8 MDK PTGES3 ARPC3 ARHGDIB RIPOR2 SOD2 CCR6 CCND3 LY86 FOXP1 ITGA4 SPTBN1 IGFBP2 SUMO1 GADD45A PRDX1 STMN1 TNFAIP3 MYB CCN2 DUSP1 ELF1 SOCS2 CXCR4 SASH3 RPL5 NPY OPTN H2BC11 DEK RNF113A HCST PRMT1 CFP KLF2 MACF1 SMARCA4 GNAI1 ZFP36 CDKN2D SNX6 SEC14L1 BST2 ARPC1B MPP1 AKAP12 PTPRE H3-3B TERF2 PCNA TPT1 SLC38A2 IL2RA STK26 ANXA1 NT5E MDM2 KLF4 CCN3 HMGA1 ARHGAP24 LEF1 CD27 DUSP6 LPAR6 CMTM3 ARRB2 PMAIP1 ECM1 PARP1 HSPD1 NFKBIZ MTDH CDKN2A FOXO1 UBC UTRN BANK1 WASF2 RCAN1 RUNX1 G6PD S100A1 UBXN1 LAPTM5 USP1 REL IFI16 SPRY1 CITED2 YWHAZ TP53INP1 CLEC4E YWHAB PHB CDT1 PTGDR LDLRAD4 LGALS9 NSMCE1 CRADD LIMS1 UBB FOS SMAD1 MGMT MZB1 BCL2L1 RGS19 EGFL7 MYD88 JUP CD34 LEMD3 RALGAPA1 TBC1D10C UCP2 GNG7 CD19 JUN CALR MYADM HCLS1 FUT7 SHISA2 NPM1 ATP6AP2 SOCS3 BCAP31 IFITM1 LAMP1 GNG2 LAMTOR4 SELL LITAF S100A13 S100A4 PPIA TLE1 ADA STMN3 HLA-DRB5 CALM1 ADGRG1 TMSB4X HLA-E TAX1BP3 DDX3X HLA-DPA1 PSMB8 GPSM3 AIF1 HLA-DPB1 ARPC4 PEG10 PSMB9 IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 PTP4A3 SMARCB1 LAIR1 C1QTNF4 PSME2 PSME1"
2.71818162490051e-16,130,1206,2.2217518566003,"Regulation of immune system process "," http://amigo.geneontology.org/amigo/term/GO:0002682"," CD99 CD79B CD9 BTK WAS DPEP1 CD74 TNFRSF1B CYBA CYFIP2 SPI1 RHOA CLEC2D ATP6AP1 TCF3 ACTB PAG1 HSP90AA1 MEF2C LAT2 FKBP1A EFNB1 EZR HSP90AB1 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA MYL9 RGCC CORO1A PYCARD PRKD2 CD79A NOP53 CDK6 SERPINE1 ICAM2 FBXW7 MDK ARPC3 RIPOR2 CCR6 FOXP1 ITGA4 IGFBP2 ID2 SUMO1 TNFAIP3 MYB DUSP1 ELF1 SASH3 PRXL2A HCST PRMT1 CFP ZFP36 SEC14L1 BST2 ARPC1B MPP1 H3-3B IL2RA ANXA1 CCN3 LEF1 CD27 CMTM3 ARRB2 ECM1 HSPD1 NFKBIZ MSN CDKN2A UBC ZFP36L2 BANK1 TSC22D3 WASF2 RUNX1 S100A1 LAPTM5 IFI16 CLEC4E PHB PTGDR LGALS9 UBB FOS MZB1 MYD88 CD34 TBC1D10C CD19 JUN CALR HCLS1 FUT7 SOCS3 EVI2B IFITM1 LAMP1 GNG2 SELL ADA HLA-DRB5 CALM1 PNP HLA-E DDX3X HLA-DPA1 PSMB8 AIF1 LST1 HLA-DPB1 ARPC4 PSMB9 IKBKG MILR1 CD24 B2M TNFRSF14 LILRA2 LILRB2 LAIR1 PSME2 PSME1"
2.51187451826867e-15,121,1111,2.24476467095656,"Cell activation "," http://amigo.geneontology.org/amigo/term/GO:0001775"," SLC25A5 CD79B CD9 BTK WAS CD74 RNASET2 TNFRSF1B CYBA GDI2 GNA15 CNN2 SPI1 KLF6 PKM RHOA TCF3 ACTB PAG1 HSP90AA1 MEF2C LAT2 FTL FKBP1A DYNLL1 IGBP1 EFNB1 CAPN3 HSP90AB1 XBP1 CTSG MYL9 TIMP1 RGCC CORO1A PYCARD CD79A HSPB1 RAB5C HSPA8 MDK POU2AF1 RIPOR2 CCR6 CCND3 FOXP1 ITGA4 IGFBP2 ID2 PRDX1 TNFAIP3 MYB CCN2 YPEL5 SASH3 HVCN1 ADGRE5 CFP GNAI1 BST2 GMFG AKAP12 CAP1 IL2RA OSTF1 ANXA1 LEF1 CD27 ARRB2 CD53 HSPD1 NFKBIZ PLAC8 MSN CDKN2A GSN ZFP36L2 BANK1 CMTM7 RUNX1 LAPTM5 YWHAZ PHB FTH1 PTGDR LGALS9 MZB1 CTPS1 MYD88 JUP TBC1D10C CD19 FUCA1 FUT7 ATP6AP2 IKZF1 LAMP1 GNG2 SELL S100A13 PPIA MME ADA SPTAN1 PNP HLA-E DDX3X VAMP2 CLIC1 HLA-DPA1 AIF1 LST1 HLA-DPB1 MILR1 CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 LAIR1"
7.40842033573801e-15,182,2058,1.82274335990474,"Cell surface receptor signaling pathway "," http://amigo.geneontology.org/amigo/term/GO:0007166"," CD79B BTK WAS CD74 VIM TNFRSF1B CYBA CYFIP2 CNN2 SPI1 RHOA CLEC2D ATP6AP1 PTPN18 ACTB PAG1 HSP90AA1 MEF2C P2RX5 AKR1B1 LAT2 FKBP1A RPLP0 FLT3LG EFNB1 ITGA6 SEMA6A EZR HSP90AB1 TSPAN15 XBP1 CTSG HIF1A PSMA6 NFKBIA TIMP1 ARHGEF7 CORO1A CCL17 PYCARD TLE5 PRKD2 CD79A HSPB1 SERPINE1 TLE4 ENG PFN1 ICAM2 DDX5 GAB1 FBXW7 HSPA8 MDK ARPC3 SOD2 CCR6 CCND3 LY86 ATP6V0E1 FOXP1 ITGA4 SPTBN1 IGFBP2 SUMO1 NCF2 GADD45A STMN1 TNFAIP3 CCN2 ELF1 SOCS2 CXCR4 HNRNPA2B1 ADGRE5 H2BC11 RNF113A ID1 PRMT1 MACF1 SMARCA4 SNX6 BST2 ARPC1B PTPRE IL2RA ANXA1 NT5E HNRNPA1 AHI1 KLF4 ATP6V1G1 CCN3 LEF1 CD27 CMTM3 ARRB2 PMAIP1 IFITM3 ECM1 PARP1 CSRNP1 NFKBIZ KDM6A MSN IL2RG MTDH ARID5B FOXO1 UBC ADGRF1 MX1 WASF2 RUNX1 LAPTM5 SPRY1 CITED2 YWHAZ CLEC4E PHB LDLRAD4 CRADD LIMS1 HNRNPF UBB FOS SMAD1 MZB1 MTSS1 JUNB BCL2L1 ISG20 EGFL7 MYD88 JUP LEMD3 CLEC14A CD19 JUN HCLS1 FUT7 SHISA2 ATP6AP2 SOCS3 SIVA1 IFITM2 IL3RA IFITM1 GNG2 PPIA EVL TLE1 ADA ANXA4 HLA-DRB5 CALM1 ADGRG1 TMSB4X HLA-E TAX1BP3 DDX3X IFI30 LTB HLA-DPA1 PSMB8 HLA-DPB1 ARPC4 PEG10 PSMB9 TXNIP IKBKG MILR1 CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 C1QTNF4 PSME2 PSME1 IRF9"
1.32715387560095e-13,230,2934,1.61572421473387,"Response to stress "," http://amigo.geneontology.org/amigo/term/GO:0006950"," CD9 BTK WAS DPEP1 CD74 VIM RNASET2 TNFRSF1B CYBA YBX3 GNA15 CNN2 NTHL1 SPI1 RHOA ENO1 ACTB CAPZB UBE2A SESN1 HSP90AA1 MEF2C P2RX5 ATRX AKR1B1 PPP1R15A FUS IGBP1 YPEL3 CAPN3 EZR HSP90AB1 CIRBP HPS4 DDX17 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA MYL9 SMCHD1 RBBP7 PGK1 TIMP1 RGCC CORO1A CCL17 PYCARD BBC3 NOP53 CDK6 HSPB1 NUDT1 SERPINE1 ENG PDLIM1 DNTT ICAM2 DDX5 WFS1 FBXW7 HSPA8 MDK PTGES3 GAPDH MAN1A1 SOD2 MCM3 CCR6 LY86 ATP6V0E1 FOXP1 ID2 SUMO1 NCF2 GADD45A PRDX1 TNFAIP3 MYB CCN2 DUSP1 CXCR4 ADGRE5 OPTN H2BC11 DEK RNF113A PRMT1 CFP KLF2 MACF1 GNAI1 ZFP36 CDKN2D SEC14L1 BST2 UBA1 JCHAIN H3-3B TERF2 PCNA TPT1 BTG1 EDEM1 SLC38A2 IL2RA STK26 ANXA1 NT5E HNRNPA1 MDM2 TRA2B KLF4 ATP6V1G1 CCN3 HMGA1 ARHGAP24 CD27 DUSP6 MAP1LC3B ARRB2 PMAIP1 IFITM3 ECM1 PARP1 HSPD1 NFKBIZ PLAC8 CDKN2A GSN FOXO1 UBC ZFP36L2 INO80C UBE2L6 TSC22D3 MX1 RCAN1 G6PD UBXN1 LAPTM5 USP1 GBP4 FCMR REL STT3B IFI16 SERINC5 CITED2 YWHAZ TP53INP1 MCM7 CLEC4E TSC22D4 PHB PTGDR LGALS9 NSMCE1 CRADD CENPX UBB FOS SMAD1 MGMT MTSS1 BCL2L1 JMJD1C ISG20 MYD88 CD34 POLD4 UCP2 JUN AGTRAP CALR FUT7 H2BC4 NPM1 SOCS3 IFITM2 BCAP31 IFITM1 LAMP1 GNG2 MT1X LAMTOR4 PPIA ADA ARID5A WDR45 H2BC12 HLA-DRB5 MT-CO2 MT-CYB RCSD1 MT-CO1 MT-ND3 MT-ND4 MT-ND1 MT-ATP6 RPL39 TMSB4X HLA-E VDAC1 DDX3X IFI30 CLIC1 HLA-DPA1 PSMB8 GPSM3 AIF1 HLA-DPB1 PSMB9 ETV5 TXNIP IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 UHRF1 PSME2 TINF2 PSME1 IRF9 LSP1"
