bulkRNAseq TD timing processing scripts and fig5F

DGE/DGE.R

+/beegfs/scratch/ric.gentner/ric.gentner/HSC/ZonariVolpin/zonarivolpin_2024/BulkRNAseq/72hvs24h/DGE_72hvs24h.R
\ No newline at end of file

DGE/edgeR-TI72h_202205-TI24h_202205.txt

+T172h_vs_T124h_202205/edgeR-TI72h_202205-TI24h_202205.txt
\ No newline at end of file

DGE/edgeR-TI72h_rd062-TI24h_rd062.txt

+T172h_vs_T124h_rd062/edgeR-TI72h_rd062-TI24h_rd062.txt
\ No newline at end of file

Fig5F_processing_and_plot.R

+library(ggplot2)
+library(openxlsx)
+library(reshape2)
+library(clusterProfiler)
+library(ReactomePA)
+library(enrichplot)
+library(scales)
+
+# re-create normalized matrix for each batch in order to perform GSEA 
+
+data = read.table("featureCounts_results.txt", 
+                  header=T, row.names = 1)
+colnames(data) <- gsub(x = gsub(x = colnames(data), pattern = "_Aligned.sortedByCoord.out.bam", replacement = ""), 
+                       pattern = "X.beegfs.scratch.ric.gentner.ric.gentner.HSC.Exp2155.results.03.aln.", replacement = "")
+data <- data[,6:ncol(data)]
+bcv <- 0.1
+
+counts <- data
+comparisons <- list(T172h_vs_T124h_rd062 = c("TI72h_rd062","TI24h_rd062"),
+                    T172h_vs_T124h_202205 = c("TI72h_202205","TI24h_202205")
+)
+normalized_data <- list()
+
+# filtering out very low counts genes
+
+comparisons_results <- list()
+for(i in names(comparisons)){
+  
+  if(i == "T172h_vs_T124h_202205"){
+    bcv <- 0.3
+  }else{
+    bcv <- 0.1
+  }
+  
+  ids <- comparisons[[i]]
+  
+  counts.ss <- subset(x = counts, select = ids)
+  rsums <- rowSums(counts.ss)
+  counts.ss <- subset(counts.ss, subset = rsums > 31) # at least 30 counts as sum in the 2
+  
+  y <- DGEList(counts=counts.ss, group = ids)
+  TMM <- calcNormFactors(y, method = "TMM") 
+  normdata <- cpm(y = TMM, normalized.lib.sizes = TRUE)
+  normalized_data[[i]] <- normdata
+}
+
+saveRDS(normalized_data, file = "Normalized_data_CPM.rds")
+
+## creating plot for CDKN1A ####
+
+comparison_1vs1 <- readRDS(file = "DGE/Comparisons_exp1_1vs1.rds")
+rd062 <- readRDS(file = "DGE/T172h_vs_T124h_rd062/edgeR-TI72h_rd062-TI24h_rd062.rds")
+exp2022_05 <- readRDS(file = "DGE/T172h_vs_T124h_202205/edgeR-TI72h_202205-TI24h_202205.rds")
+SizeFactors_norm_cpm_exp1_1vs1 <- readRDS("DGE/SizeFactors_norm_cpm_exp1_1vs1.rds")
+cpm_normalized_rd062 <- SizeFactors_norm_cpm_exp1_1vs1$T172h_vs_T124h_rd062
+cpm_normalized_exp2022_05 <- SizeFactors_norm_cpm_exp1_1vs1$T172h_vs_T124h_202205
+
+
+# creating a common universe for perforing ORA
+exp1_72vs24 <- readRDS("DGE/Comparisons_exp1_1vs1.rds")
+universe <- union(x = rownames(exp1_72vs24$T172h_vs_T124h_rd062), 
+                  y = rownames(exp1_72vs24$T172h_vs_T124h_202205)
+)
+
+universe_conv <- bitr(geneID = universe, fromType = "ENSEMBL", toType = "SYMBOL", OrgDb = "org.Hs.eg.db")
+universe[as.numeric(rownames(universe_conv))] <- universe_conv$SYMBOL # convert ensembl symbols where possible, remnants ens genes are pseudogenes without symbol
+
+# up & concordant genes
+up <- exp1_72vs24$intersection_sign_concordant_UP$Concordant_UP
+up_conv_df <- bitr(geneID = up, fromType = "ENSEMBL", toType = "SYMBOL", OrgDb = "org.Hs.eg.db")
+up[as.numeric(rownames(up_conv_df))] <- up_conv_df$SYMBOL
+
+# down and concordant genes
+dw <- exp1_72vs24$intersection_sign_concordant_DW$Concordant_DOWN
+dw_conv_df <- bitr(geneID = dw, fromType = "ENSEMBL", toType = "SYMBOL", OrgDb = "org.Hs.eg.db")
+dw[as.numeric(rownames(dw_conv_df))] <- dw_conv_df$SYMBOL
+
+# performing enrichment on reactome
+# converting ids to entrez
+dw_entrez.conv <- bitr(geneID = dw, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = "org.Hs.eg.db")
+dw_entrez <- dw_entrez.conv$ENTREZID
+
+up_entrez.conv <- bitr(geneID = up, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = "org.Hs.eg.db")
+up_entrez <- up_entrez.conv$ENTREZID
+
+universe_entrez <- bitr(geneID = universe, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = "org.Hs.eg.db")
+universe_entrez <- universe_entrez$ENTREZID
+
+### geneList with foldChange for cnet and viewPathway plots
+
+T172h_vs_T124h_rd062_light <- comparison_1vs1$T172h_vs_T124h_rd062[,c("logFC","FDR")]
+T172h_vs_T124h_exp202205_light <- comparison_1vs1$T172h_vs_T124h_202205[,c("logFC","FDR")]
+
+# up genes
+up_entrez.conv_annot <- merge.data.frame(x = up_entrez.conv, 
+                                         y = T172h_vs_T124h_rd062_light, 
+                                         by.x = "SYMBOL",
+                                         by.y = 0, all.x = T, sort = F)
+colnames(up_entrez.conv_annot)[3:4] <- c("logFC.rd062","FDR.rd062")
+
+up_entrez.