1.62821770091781e-13,109,1004,2.23765069382115,"Response to biotic stimulus "," http://amigo.geneontology.org/amigo/term/GO:0009607"," BTK WAS CD74 VIM RNASET2 TNFRSF1B CYBA SPI1 RHOA ENO1 HSP90AA1 MEF2C HSP90AB1 DDX17 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA CORO1A CCL17 PYCARD NOP53 CDK6 HSPB1 SERPINE1 ICAM2 WFS1 POU2AF1 GAPDH SOD2 LY86 FOXP1 SUMO1 NCF2 PRDX1 STMN1 TNFAIP3 CXCR4 OPTN NR4A1 H2BC11 CFP ZFP36 SEC14L1 BST2 AKAP12 JCHAIN TPT1 ANXA1 KLF4 HMGA1 CREBZF ARRB2 PMAIP1 IFITM3 HSPD1 PLAC8 MTDH GSN UBC BANK1 MX1 LAPTM5 GBP4 REL IFI16 SERINC5 CCDC186 CLEC4E PHB LGALS9 UBB FOS BCL2L1 ISG20 MYD88 JUN H2BC4 SOCS3 IFITM2 IFITM1 LAMP1 LITAF ARID5A H2BC12 HLA-DRB5 HMGN2 RPL39 HLA-E DDX3X IFI30 HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PSMB9 EEF1G TXNIP IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 PSME2 PSME1 IRF9"
3.93287490532881e-13,115,1103,2.14892785404768,"Biological process involved in interspecies interaction between organisms "," http://amigo.geneontology.org/amigo/term/GO:0044419"," BTK WAS CD74 VIM RNASET2 TNFRSF1B CYBA SPI1 RHOA ENO1 HSP90AA1 MEF2C HSP90AB1 DDX17 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA PGK1 CORO1A CCL17 PYCARD SLC1A5 NOP53 CDK6 HSPB1 SERPINE1 ICAM2 HSPA8 POU2AF1 GAPDH SOD2 LY86 FOXP1 SUMO1 NCF2 PRDX1 STMN1 TNFAIP3 CXCR4 OPTN NR4A1 H2BC11 CFP SMARCA4 ZFP36 SEC14L1 BST2 AKAP12 JCHAIN TPT1 ANXA1 HMGA1 CREBZF LEF1 ARRB2 PMAIP1 IFITM3 HSPD1 PLAC8 MTDH GSN UBC BANK1 MX1 GBP4 REL IFI16 SERINC5 CCDC186 CLEC4E PHB RPSA LGALS9 UBB FOS BCL2L1 ISG20 MYD88 TYMS JUN H2BC4 SOCS3 SIVA1 IFITM2 IFITM1 LAMP1 LITAF PPIA ARID5A H2BC12 HLA-DRB5 HMGN2 RPL39 HLA-E DDX3X IFI30 HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PSMB9 EEF1G IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 SMARCB1 PSME2 PSME1 IRF9"
7.92243852591006e-13,105,973,2.2242109068013,"Response to external biotic stimulus "," http://amigo.geneontology.org/amigo/term/GO:0043207"," BTK WAS CD74 VIM RNASET2 TNFRSF1B CYBA SPI1 RHOA ENO1 HSP90AA1 MEF2C HSP90AB1 DDX17 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA CORO1A CCL17 PYCARD NOP53 CDK6 HSPB1 SERPINE1 ICAM2 POU2AF1 GAPDH SOD2 LY86 FOXP1 SUMO1 NCF2 PRDX1 STMN1 TNFAIP3 CXCR4 OPTN NR4A1 H2BC11 CFP ZFP36 SEC14L1 BST2 AKAP12 JCHAIN TPT1 ANXA1 HMGA1 CREBZF ARRB2 PMAIP1 IFITM3 HSPD1 PLAC8 MTDH GSN UBC BANK1 MX1 GBP4 REL IFI16 SERINC5 CCDC186 CLEC4E PHB LGALS9 UBB FOS BCL2L1 ISG20 MYD88 JUN H2BC4 SOCS3 IFITM2 IFITM1 LAMP1 LITAF ARID5A H2BC12 HLA-DRB5 HMGN2 RPL39 HLA-E DDX3X IFI30 HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PSMB9 EEF1G IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 PSME2 PSME1 IRF9"
7.92243852591006e-13,105,973,2.2242109068013,"Response to other organism "," http://amigo.geneontology.org/amigo/term/GO:0051707"," BTK WAS CD74 VIM RNASET2 TNFRSF1B CYBA SPI1 RHOA ENO1 HSP90AA1 MEF2C HSP90AB1 DDX17 XBP1 CTSG COCH HIF1A PSMA6 NFKBIA CORO1A CCL17 PYCARD NOP53 CDK6 HSPB1 SERPINE1 ICAM2 POU2AF1 GAPDH SOD2 LY86 FOXP1 SUMO1 NCF2 PRDX1 STMN1 TNFAIP3 CXCR4 OPTN NR4A1 H2BC11 CFP ZFP36 SEC14L1 BST2 AKAP12 JCHAIN TPT1 ANXA1 HMGA1 CREBZF ARRB2 PMAIP1 IFITM3 HSPD1 PLAC8 MTDH GSN UBC BANK1 MX1 GBP4 REL IFI16 SERINC5 CCDC186 CLEC4E PHB LGALS9 UBB FOS BCL2L1 ISG20 MYD88 JUN H2BC4 SOCS3 IFITM2 IFITM1 LAMP1 LITAF ARID5A H2BC12 HLA-DRB5 HMGN2 RPL39 HLA-E DDX3X IFI30 HLA-DPA1 PSMB8 AIF1 HLA-DPB1 PSMB9 EEF1G IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 PSME2 PSME1 IRF9"
2.93220077743464e-12,106,1007,2.16958116523074,"Leukocyte activation "," http://amigo.geneontology.org/amigo/term/GO:0045321"," SLC25A5 CD79B BTK WAS CD74 RNASET2 TNFRSF1B CYBA GDI2 CNN2 SPI1 KLF6 PKM RHOA TCF3 PAG1 HSP90AA1 MEF2C LAT2 FTL FKBP1A DYNLL1 IGBP1 EFNB1 HSP90AB1 XBP1 CTSG CORO1A PYCARD CD79A RAB5C HSPA8 MDK POU2AF1 RIPOR2 CCR6 CCND3 FOXP1 ITGA4 IGFBP2 ID2 PRDX1 TNFAIP3 MYB YPEL5 SASH3 HVCN1 ADGRE5 CFP BST2 GMFG CAP1 IL2RA OSTF1 ANXA1 LEF1 CD27 CD53 HSPD1 NFKBIZ PLAC8 MSN CDKN2A GSN ZFP36L2 BANK1 CMTM7 RUNX1 LAPTM5 PHB FTH1 PTGDR LGALS9 MZB1 CTPS1 MYD88 JUP TBC1D10C CD19 FUCA1 FUT7 ATP6AP2 IKZF1 LAMP1 SELL S100A13 PPIA MME ADA SPTAN1 PNP HLA-E DDX3X VAMP2 HLA-DPA1 AIF1 LST1 HLA-DPB1 MILR1 CD24 B2M TNFRSF14 LILRA2 LILRB2 ALOX5 LAIR1"
3.541292358779e-12,93,829,2.31221346137679,"Positive regulation of gene expression "," http://amigo.geneontology.org/amigo/term/GO:0010628"," CD74 VIM CYBA YBX3 CNN2 SPI1 PKM RHOA ENO1 ACTB HSP90AA1 MEF2C PPP1R15A FUS EZR CIRBP XBP1 HIF1A SRSF5 RBM3 RGCC PYCARD EIF3E PRKD2 HSPB1 SERPINE1 ENG DDX5 HSPA8 MDK POU2AF1 GAPDH FOXP1 SPTBN1 PASK ID2 KDM5B MYB CCN2 SASH3 RPL5 DEK ID1 ZFP36 EIF2S3 DNMT1 AKAP12 TERF2 SLC38A2 ANXA1 MDM2 TRA2B KLF4 LEF1 ARRB2 HSPD1 KDM6A MSN CDKN2A GSN HMHB1 RUNX1 LAPTM5 IFI16 CITED2 TP53INP1 TAF1D PHB LGALS9 CNBP LIMS1 SMAD1 MZB1 MYD88 CD34 CALR NPM1 ATP6AP2 S100A13 TLE1 RPS4X PNP HLA-E DDX3X LTB HLA-DPA1 AIF1 HLA-DPB1 B2M TNFRSF14 LILRA2 LILRB2 C1QTNF4"
3.89211295484161e-12,204,2573,1.63414236230749,"Regulation of biological quality "," http://amigo.geneontology.org/amigo/term/GO:0065008"," SLC25A5 MCUB CD9 BTK WAS CD74 VIM TNFRSF1B TMSB10 CYBA CYFIP2 YBX3 GNA15 SPI1 RHOA ATP1B3 ATP6AP1 HSD17B10 ACTB CAPZB HSP90AA1 MEF2C P2RX5 RPS5 AKR1B1 PPP1R15A FTL FKBP1A DYNLL1 FUS ARHGAP4 SEMA6A CAPN3 EZR HSP90AB1 CIRBP HPS4 XBP1 CTSG COCH HIF1A TCL1A PSMA6 MYL9 ARHGEF7 CORO1A PYCARD BBC3 NOP53 SCN1B CDK6 HSPB1 SERPINE1 ENG PFN1 WFS1 FBXW7 HSPA8 ATP5F1B PTGES3 ARPC3 GAPDH SOD2 CCR6 ATP6V0E1 DBN1 SPTBN1 PASK ID2 SUMO1 NCF2 KDM5B PRDX1 STMN1 TNFAIP3 MYB CCN2 MYL12B SOCS2 PPP3CC CXCR4 SASH3 RPL5 NPY HVCN1 VASP S1PR4 ID1 PRMT1 KLF2 MACF1 SMARCA4 GNAI1 ZFP36 LSM7 QRSL1 ARPC1B EIF2S3 GMFG AKAP12 STARD3 JCHAIN H3-3B PEMT TPT1 YWHAQ IL2RA ANXA1 MDM2 TRA2B BIN1 ATP6V1G1 ARPC5L CCN3 GCHFR LPAR6 PCTP SSH2 ARRB2 PMAIP1 SERBP1 PARP1 CALM2 HSPD1 PLAC8 MSN CDKN2A GSN FOXO1 UBC FLI1 ZFP36L2 CARHSP1 TSC22D3 MX1 WASF2 CALM3 G6PD LAPTM5 CDC42EP3 CITED2 YWHAZ ALDH1A1 CCDC186 YWHAB PHB TUBA1A FTH1 PTGDR LGALS9 CD52 CNBP UBB SMAD1 CYTL1 BCL2L1 JMJD1C MYD88 GLRX JUP CD34 UCP2 CD19 JUN AGTRAP CALR MYADM HCLS1 NPM1 ATP6AP2 SIVA1 IKZF1 BCAP31 LAMP1 GNG2 MT1X LAMTOR4 S100A13 PPIA EVL MME ADA SPTAN1 CALM1 MT-CO2 GDI1 ADGRG1 TMSB4X DDX3X IFI30 VAMP2 CLIC1 PSMB8 LST1 ARPC4 PSMB9 CD24 B2M LILRB2 ALOX5 SMARCB1 PSME2 PSME1 NEDD8"
4.47239389365833e-12,74,583,2.61615018723364,"Cytokine-mediated signaling pathway "," http://amigo.geneontology.org/amigo/term/GO:0019221"," CD74 VIM TNFRSF1B CNN2 SPI1 HSP90AA1 RPLP0 FLT3LG HSP90AB1 CTSG HIF1A PSMA6 NFKBIA TIMP1 CCL17 PYCARD HSPA8 SOD2 CCR6 SUMO1 TNFAIP3 ELF1 SOCS2 CXCR4 HNRNPA2B1 H2BC11 RNF113A SMARCA4 BST2 IL2RA ANXA1 CD27 IFITM3 ECM1 MSN IL2RG FOXO1 UBC MX1 RUNX1 LAPTM5 YWHAZ LIMS1 HNRNPF UBB FOS JUNB BCL2L1 ISG20 MYD88 SOCS3 IFITM2 IL3RA IFITM1 PPIA HLA-DRB5 TMSB4X HLA-E IFI30 LTB HLA-DPA1 PSMB8 HLA-DPB1 PSMB9 IKBKG CD24 B2M TNFRSF14 LILRB2 ALOX5 C1QTNF4 PSME2 PSME1 IRF9"
4.52494767120436e-12,84,713,2.42822688619093,"Positive regulation of immune system process "," http://amigo.geneontology.org/amigo/term/GO:0002684"," CD99 CD79B BTK WAS CD74 CYBA CYFIP2 SPI1 RHOA ATP6AP1 TCF3 ACTB PAG1 HSP90AA1 MEF2C LAT2 EFNB1 EZR HSP90AB1 XBP1 CTSG COCH PSMA6 RGCC CORO1A PYCARD PRKD2 CD79A SERPINE1 ICAM2 MDK ARPC3 RIPOR2 CCR6 FOXP1 ITGA4 IGFBP2 ID2 MYB ELF1 SASH3 CFP ARPC1B IL2RA ANXA1 LEF1 CD27 CMTM3 HSPD1 NFKBIZ WASF2 RUNX1 LAPTM5 IFI16 CLEC4E PHB LGALS9 FOS MZB1 MYD88 CD19 JUN CALR HCLS1 EVI2B LAMP1 ADA HLA-DRB5 PNP HLA-E HLA-DPA1 PSMB8 AIF1 HLA-DPB1 ARPC4 PSMB9 IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 PSME2 PSME1"
8.89358758707407e-12,114,1144,2.05389545624554,"Response to oxygen-containing compound "," http://amigo.geneontology.org/amigo/term/GO:1901700"," CD9 BTK DPEP1 VIM TNFRSF1B CYBA GNA15 SPI1 PKM RHOA ATP6AP1 ACTB SESN1 MEF2C P2RX5 AKR1B1 DYNLL1 EZR HSP90AB1 RANBP1 XBP1 CTSG HIF1A SRSF5 NFKBIA TIMP1 PYCARD SERPINE1 DNTT GAB1 MAN1A1 SOD2 CCND3 LY86 NR3C1 ATP6V0E1 FOXP1 ITGA4 IGFBP2 PRDX1 TNFAIP3 MYB CCN2 DUSP1 SOCS2 NR4A1 FOSB ID1 KLF2 SMARCA4 GNAI1 ZFP36 CDKN2D SNX6 ARPC1B DNMT1 AKAP12 PTPRE PCNA NASP PEMT BTG1 EDEM1 SLC38A2 STK26 ANXA1 MDM2 TRA2B KLF4 ATP6V1G1 CD27 ARRB2 PARP1 HSPD1 MSN MTDH GSN FOXO1 CALM3 G6PD TP53INP1 CCDC186 CDT1 LGALS9 FOS MGMT MZB1 BCL2L1 RGS19 MYD88 JUP RPL15 UCP2 TYMS JUN AGTRAP CALR NRIP1 FUT7 SOCS3 GNG2 LAMTOR4 LITAF ADA CALM1 MT-CYB RPL10A MT-ND4 MT-ND1 VAMP2 TXNIP LILRA2 LILRB2 SMARCB1"
5.83101433811234e-11,122,1297,1.93873906746525,"Cell population proliferation "," http://amigo.geneontology.org/amigo/term/GO:0008283"," SLC25A5 CD9 BTK CD74 TNFRSF1B CYBA CNN2 RHOA TCF3 UBE2A MEF2C AKR1B1 FLT3LG EFNB1 XBP1 HIF1A TCL1A NFKBIA TIMP1 BEX4 RGCC CORO1A PYCARD PRKD2 CD79A CLEC11A CDK6 ENG FBXW7 MDK PTGES3 RIPOR2 SOD2 CCND3 DBN1 FOXP1 ITGA4 IGFBP2 ID2 KDM5B PRDX1 TNFAIP3 MYB CCN2 CCND2 YPEL5 SASH3 NR4A1 ID1 PRMT1 ZFP36 CDKN2D BST2 DNMT1 PEMT BTG1 IL2RA ANXA1 MDM2 KLF4 FPGS CCN3 HMGA1 LEF1 ECM1 HSPD1 PLAC8 MSN CDKN2A RUNX1 SPRY1 CITED2 TP53INP1 MCM7 PHB FTH1 SF1 LGALS9 CNBP SMAD1 MZB1 MTSS1 JUNB BCL2L1 CTPS1 EGFL7 MYD88 JUP CD34 MARCKSL1 CD19 JUN CALR HCLS1 H2AC6 NPM1 IDH2 PRKX IFITM1 GNG2 NAP1L1 S100A13 ADA RPS4X PNP ADGRG1 HLA-E TAX1BP3 IFI30 HLA-DPA1 AIF1 LST1 HLA-DPB1 ETV5 TXNIP CD24 B2M TNFRSF14 LILRB2 ALOX5 UHRF1 TINF2"
8.85770013021518e-11,140,1586,1.81938395318845,"Cell death "," http://amigo.geneontology.org/amigo/term/GO:0008219"," SLC25A5 BTK DPEP1 CD74 TNFRSF1B CYFIP2 YBX3 SPI1 PKM RHOA ENO1 HSP90AA1 MEF2C AKR1B1 PPP1R15A DYNLL1 IGBP1 ITGA6 SEMA6A CAPN3 HSP90AB1 XBP1 CTSG HIF1A TCL1A NFKBIA TIMP1 ARHGEF7 RGCC CORO1A PYCARD TLE5 PRKD2 BBC3 NOP53 HSPB1 SERPINE1 DDX5 WFS1 FBXW7 MDK GAPDH SOD2 NR3C1 FOXP1 ITGA4 GADD45A TNFAIP3 MYB CCN2 CCND2 DUSP1 SOCS2 PPP3CC CXCR4 OPTN NR4A1 ID1 UXT ZFP36 CDKN2D SNX6 DNMT1 AKAP12 TPT1 BTG1 YWHAQ IL2RA STK26 ANXA1 AHI1 MDM2 BIN1 KLF4 CCN3 LEF1 CD27 DUSP6 ARRB2 PMAIP1 EMP3 SERBP1 PARP1 HSPD1 CSRNP1 PLAC8 RPL10 MTDH CDKN2A GSN FOXO1 UBC TSC22D3 MX1 G6PD LAPTM5 FCMR REL IFI16 SPRY1 CITED2 YWHAZ TP53INP1 YWHAB PHB LGALS9 SLC25A6 CRADD UBB FOS MGMT MZB1 BCL2L1 MYD88 JUP CD34 UCP2 JUN CALR HCLS1 NPM1 SOCS3 SIVA1 BCAP31 EVI2B LAMP1 FHIT PPIA TLE1 ADA ANXA4 MT-CO2 VDAC1 DDX3X DDAH2 PEG10 TXNIP IKBKG CD24 CHMP4A"
9.0865107374839e-11,108,1096,2.03101302511564,"Regulation of cell population proliferation "," http://amigo.geneontology.org/amigo/term/GO:0042127"," SLC25A5 CD9 BTK CD74 TNFRSF1B CYBA CNN2 RHOA TCF3 UBE2A MEF2C AKR1B1 FLT3LG EFNB1 XBP1 HIF1A TCL1A NFKBIA TIMP1 BEX4 RGCC CORO1A PYCARD PRKD2 CLEC11A CDK6 ENG FBXW7 MDK RIPOR2 SOD2 CCND3 FOXP1 ITGA4 IGFBP2 ID2 KDM5B TNFAIP3 MYB CCN2 CCND2 SASH3 NR4A1 ID1 PRMT1 ZFP36 CDKN2D BST2 DNMT1 PEMT BTG1 IL2RA ANXA1 MDM2 KLF4 CCN3 HMGA1 LEF1 ECM1 PLAC8 CDKN2A RUNX1 SPRY1 CITED2 TP53INP1 PHB FTH1 SF1 LGALS9 CNBP SMAD1 MZB1 MTSS1 JUNB BCL2L1 EGFL7 MYD88 JUP MARCKSL1 JUN CALR HCLS1 H2AC6 NPM1 IDH2 IFITM1 NAP1L1 S100A13 ADA RPS4X PNP ADGRG1 HLA-E TAX1BP3 IFI30 HLA-DPA1 AIF1 LST1 HLA-DPB1 ETV5 TXNIP CD24 B2M TNFRSF14 LILRB2 ALOX5 UHRF1 TINF2"
9.92592541752269e-11,172,2113,1.67775467297067,"Intracellular signal transduction "," http://amigo.geneontology.org/amigo/term/GO:0035556"," BTK WAS CD74 TNFRSF1B ARAP2 GDI2 YBX3 HDAC7 SPI1 RHOA RASGRP2 ATP6AP1 ENO1 PAG1 SESN1 HSP90AA1 MEF2C P2RX5 ATRX AKR1B1 LAT2 PPP1R15A FKBP1A PEBP1 IGBP1 ARHGAP4 FLT3LG SEMA6A CAPN3 EZR HSP90AB1 PIK3IP1 XBP1 HIF1A PSMA6 NFKBIA RBBP7 ARHGEF7 RGCC CCL17 PYCARD PRKD2 BBC3 DENND3 NOP53 HSPB1 ENG RASD1 DDX5 WSB1 GAB1 WFS1 FBXW7 ARHGDIB RIPOR2 SOD2 CCR6 SPTBN1 PASK GADD45A PRDX1 STMN1 TNFAIP3 CCN2 CTNNAL1 DUSP1 SOCS2 PPP3CC CXCR4 RPL5 OPTN HCST PRMT1 GNAI1 ZFP36 CDKN2D SEC14L1 BST2 DNMT1 AKAP12 CAP1 SH3BP5 PCNA TPT1 YWHAQ IL2RA MDM2 KLF4 CCN3 ARHGAP24 CD27 DUSP6 LPAR6 ARRB2 PMAIP1 ECM1 PARP1 CALM2 IL2RG MTDH CDKN2A GSN FOXO1 UBC ZFP36L2 STK32B CARHSP1 BANK1 WASF2 RCAN1 CALM3 S100A1 LAPTM5 REL CDC42EP3 IFI16 SPRY1 YWHAZ TP53INP1 YWHAB PHB CDT1 LGALS9 NSMCE1 CRADD LIMS1 UBB SMAD1 BCL2L1 RGS19 MYD88 NUDT4 LEMD3 RALGAPA1 TBC1D10C CD19 JUN CALR MYADM HCLS1 NPM1 ATP6AP2 SOCS3 SIVA1 IL3RA BCAP31 LAMTOR4 LITAF S100A13 FHIT S100A4 PPIA TLE1 ADA STMN3 SPTAN1 CALM1 GDI1 ADGRG1 TMSB4X TAX1BP3 DDX3X DDAH2 PSMB8 AIF1 PSMB9 IKBKG CD24 PTP4A3 C1QTNF4 PSME2 PSME1"
2.56220550527902e-10,177,2221,1.64257124238428,"Negative regulation of macromolecule metabolic process "," http://amigo.geneontology.org/amigo/term/GO:0010605"," BTK DPEP1 VIM YBX3 HDAC7 SPI1 TCF3 ENO1 MEF2C RPS5 ATRX PPP1R15A RTRAF FKBP1A RPLP0 PEBP1 FUS IGBP1 CAPN3 EZR HSP90AB1 CIRBP DDX17 XBP1 RPL3 HIF1A PSMA6 SMCHD1 RBBP7 TIMP1 KLF8 BEX4 RGCC PYCARD EIF3E TLE5 NOP53 HSPB1 SERPINE1 TLE4 ENG SPOCK2 RASD1 DDX5 WFS1 FBXW7 HSPA8 GAPDH CCND3 NR3C1 FOXP1 PASK ID2 SUMO1 GADD45A PRDM2 KDM5B TNFAIP3 MYB CCN2 DUSP1 EPC1 ZNF706 RPL5 HNRNPA2B1 OPTN NR4A1 FOSB ID1 UXT KLF2 SMARCA4 ZFP36 CDKN2D SNX6 RPS4Y1 BST2 LSM7 GMFG DNMT1 SH3BP5 RAN H3-3B TERF2 PCNA YWHAQ ANXA1 HNRNPA1 MDM2 ITM2B BIN1 KLF4 HMGA1 CREBZF LEF1 CD27 DUSP6 ARRB2 SERBP1 ECM1 PARP1 CALM2 RPL10 MTDH CDKN2A ARID5B FOXO1 UBC ZFP36L2 CARHSP1 BANK1 UBE2L6 ETS2 RUNX1 CALM3 G6PD S100A1 UBXN1 LAPTM5 REL PBXIP1 IFI16 SPRY1 CITED2 YWHAZ TP53INP1 YWHAB TSC22D4 PHB RPSA SF1 LDLRAD4 LGALS9 CNBP LIMS1 UBB SMAD1 MYD88 CD34 RPL15 CCDC85B TYMS JUN CALR MYADM HCLS1 NRIP1 NPM1 SOCS3 IKZF1 FHIT GSPT2 PPIA TLE1 ARID5A ANXA4 RPS4X CALM1 RPL10A RPL39 NBDY TMSB4X RPS29 DDX3X PSMB8 RPL36A PSMB9 ETV5 TXNIP CD24 LILRB2 SMARCB1 UHRF1 ID3 PSME2 TINF2 PSME1"
2.67983277909025e-10,187,2394,1.60996697578631,"Negative regulation of metabolic process "," http://amigo.geneontology.org/amigo/term/GO:0009892"," BTK DPEP1 VIM YBX3 HDAC7 SPI1 RHOA TCF3 ENO1 MEF2C RPS5 ATRX PPP1R15A RTRAF FKBP1A DYNLL1 RPLP0 PEBP1 FUS IGBP1 CAPN3 EZR HSP90AB1 CIRBP PIK3IP1 DDX17 XBP1 RPL3 HIF1A PSMA6 SMCHD1 RBBP7 TIMP1 KLF8 BEX4 RGCC PYCARD EIF3E TLE5 NOP53 HSPB1 SERPINE1 TLE4 ENG SPOCK2 RASD1 DDX5 WFS1 FBXW7 HSPA8 GAPDH CCND3 NR3C1 FOXP1 PASK ID2 SUMO1 GADD45A PRDM2 KDM5B TNFAIP3 MYB CCN2 DUSP1 EPC1 ZNF706 RPL5 HNRNPA2B1 OPTN NR4A1 FOSB ID1 UXT KLF2 SMARCA4 ZFP36 CDKN2D SNX6 RPS4Y1 BST2 LSM7 GMFG DNMT1 SH3BP5 RAN H3-3B TERF2 PCNA YWHAQ ANXA1 HNRNPA1 MDM2 ITM2B BIN1 KLF4 HMGA1 CREBZF GCHFR LEF1 CD27 DUSP6 PCTP ARRB2 SERBP1 ECM1 PARP1 CALM2 HSPD1 RPL10 MTDH CDKN2A ARID5B FOXO1 UBC ZFP36L2 CARHSP1 BANK1 UBE2L6 ETS2 RUNX1 CALM3 G6PD S100A1 UBXN1 LAPTM5 REL PBXIP1 IFI16 SPRY1 CITED2 YWHAZ TP53INP1 ALDH1A1 YWHAB TSC22D4 PHB RPSA SF1 LDLRAD4 LGALS9 CNBP LIMS1 UBB SMAD1 BCL2L1 MYD88 CD34 RPL15 CCDC85B TYMS JUN CALR MYADM HCLS1 NRIP1 NPM1 SOCS3 IKZF1 FHIT GSPT2 PPIA TLE1 ARID5A ANXA4 RPS4X CALM1 RPL10A RPL39 NBDY TMSB4X VDAC1 RPS29 DDX3X PSMB8 RPL36A PSMB9 ETV5 TXNIP CD24 LILRB2 SMARCB1 UHRF1 ID3 PSME2 TINF2 PSME1 CHMP4A"
2.67983277909025e-10,89,841,2.18119010131105,"Regulation of immune response "," http://amigo.geneontology.org/amigo/term/GO:0050776"," CD99 CD79B BTK WAS DPEP1 CD74 TNFRSF1B CYBA CYFIP2 SPI1 CLEC2D ACTB PAG1 HSP90AA1 MEF2C LAT2 FKBP1A EZR HSP90AB1 XBP1 CTSG COCH PSMA6 NFKBIA RGCC PYCARD PRKD2 CD79A NOP53 ICAM2 ARPC3 FOXP1 ITGA4 SUMO1 TNFAIP3 MYB ELF1 SASH3 HCST CFP SEC14L1 ARPC1B ANXA1 CMTM3 ARRB2 ECM1 HSPD1 NFKBIZ UBC WASF2 RUNX1 S100A1 LAPTM5 IFI16 CLEC4E PHB PTGDR LGALS9 UBB FOS MYD88 CD34 CD19 JUN FUT7 SOCS3 IFITM1 LAMP1 GNG2 SELL ADA HLA-DRB5 CALM1 HLA-E DDX3X HLA-DPA1 PSMB8 HLA-DPB1 ARPC4 PSMB9 IKBKG CD24 B2M TNFRSF14 LILRA2 LILRB2 LAIR1 PSME2 PSME1"
scRNAseq/low_GObiopro.csv 0 → 100644
View file @ fe5789a9
"Enrichment FDR","nGenes","Pathway Genes","Fold Enrichment","Pathway","URL","Genes"
6.12843933790858e-28,212,1126,2.23977522526489,"Cell cycle process "," http://amigo.geneontology.org/amigo/term/GO:0022402"," BRCA1 TACC3 POLA2 BID NCAPH2 RFC1 PSMA4 RFC2 RAD51 POLD1 ASPM TRIP13 SMC1A HMMR MCM2 TIPIN GTSE1 MCM6 POLD3 KIF22 HSP90AA1 NDC80 BAX DYNLL1 BIRC5 NUDC HAUS4 WDR76 EZR CLSPN CDC45 CDC6 CBX5 RANBP1 CENPM MCM5 RBX1 CDKN3 PSMA3 VRK1 PSMB5 APEX1 PSMA6 MYBL2 PSMA7 E2F1 RBBP8 NAA10 RBBP7 EMD PSMD7 UBE2I MCM4 LIG1 RPA3 EZH2 POLD2 SMC3 UBE2S RAD51C KPNB1 PRKAR1A NCAPG FOXM1 RAD51AP1 RFC5 CNOT2 PTPN6 RIPOR2 MCM3 GMNN CCND3 SMC4 PSMD14 ID2 SFPQ MAD2L2 CDC20 STMN1 CENPF RPA2 SET NCAPH MND1 NAA50 PDS5A CBX3 ZWINT CENPK CDK2 CKS2 CDKN1A SOX4 CNOT1 PSMB2 PIN1 PKMYT1 TUBA4A SEM1 SGO1 TUBG1 PSMC3IP TOP2A RAN RPA1 PCNA NASP RBM38 RFC3 CCNB1 CDCA8 ANXA1 CDK4 RNASEH2B CKAP2 SMC2 DSCC1 MYC RPS6 TMEM14B RDX NUSAP1 KIF11 ARL3 KIF20B CENPE CSNK1D ANAPC11 NUF2 DTL EML4 CALM2 FANCD2 CCNA2 PRIM2 CDCA5 CDKN2A MKI67 CHEK1 UBC CENPU HAUS1 CENPH BUB3 RHOC CCNB2 CALM3 PMF1 LMNA ZNF385A SPC24 RBBP4 SGO2 RFC4 MAD2L1 PTTG1 RAD21 MELK PSMC3 CENPN MCM7 PCLAF CDT1 TUBA1A CDK2AP2 FEN1 CENPX CDK1 UBB RRM2 CFL1 PSMD1 ZWILCH UBE2C PSMD2 CENPS BANF1 RMI2 AURKAIP1 ATAD5 TYMS PIDD1 SUZ12 AURKB CALR RCC1 NPM1 SKA2 UBE2L3 FANCA EIF4EBP1 TUBB4B FLNA DYNC1H1 PRIM1 TFDP1 PRC1 MZT1 PPP1CB DHFR KIFC1 TUBB AIF1 PSMB9 CHMP1B PTPRC YWHAE PSMB3 SPC25 PAGR1 RCC2 PSME2"
2.48600524895185e-27,184,919,2.38182186477961,"Mitotic cell cycle "," http://amigo.geneontology.org/amigo/term/GO:0000278"," BRCA1 TACC3 BID NCAPH2 PSMA4 RAD51 TRIP13 SMC1A HMMR MCM2 TIPIN GTSE1 MCM6 KIF22 HSP90AA1 NDC80 BAX DYNLL1 BIRC5 NUDC HAUS4 CLSPN CDC45 CDC6 RANBP1 CENPM RBX1 CDKN3 PSMA3 VRK1 PSMB5 APEX1 PSMA6 MYBL2 PSMA7 E2F1 RBBP8 NAA10 EMD PSMD7 UBE2I MCM4 LIG1 RPA3 EZH2 SMC3 UBE2S RAD51C KPNB1 NCAPG FOXM1 CNOT2 PTPN6 RIPOR2 MCM3 CCND3 SMC4 PSMD14 ID2 MAD2L2 CDC20 STMN1 CENPF RPA2 SET NCAPH NAA50 PDS5A ZWINT CENPK CDK2 TUBA1B CKS2 CDKN1A SOX4 CNOT1 PSMB2 NUP214 YEATS4 PIN1 PKMYT1 TUBA4A SEM1 SGO1 TUBG1 RAN PCNA NASP RBM38 BTG1 CCNB1 CDCA8 ANXA1 CDK4 RNASEH2B CKAP2 SMC2 DSCC1 MYC RPS6 TMEM14B RDX NUSAP1 KIF11 ARL3 KIF20B CENPE IQGAP1 CSNK1D ANAPC11 NUF2 DTL EML4 CALM2 FANCD2 CCNA2 CDCA5 CDKN2A MKI67 CHEK1 UBC CENPU HAUS1 CENPH BUB3 RHOC CCNB2 CALM3 PMF1 LMNA SRSF2 ZNF385A SPC24 SGO2 MAD2L1 PTTG1 RAD21 MELK SKA3 PSMC3 NOLC1 CENPN RRM1 CDT1 TUBA1A TUBA1C CDK2AP2 CDK1 UBB RRM2 CKS1B PSMD1 ZWILCH UBE2C PSMD2 CENPS BANF1 AURKAIP1 TYMS PIDD1 AURKB RCC1 SKA2 EIF4EBP1 TUBB4B FLNA DYNC1H1 TFDP1 PRC1 CENPW MZT1 SRA1 PPP1CB DHFR KIFC1 TUBB AIF1 PSMB9 CHMP1B YWHAE PSMB3 SPC25 RCC2 PSME2"
3.10490649032622e-27,170,816,2.47836763330215,"Mitotic cell cycle process "," http://amigo.geneontology.org/amigo/term/GO:1903047"," BRCA1 TACC3 BID NCAPH2 PSMA4 RAD51 TRIP13 SMC1A HMMR MCM2 TIPIN GTSE1 MCM6 KIF22 HSP90AA1 NDC80 BAX DYNLL1 BIRC5 NUDC HAUS4 CLSPN CDC45 CDC6 RANBP1 CENPM RBX1 CDKN3 PSMA3 VRK1 PSMB5 APEX1 PSMA6 MYBL2 PSMA7 E2F1 RBBP8 NAA10 EMD PSMD7 UBE2I MCM4 LIG1 EZH2 SMC3 UBE2S RAD51C KPNB1 NCAPG FOXM1 CNOT2 PTPN6 RIPOR2 MCM3 CCND3 SMC4 PSMD14 ID2 MAD2L2 CDC20 STMN1 CENPF RPA2 SET NCAPH NAA50 PDS5A ZWINT CENPK CDK2 CKS2 CDKN1A SOX4 CNOT1 PSMB2 PIN1 PKMYT1 TUBA4A SEM1 SGO1 TUBG1 RAN PCNA NASP RBM38 CCNB1 CDCA8 ANXA1 CDK4 RNASEH2B CKAP2 SMC2 DSCC1 MYC RPS6 TMEM14B RDX NUSAP1 KIF11 ARL3 KIF20B CENPE CSNK1D ANAPC11 NUF2 DTL EML4 CALM2 FANCD2 CCNA2 CDCA5 CDKN2A MKI67 CHEK1 UBC CENPU HAUS1 CENPH BUB3 RHOC CCNB2 CALM3 PMF1 LMNA ZNF385A SPC24 SGO2 MAD2L1 PTTG1 RAD21 MELK PSMC3 CENPN CDT1 TUBA1A CDK2AP2 CDK1 UBB RRM2 PSMD1 ZWILCH UBE2C PSMD2 CENPS BANF1 AURKAIP1 TYMS PIDD1 AURKB RCC1 SKA2 EIF4EBP1 TUBB4B FLNA DYNC1H1 TFDP1 PRC1 MZT1 PPP1CB DHFR KIFC1 TUBB AIF1 PSMB9 CHMP1B YWHAE PSMB3 SPC25 RCC2 PSME2"
1.45647325997386e-26,252,1501,1.99722417671038,"Cell cycle "," http://amigo.geneontology.org/amigo/term/GO:0007049"," BRCA1 TACC3 POLA2 BID NCAPH2 BAK1 RFC1 PSMA4 RFC2 RAD51 POLD1 GNAI3 ASPM RASSF1 TRIP13 SMC1A HMMR MCM2 TIPIN GTSE1 ACTB MCM6 POLD3 KIF22 HSP90AA1 NDC80 BAX DYNLL1 BIRC5 NUDC HAUS4 WDR76 EZR CLSPN CDC45 CDC6 CBX5 HSP90AB1 RANBP1 CENPM MCM5 RBX1 CDKN3 PSMA3 ERH VRK1 PSMB5 APEX1 PSMA6 MYBL2 PSMA7 E2F1 RBBP8 NAA10 RBBP7 EMD PSMD7 UBE2I MCM4 LIG1 RPA3 EZH2 POLD2 MAP3K8 SMC3 UBE2S RAD51C KPNB1 PRKAR1A NCAPG HSPA8 BIRC2 FOXM1 RAD51AP1 RFC5 CNOT2 CDCA3 PTPN6 RIPOR2 MCM3 GMNN CCND3 NR3C1 SMC4 PSMD14 ID2 SFPQ MAD2L2 CDC20 STMN1 CENPF RPA2 TNFAIP3 SET HELLS NCAPH MND1 NAA50 PDS5A CBX3 ZWINT CENPK NR4A1 CDK2 TUBA1B CKS2 CDKN1A SOX4 CNOT1 PSMB2 NUP214 YEATS4 PIN1 PKMYT1 TUBA4A SEM1 SGO1 JUND TUBG1 PSMC3IP TOP2A RAN RPA1 PCNA NASP RBM38 RFC3 BTG1 CCNB1 CDCA8 ANXA1 CDK4 RNASEH2B CKAP2 SMC2 DSCC1 MYC RPS6 TMEM14B RDX NUSAP1 KIF11 ARL3 KIF20B MNS1 CENPE FANCI IQGAP1 CSNK1D ANAPC11 NUF2 DTL EML4 CALM2 FANCD2 CCNA2 PRIM2 WTAP CDCA5 CDKN2A MKI67 CHEK1 UBC CENPU HAUS1 CENPH BUB3 RHOC CCNB2 MIS18A CTBP1 CALM3 PMF1 LMNA SRSF2 ZNF385A SPC24 RBBP4 SGO2 RFC4 MAD2L1 PTTG1 RAD21 MELK SKA3 PSMC3 NOLC1 CENPN MCM7 PCLAF RRM1 CDT1 TUBA1A TUBA1C CHAF1A CDK2AP2 FEN1 CENPX CDK1 UBB RRM2 PPP1CA CFL1 CKS1B PSMD1 ZWILCH UBE2C PSMD2 CENPS BANF1 RMI2 AURKAIP1 ATAD5 TYMS PIDD1 SUZ12 AURKB CALR RAD23A RCC1 NPM1 SKA2 UBE2L3 FANCA EIF4EBP1 TUBB4B FLNA DYNC1H1 PRIM1 TFDP1 PRC1 CENPW MZT1 SRA1 PPP1CB DHFR KIFC1 TUBB AIF1 PSMB9 CHMP1B PTPRC TXNIP YWHAE PSMB3 UHRF1 SPC25 PAGR1 RCC2 PSME2 RUVBL1 BOP1"
1.17119059522123e-23,90,311,3.44261999223964,"DNA replication "," http://amigo.geneontology.org/amigo/term/GO:0006260"," BRCA1 POLA2 RFC1 PSMA4 RFC2 RAD51 POLD1 MCM10 NUCKS1 MCM2 TIPIN MCM6 POLD3 ORC6 CLSPN CDC45 CDC6 MCM5 PSMA3 PSMB5 PSMA6 PSMA7 SAMHD1 RBBP8 RBBP7 PSMD7 MCM4 LIG1 RPA3 POLD2 SMC3 RFC5 MCM3 GMNN PSMD14 POLE4 CACYBP RPA2 SET PDS5A CDK2 PSMB2 SEM1 DUT GINS2 RPA1 PCNA NASP RFC3 RAC1 DSCC1 HMGA1 NFIC DTL CCNA2 PRIM2 MMS22L POLE3 SSRP1 CHEK1 UBC RBBP4 S100A11 RFC4 STOML2 DDX21 PSMC3 MCM7 FAM111A PCLAF RRM1 CDT1 CHAF1A FEN1 CENPX CDK1 UBB RRM2 PSMD1 PSMD2 CENPS RMI2 ATAD5 NAP1L1 FAM111B PRIM1 PSMB9 SSBP1 PSMB3 PSME2"
1.30864671518348e-21,160,833,2.28497760189202,"Chromosome organization "," http://amigo.geneontology.org/amigo/term/GO:0051276"," TACC3 POLA2 NCAPH2 HMGB3 RFC1 HDAC9 RFC2 RAD51 POLD1 NUCKS1 TRIP13 SMC1A MCM2 MCM6 POLD3 UBE2A XRCC5 KIF22 HSP90AA1 NDC80 NUDC CDC6 HSP90AB1 JAK2 CENPM MCM5 RBX1 APEX1 NAA10 RBBP7 MCM4 RPA3 EZH2 POLD2 NPM3 SMC3 RAD51C KPNB1 NSD2 NCAPG PTGES3 RFC5 MCM3 SUB1 NR3C1 SMC4 SFPQ MAD2L2 CDC20 CENPF RPA2 HMGN3 SET PHF19 HELLS TCP1 ENY2 NCAPH NAA50 PDS5A CBX3 HNRNPA2B1 ZWINT CENPK CDK2 NFE2 DEK YEATS4 HAT1 SGO1 TRIM28 DNMT1 TUBG1 TOP2A RAN RPA1 PCNA NASP RFC3 CCNB1 DDB2 CDCA8 ANXA1 CCT7 ACTL6A SMC2 ANP32B DSCC1 MYC HMGA1 NUSAP1 HNRNPD CENPE COPS3 ANAPC11 NUF2 ANP32E PARP1 EML4 FANCD2 G3BP1 NHP2 PRIM2 CDCA5 POLE3 MKI67 SSRP1 HIRIP3 CCT5 UBC CENPU CENPH CHD1 BUB3 ATAD2 MIS18A LMNA RBBP4 CCT3 SGO2 RFC4 HMGB2 MAD2L1 PTTG1 RAD21 CCT2 CENPN MCM7 CDT1 CHAF1A FEN1 CENPX CDK1 UBB COPRS UBE2C CENPS BANF1 RMI2 SUZ12 AURKB NPM1 NAP1L1 HMGB1 XRCC6 HDAC2 PRIM1 HMGN2 PRC1 CENPW HMGN1 ANKRD28 KIFC1 PRKDC CHMP1B SSBP1 NAP1L4 UHRF1 RUVBL1 ASF1B"
1.42305152566402e-21,158,819,2.29498658497723,"DNA metabolic process "," http://amigo.geneontology.org/amigo/term/GO:0006259"," POLR2J BRCA1 POLA2 HMGB3 RFC1 RFC2 RAD51 HPF1 POLD1 MCM10 SPI1 NUCKS1 UFD1 TRIP13 SMC1A MCM2 TIPIN MCM6 UNG UBE2T EXOSC5 POLD3 UBE2A XRCC5 KIF22 HSP90AA1 BAX FUS FH ORC6 CLSPN CDC45 CDC6 HSP90AB1 POLR2F MCM5 RBX1 APEX1 SAMHD1 RBBP8 MCM4 POLR2I LIG1 NUDT1 RPA3 EZH2 POLD2 SMC3 RAD51C KPNB1 NSD2 PTGES3 FOXM1 RAD51AP1 RFC5 MCM3 GMNN SUB1 PSMD14 POLE4 SFPQ MAD2L2 CENPF RPA2 HELLS TCP1 MND1 PDS5A HNRNPA2B1 CDK2 CDKN1A DEK SEM1 TRIM28 DNMT1 GINS2 PSMC3IP TOP2A RAN RPA1 PCNA RFC3 DDB2 CCT7 ACTL6A DSCC1 MYC HMGA1 HNRNPD FANCI COPS3 ARRB2 DTL PARP1 HSPD1 FANCD2 NHP2 PRIM2 MMS22L CDCA5 NONO POLR2K CDKN2A POLE3 PAXX SSRP1 CHEK1 CCT5 UBC MIS18A LMNA TNFSF13 USP1 CCT3 POLR2H RFC4 HMGB2 PTTG1 RAD21 DDX21 CCT2 PLD4 MCM7 FAM111A PCLAF PHB CDT1 CHAF1A TK1 NUDT16L1 FEN1 CENPX CDK1 UBB CENPS RMI2 TYMS POLR2L UBE2N AURKB RAD23A DCTPP1 NPM1 KPNA2 FANCA HMGB1 PPIA XRCC6 PRIM1 TFDP1 HMGN1 ANKRD28 FANCG NME1 PRKDC UHRF1 PAGR1 RUVBL1"
4.62381511498833e-20,190,1111,2.03444759817422,"Cell activation "," http://amigo.geneontology.org/amigo/term/GO:0001775"," FGR SLC25A5 TYROBP CD22 CD74 SERPINB1 NCAPH2 RNASET2 CD44 TNFRSF1B POU2F2 BAK1 GRN HDAC9 CYBA SLC2A3 GNA15 TSPAN32 CNN2 GNAI3 SPI1 PKM KCNAB2 ACTB PLD1 UNG XRCC5 HSP90AA1 GSTP1 FTL BAX GNAS DYNLL1 LYZ HSP90AB1 JAK2 LGALS1 CTSG CTSZ MYL9 CST3 PGRMC1 TIMP1 TNFSF13B CORO1A PSMD7 COTL1 PYCARD CSK CTSH NDRG1 EZH2 VSIR MAP3K8 KPNB1 PRKAR1A NSD2 CTSC HSPA8 MDK CD81 BIN2 SH2B3 PTPN6 RIPOR2 CD83 CCND3 SELENOK CD86 PSMD14 ID2 MAD2L2 CD58 PRDX1 CTSD TNFAIP3 VAMP8 YPEL5 HELLS GNA13 FLT3 SASH3 PRDX4 NMI CDKN1A SOX4 CD70 CFP IGLL1 RAC2 CD68 LDLR JUND CAP1 LGALS3 ANXA1 CD36 CD63 TMBIM1 LCP1 RAC1 TLN1 RPS6 GGH GLIPR1 LMBR1L IQGAP1 IRF8 ARRB2 SOD1 SLC2A5 S100A8 ILF2 C1orf35 HSPD1 FANCD2 IQGAP2 CDKN2A GSN PTGES2 LAMTOR1 NDUFC2 VSIG4 MS4A1 CD1C FCER1G TNFRSF13C ATAD3B CSTB ICOSLG ITGB2 JAML IL6R CYGB TNFSF13 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 YWHAZ CYBB STOML2 HPRT1 PSMC3 CCT2 PHB CD320 FTH1 IRF2BP2 RHOH GUSB PA2G4 PGAM1 CTPS1 PSMD1 PSMD2 RHOG ANXA2 LCK ACTG1 CLECL1 ATP6V0C IRS2 GNG2 FANCA TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 FLNA DYNC1H1 PSAP SVIP HLA-DPA1 TUBB AIF1 LST1 HLA-DPB1 HLA-DMB NME2 PRKDC LYN PTPRC CFD MIF"
5.02656801746117e-19,175,1007,2.06735731079822,"Leukocyte activation "," http://amigo.geneontology.org/amigo/term/GO:0045321"," FGR SLC25A5 TYROBP CD22 CD74 SERPINB1 NCAPH2 RNASET2 CD44 TNFRSF1B POU2F2 BAK1 GRN HDAC9 CYBA SLC2A3 TSPAN32 CNN2 SPI1 PKM KCNAB2 PLD1 UNG XRCC5 HSP90AA1 GSTP1 FTL BAX DYNLL1 LYZ HSP90AB1 JAK2 LGALS1 CTSG CTSZ CST3 PGRMC1 TNFSF13B CORO1A PSMD7 COTL1 PYCARD CSK CTSH NDRG1 EZH2 VSIR MAP3K8 KPNB1 PRKAR1A NSD2 CTSC HSPA8 MDK CD81 BIN2 PTPN6 RIPOR2 CD83 CCND3 SELENOK CD86 PSMD14 ID2 MAD2L2 CD58 PRDX1 CTSD TNFAIP3 VAMP8 YPEL5 HELLS FLT3 SASH3 PRDX4 NMI CDKN1A SOX4 CD70 CFP IGLL1 RAC2 CD68 LDLR JUND CAP1 LGALS3 ANXA1 CD36 CD63 TMBIM1 LCP1 RAC1 RPS6 GGH GLIPR1 LMBR1L IQGAP1 IRF8 SOD1 SLC2A5 S100A8 ILF2 C1orf35 HSPD1 FANCD2 IQGAP2 CDKN2A GSN PTGES2 LAMTOR1 NDUFC2 VSIG4 MS4A1 CD1C FCER1G TNFRSF13C ATAD3B CSTB ICOSLG ITGB2 JAML IL6R TNFSF13 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB STOML2 HPRT1 PSMC3 CCT2 PHB CD320 FTH1 IRF2BP2 RHOH GUSB PA2G4 PGAM1 CTPS1 PSMD1 PSMD2 RHOG ANXA2 LCK CLECL1 ATP6V0C IRS2 FANCA TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP HLA-DPA1 TUBB AIF1 LST1 HLA-DPB1 HLA-DMB NME2 PRKDC LYN PTPRC CFD MIF"
1.59415153122539e-18,147,788,2.21920837824619,"Cellular response to DNA damage stimulus "," http://amigo.geneontology.org/amigo/term/GO:0006974"," POLR2J BRCA1 BID CD74 CD44 TNFRSF1B BAK1 RFC1 RFC2 RAD51 HPF1 POLD1 MCM10 RASSF1 NUCKS1 UFD1 TRIP13 SMC1A MCM2 TIPIN GTSE1 MCM6 UNG UBE2T POLD3 UBE2A XRCC5 KIF22 DNAJA1 BAX FUS FH WDR76 CLSPN CDC45 CBX5 POLR2F MCM5 RBX1 APEX1 SAMHD1 E2F1 RBBP8 PYCARD NDRG1 MCM4 POLR2I LIG1 NUDT1 RPA3 POLD2 SMC3 RAD51C CBX1 NSD2 TMEM109 FOXM1 RAD51AP1 RFC5 CNOT2 MCM3 PSMD14 SFPQ MAD2L2 RPA2 PDS5A CBX3 CDK2 CDKN1A SOX4 DEK CNOT1 MRPS26 BCL2L12 SEM1 TRIM28 GINS2 TOP2A RPA1 PCNA RBM38 CASP9 RFC3 CCNB1 DDB2 RNASEH2B ACTL6A SKIL MYC HMGA1 FANCI COPS3 MCL1 DTL PARP1 FANCD2 MMS22L CDCA5 NONO POLR2K CDKN2A POLE3 PAXX SSRP1 CHEK1 UBC ZNF385A USP1 MNDA POLR2H RFC4 HMGB2 PTTG1 RAD21 MCM7 FAM111A PCLAF CHAF1A NUDT16L1 DDIT4 FEN1 CENPX CDK1 UBB CENPS RMI2 ATAD5 PIDD1 POLR2L UBE2N RAD23A NPM1 KPNA2 FANCA HMGB1 XRCC6 TFDP1 HMGN1 CRIP1 FANCG GNL1 PRKDC LYN UHRF1 MIF PAGR1 RUVBL1"
2.89359088465336e-18,98,424,2.74958522336163,"Neutrophil mediated immunity "," http://amigo.geneontology.org/amigo/term/GO:0002446"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 WDR1 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 CFP CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 IL6R ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 PTPRC CFD MIF"
3.11204672941558e-18,96,411,2.77866619324971,"Neutrophil degranulation "," http://amigo.geneontology.org/amigo/term/GO:0043312"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 CFP CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 PTPRC CFD MIF"
4.56591807805605e-18,63,203,3.69191316409148,"DNA-dependent DNA replication "," http://amigo.geneontology.org/amigo/term/GO:0006261"," POLA2 RFC1 PSMA4 RFC2 RAD51 POLD1 MCM10 NUCKS1 MCM2 TIPIN MCM6 POLD3 ORC6 CDC45 CDC6 MCM5 PSMA3 PSMB5 PSMA6 PSMA7 SAMHD1 PSMD7 MCM4 LIG1 RPA3 POLD2 RFC5 MCM3 GMNN PSMD14 POLE4 RPA2 CDK2 PSMB2 SEM1 GINS2 RPA1 PCNA RFC3 DSCC1 HMGA1 PRIM2 MMS22L POLE3 UBC RFC4 STOML2 DDX21 PSMC3 MCM7 FAM111A CDT1 FEN1 CENPX UBB PSMD1 PSMD2 CENPS PRIM1 PSMB9 SSBP1 PSMB3 PSME2"
5.54742368403089e-18,96,415,2.7518838684955,"Neutrophil activation involved in immune response "," http://amigo.geneontology.org/amigo/term/GO:0002283"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 CFP CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 PTPRC CFD MIF"
7.45146231300009e-18,374,2934,1.51641635150103,"Response to stress "," http://amigo.geneontology.org/amigo/term/GO:0006950"," FGR POLR2J TYROBP BRCA1 BID CD74 VIM RNASET2 CD44 TNFRSF1B HMGB3 BAK1 GRN RFC1 PSMA4 HSPA5 HDAC9 RFC2 HERPUD1 RAD51 CYBA HPF1 GNA15 POLD1 TSPAN32 CNN2 GNAI3 MCM10 YBX1 SPI1 RASSF1 NUCKS1 UFD1 TRIP13 SMC1A MCM2 MGLL HACD3 ENO1 TIPIN GTSE1 ACTB MCM6 UNG UBE2T EXOSC5 POLD3 UBE2A XRCC5 KIF22 HSP90AA1 GSTP1 AKR1B1 DNAJA1 BAX GNAS FUS LYZ FH WDR76 EZR CLSPN CDC45 CBX5 HSP90AB1 JAK2 HNRNPM LGALS1 POLR2F MCM5 TSPO RBX1 CTSG PSMA3 PSMB5 APEX1 PSMA6 GSS PSMA7 MYL9 HCK SAMHD1 E2F1 CST3 AHCY RBBP8 RBBP7 PGK1 TIMP1 CORO1A PSMD7 COTL1 PYCARD NDRG1 MCM4 POLR2I LIG1 CHCHD2 NUDT1 RPA3 EZH2 RHEB POLD2 SEC61B PDLIM1 MAP3K8 SMC3 PPIF RAD51C CBX1 C1QBP PRKAR1A NSD2 CTSC HSPA8 TMEM109 BIRC2 MDK CD81 NDUFS8 PTGES3 FOXM1 RAD51AP1 SH2B3 RFC5 CNOT2 GAPDH PTPN6 SOD2 MCM3 CD83 DUSP22 LY86 SPARC ATP6V0E1 CPEB4 SELENOK PSMD14 HSPE1 ID2 NFE2L2 SFPQ MAD2L2 NCF2 CD58 CD48 PRDX1 RPA2 TNFAIP3 VAMP8 GNA13 PDS5A CBX3 PRDX4 CDK2 NFE2 NMI CDKN1A SOX4 DEK CNOT1 MT2A MRPS26 PSMB2 BCL2L12 CFP NUP214 PIN1 SEM1 ZFP36 SDF2L1 IGLL1 CD68 LDLR TRIM28 GINS2 TOP2A LGALS3 NUP210 RPA1 PCNA RBM38 OSER1 CASP9 RFC3 BTG1 EIF2S1 CCNB1 DDB2 ANXA1 CD36 SYNCRIP CDK4 NIBAN1 RNASEH2B RAC1 ACTL6A SKIL TXN MYC TLN1 HMGA1 ENTPD1 FANCI IRF8 COPS3 ARRB2 IGFBP4 SOD1 MCL1 DTL S100A8 PARP1 PDIA6 HSPD1 FANCD2 ARL6IP5 MANF CCNA2 G3BP1 KCNMB1 MMS22L CDCA5 NONO POLR2K CDKN2A GSN POLE3 PAXX SSRP1 LAMTOR1 CHEK1 UBC NR4A2 CD96 ELOC RHOC VSIG4 NCF1 FCER1G ICOSLG ITGB2 JAML IL6R LMNA CYGB ZNF385A POLR3K RPS6KA4 USP1 CAPN2 REL S100A9 S100A12 IGFBP7 MNDA POLR2H RFC4 HMGB2 ANXA5 PTTG1 RAD21 YWHAZ PSIP1 CYBB STOML2 MELK PRDX3 DDX21 PSMC3 NOLC1 PLD4 MCM7 HSP90B1 FAM111A PCLAF PDIA3 PHB ACAA2 VKORC1 CHAF1A NUDT16L1 DDIT4 FEN1 RNASE6 CENPX GABARAP CDK1 UBB CYCS PPP1CA PSMD1 CCS CD34 PSMD2 CENPS RMI2 ATAD5 ATOX1 PIDD1 POLR2L UBE2N PLEC CALR RAD23A DCTPP1 NPM1 SHMT2 KPNA2 CSF1R ANXA2 LCK ACTG1 NDUFA12 P4HB ATP6V0C GNG2 FANCA EIF4EBP1 LAMTOR4 TUBB4B HMGB1 AKR1C3 PPIA CD55 XRCC6 SULF2 HDAC2 CD47 ARID5A FLNA ANXA6 MPEG1 SVIP TFDP1 HLA-DRB5 MT-ND4 TMSB4X HMGN1 CRIP1 VDAC1 IFI30 FANCG HLA-DPA1 DHFR GNL1 HSBP1 HLA-DQA2 GPSM3 TUBB AIF1 HLA-DPB1 ADSL STMP1 NME2 PSMB9 PRKDC LYN PTPRC TXNIP DERL3 YWHAE CFD DDT METRNL PSMB3 UHRF1 MIF NDUFA6 PAGR1 PSME2 RUVBL1 ATP5IF1 LSP1"
7.6371248860885e-18,101,453,2.65234575855383,"Leukocyte degranulation "," http://amigo.geneontology.org/amigo/term/GO:0043299"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ HCK CST3 PGRMC1 CORO1A PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 CFP RAC2 CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 LYN PTPRC CFD MIF"
7.6371248860885e-18,62,200,3.6878110383536,"Protein-DNA complex assembly "," http://amigo.geneontology.org/amigo/term/GO:0065004"," PSMA4 RAD51 MCM2 MCM6 ORC6 CDC45 CDC6 CENPM MCM5 RBX1 PSMA3 PSMB5 PSMA6 PSMA7 RBBP7 PSMD7 MCM4 RPA3 RAD51C MCM3 GMNN PSMD14 CENPF RPA2 SET HELLS CENPK PSMB2 SEM1 HAT1 RPA1 NASP DDB2 ANP32B CENPE PARP1 UBC CENPU CENPH MIS18A RBBP4 HMGB2 PSMC3 CENPN MCM7 CDT1 CHAF1A CENPX UBB PSMD1 PSMD2 CENPS NPM1 NAP1L1 HMGB1 CENPW PSMB9 NAP1L4 PSMB3 PSME2 RUVBL1 ASF1B"
2.14826578717523e-17,96,424,2.69347123921139,"Neutrophil activation "," http://amigo.geneontology.org/amigo/term/GO:0042119"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 CFP CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 PTPRC CFD MIF"
3.45913494390436e-17,102,470,2.58172083673348,"Myeloid leukocyte mediated immunity "," http://amigo.geneontology.org/amigo/term/GO:0002444"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 WDR1 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 CFP RAC2 CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 IL6R ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB DDX21 PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 LYN PTPRC CFD MIF"
3.7379046495672e-17,67,236,3.37730097826259,"Protein-DNA complex subunit organization "," http://amigo.geneontology.org/amigo/term/GO:0071824"," PSMA4 RAD51 MCM2 MCM6 ORC6 CDC45 CDC6 CENPM MCM5 RBX1 PSMA3 PSMB5 PSMA6 PSMA7 RBBP7 PSMD7 MCM4 RPA3 RAD51C MCM3 GMNN PSMD14 CENPF RPA2 SET HELLS CENPK NFE2 PSMB2 SEM1 HAT1 RPA1 NASP DDB2 ANP32B MYC HMGA1 CENPE ANP32E PARP1 POLE3 UBC CENPU CENPH MIS18A RBBP4 HMGB2 PSMC3 CENPN MCM7 CDT1 CHAF1A CENPX UBB PSMD1 PSMD2 CENPS NPM1 NAP1L1 HMGB1 CENPW PSMB9 NAP1L4 PSMB3 PSME2 RUVBL1 ASF1B"
4.17277506635676e-17,118,591,2.37520715313424,"Leukocyte activation involved in immune response "," http://amigo.geneontology.org/amigo/term/GO:0002366"," FGR TYROBP CD74 SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 UNG XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 LGALS1 CTSG CTSZ CST3 PGRMC1 CORO1A PSMD7 COTL1 PYCARD CTSH KPNB1 NSD2 CTSC HSPA8 MDK CD81 BIN2 PTPN6 CD86 PSMD14 MAD2L2 CD58 CTSD VAMP8 YPEL5 PRDX4 NMI CFP RAC2 CD68 CAP1 LGALS3 ANXA1 CD36 CD63 TMBIM1 LCP1 RAC1 RPS6 GGH GLIPR1 IQGAP1 IRF8 SLC2A5 S100A8 ILF2 C1orf35 HSPD1 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 CD1C FCER1G ATAD3B CSTB ITGB2 IL6R TNFSF13 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB HLA-DMB NME2 LYN PTPRC CFD MIF"
4.18424188995792e-17,96,429,2.66207880052595,"Granulocyte activation "," http://amigo.geneontology.org/amigo/term/GO:0036230"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 CFP CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 PTPRC CFD MIF"
5.82140170174633e-17,118,594,2.36321115741134,"Cell activation involved in immune response "," http://amigo.geneontology.org/amigo/term/GO:0002263"," FGR TYROBP CD74 SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 UNG XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 LGALS1 CTSG CTSZ CST3 PGRMC1 CORO1A PSMD7 COTL1 PYCARD CTSH KPNB1 NSD2 CTSC HSPA8 MDK CD81 BIN2 PTPN6 CD86 PSMD14 MAD2L2 CD58 CTSD VAMP8 YPEL5 PRDX4 NMI CFP RAC2 CD68 CAP1 LGALS3 ANXA1 CD36 CD63 TMBIM1 LCP1 RAC1 RPS6 GGH GLIPR1 IQGAP1 IRF8 SLC2A5 S100A8 ILF2 C1orf35 HSPD1 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 CD1C FCER1G ATAD3B CSTB ITGB2 IL6R TNFSF13 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB HLA-DMB NME2 LYN PTPRC CFD MIF"
6.6953726251632e-17,100,461,2.58051293706081,"Myeloid cell activation involved in immune response "," http://amigo.geneontology.org/amigo/term/GO:0002275"," FGR TYROBP SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 NMI CFP RAC2 CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 FCER1G ATAD3B CSTB ITGB2 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB NME2 LYN PTPRC CFD MIF"
6.76470593282529e-17,110,535,2.44594039324025,"Myeloid leukocyte activation "," http://amigo.geneontology.org/amigo/term/GO:0002274"," FGR TYROBP CD74 SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 TSPAN32 CNN2 SPI1 PKM KCNAB2 PLD1 XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 JAK2 CTSG CTSZ CST3 PGRMC1 PSMD7 COTL1 PYCARD CTSH NDRG1 KPNB1 CTSC HSPA8 BIN2 PTPN6 PSMD14 CD58 CTSD VAMP8 YPEL5 PRDX4 NMI CFP RAC2 CD68 LDLR JUND CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 GGH GLIPR1 IQGAP1 SLC2A5 S100A8 ILF2 C1orf35 HSPD1 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 VSIG4 FCER1G ATAD3B CSTB ITGB2 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB PSMC3 CCT2 FTH1 RHOH GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP TUBB AIF1 NME2 LYN PTPRC CFD MIF"
8.94707449553553e-17,249,1738,1.70434119408673,"Cellular response to stress "," http://amigo.geneontology.org/amigo/term/GO:0033554"," POLR2J BRCA1 BID CD74 CD44 TNFRSF1B BAK1 GRN RFC1 PSMA4 HSPA5 RFC2 HERPUD1 RAD51 CYBA HPF1 POLD1 MCM10 YBX1 SPI1 RASSF1 NUCKS1 UFD1 TRIP13 SMC1A MCM2 HACD3 ENO1 TIPIN GTSE1 MCM6 UNG UBE2T POLD3 UBE2A XRCC5 KIF22 HSP90AA1 GSTP1 AKR1B1 DNAJA1 BAX FUS FH WDR76 EZR CLSPN CDC45 CBX5 HSP90AB1 JAK2 HNRNPM POLR2F MCM5 TSPO RBX1 PSMA3 PSMB5 APEX1 PSMA6 PSMA7 SAMHD1 E2F1 RBBP8 RBBP7 PGK1 PSMD7 PYCARD NDRG1 MCM4 POLR2I LIG1 CHCHD2 NUDT1 RPA3 EZH2 RHEB POLD2 SEC61B MAP3K8 SMC3 PPIF RAD51C CBX1 NSD2 HSPA8 TMEM109 PTGES3 FOXM1 RAD51AP1 RFC5 CNOT2 SOD2 MCM3 DUSP22 ATP6V0E1 CPEB4 SELENOK PSMD14 ID2 NFE2L2 SFPQ MAD2L2 NCF2 PRDX1 RPA2 TNFAIP3 PDS5A CBX3 CDK2 CDKN1A SOX4 DEK CNOT1 MRPS26 PSMB2 BCL2L12 NUP214 SEM1 ZFP36 SDF2L1 TRIM28 GINS2 TOP2A NUP210 RPA1 PCNA RBM38 OSER1 CASP9 RFC3 EIF2S1 CCNB1 DDB2 ANXA1 CD36 NIBAN1 RNASEH2B ACTL6A SKIL TXN MYC TLN1 HMGA1 FANCI COPS3 SOD1 MCL1 DTL PARP1 PDIA6 HSPD1 FANCD2 ARL6IP5 MANF CCNA2 KCNMB1 MMS22L CDCA5 NONO POLR2K CDKN2A POLE3 PAXX SSRP1 LAMTOR1 CHEK1 UBC NR4A2 ELOC NCF1 LMNA ZNF385A USP1 MNDA POLR2H RFC4 HMGB2 PTTG1 RAD21 CYBB STOML2 MELK PRDX3 PSMC3 MCM7 HSP90B1 FAM111A PCLAF PDIA3 ACAA2 CHAF1A NUDT16L1 DDIT4 FEN1 CENPX GABARAP CDK1 UBB CYCS PPP1CA PSMD1 CCS CD34 PSMD2 CENPS RMI2 ATAD5 PIDD1 POLR2L UBE2N CALR RAD23A DCTPP1 NPM1 KPNA2 P4HB ATP6V0C FANCA EIF4EBP1 LAMTOR4 HMGB1 AKR1C3 PPIA XRCC6 HDAC2 FLNA SVIP TFDP1 HMGN1 CRIP1 FANCG DHFR GNL1 HSBP1 AIF1 NME2 PSMB9 PRKDC LYN DERL3 YWHAE PSMB3 UHRF1 MIF PAGR1 PSME2 RUVBL1 ATP5IF1"
1.38748722946312e-16,105,503,2.48329480553536,"Mitotic cell cycle phase transition "," http://amigo.geneontology.org/amigo/term/GO:0044772"," BRCA1 TACC3 BID PSMA4 TRIP13 HMMR GTSE1 HSP90AA1 NDC80 BAX DYNLL1 HAUS4 CLSPN CDC45 CDC6 RBX1 CDKN3 PSMA3 PSMB5 APEX1 PSMA6 PSMA7 E2F1 RBBP8 PSMD7 EZH2 UBE2S RAD51C FOXM1 CNOT2 PTPN6 CCND3 PSMD14 ID2 MAD2L2 CDC20 CENPF RPA2 ZWINT CDK2 CKS2 CDKN1A SOX4 CNOT1 PSMB2 PKMYT1 TUBA4A SEM1 TUBG1 PCNA NASP RBM38 CCNB1 ANXA1 CDK4 RNASEH2B MYC RPS6 TMEM14B RDX CENPE CSNK1D ANAPC11 DTL CALM2 CCNA2 CDCA5 CDKN2A CHEK1 UBC HAUS1 BUB3 CCNB2 CALM3 ZNF385A MAD2L1 RAD21 MELK PSMC3 CDT1 TUBA1A CDK2AP2 CDK1 UBB RRM2 PSMD1 UBE2C PSMD2 TYMS PIDD1 AURKB RCC1 EIF4EBP1 TUBB4B DYNC1H1 TFDP1 PPP1CB DHFR TUBB AIF1 PSMB9 YWHAE PSMB3 RCC2 PSME2"
2.09936982628342e-16,121,628,2.29209541627689,"Leukocyte mediated immunity "," http://amigo.geneontology.org/amigo/term/GO:0002443"," FGR TYROBP CD74 SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 WDR1 PLD1 UNG XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 CTSG CTSZ CST3 PGRMC1 CORO1A PSMD7 COTL1 PYCARD CTSH KPNB1 C1QBP NSD2 CTSC HSPA8 CD81 BIN2 PTPN6 PSMD14 MAD2L2 CD58 PRDX1 CTSD VAMP8 YPEL5 SASH3 PRDX4 CD70 CFP IGLL1 RAC2 CD68 CAP1 LGALS3 CD36 CD63 TMBIM1 RAC1 MYO1G GGH GLIPR1 IQGAP1 ARRB2 SLC2A5 S100A8 ILF2 C1orf35 HSPD1 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 CD96 CD1C FCER1G ATAD3B CSTB ITGB2 IL6R TNFSF13 ARPC5 S100A11 S100A9 S100A12 MNDA FABP5 CYBB HPRT1 DDX21 PSMC3 CCT2 FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP GNL1 TUBB NME2 LYN PTPRC CFD MIF"
2.36514164146682e-16,114,575,2.35854394598772,"Cell cycle phase transition "," http://amigo.geneontology.org/amigo/term/GO:0044770"," BRCA1 TACC3 BID PSMA4 TRIP13 HMMR TIPIN GTSE1 HSP90AA1 NDC80 BAX DYNLL1 HAUS4 WDR76 CLSPN CDC45 CDC6 RBX1 CDKN3 PSMA3 PSMB5 APEX1 PSMA6 PSMA7 E2F1 RBBP8 PSMD7 EZH2 UBE2S RAD51C FOXM1 CNOT2 PTPN6 CCND3 PSMD14 ID2 MAD2L2 CDC20 CENPF RPA2 ZWINT CDK2 CKS2 CDKN1A SOX4 CNOT1 PSMB2 PKMYT1 TUBA4A SEM1 TUBG1 PCNA NASP RBM38 CCNB1 ANXA1 CDK4 RNASEH2B MYC RPS6 TMEM14B RDX CENPE CSNK1D ANAPC11 DTL CALM2 FANCD2 CCNA2 CDCA5 CDKN2A CHEK1 UBC HAUS1 BUB3 CCNB2 CALM3 ZNF385A MAD2L1 RAD21 MELK PSMC3 CDT1 TUBA1A CDK2AP2 CDK1 UBB RRM2 PSMD1 ZWILCH UBE2C PSMD2 ATAD5 TYMS PIDD1 AURKB RCC1 NPM1 UBE2L3 EIF4EBP1 TUBB4B DYNC1H1 TFDP1 PPP1CB DHFR TUBB AIF1 PSMB9 PTPRC YWHAE PSMB3 PAGR1 RCC2 PSME2"
2.49076248603547e-16,141,790,2.12323951166949,"Immune effector process "," http://amigo.geneontology.org/amigo/term/GO:0002252"," FGR TYROBP CD22 CD74 SERPINB1 RNASET2 CD44 TNFRSF1B GRN CYBA SLC2A3 CNN2 SPI1 PKM KCNAB2 WDR1 ACTB PLD1 UNG XRCC5 HSP90AA1 GSTP1 FTL DYNLL1 LYZ HSP90AB1 LGALS1 CTSG CTSZ HCK CST3 PGRMC1 CORO1A PSMD7 COTL1 SLC7A5 PYCARD CTSH KPNB1 C1QBP NSD2 CTSC HSPA8 MDK CD81 BIN2 PTPN6 SELENOK CD86 PSMD14 MAD2L2 CD58 PRDX1 CTSD VAMP8 YPEL5 SASH3 PRDX4 NMI CD70 CFP IGLL1 RAC2 CD68 ARPC1B CAP1 LGALS3 ANXA1 CD36 CD63 TMBIM1 LCP1 RAC1 MYO1G RPS6 GGH GLIPR1 IQGAP1 IRF8 ARRB2 SLC2A5 S100A8 ILF2 C1orf35 HSPD1 IQGAP2 GSN PTGES2 LAMTOR1 NDUFC2 CD96 VSIG4 CD1C FCER1G ATAD3B CSTB ITGB2 IL6R TNFSF13 ARPC5 S100A11 S100A9 S100A12 ARPC2 MNDA FABP5 CYBB HPRT1 DDX21 PSMC3 CCT2 PHB FTH1 GUSB PA2G4 PGAM1 PSMD1 PSMD2 RHOG ANXA2 ACTG1 ATP6V0C TUBB4B S100A13 HMGB1 PPIA CD55 XRCC6 CD47 DYNC1H1 PSAP SVIP GNL1 TUBB HLA-DMB NME2 PRKDC LYN PTPRC CFD MIF"
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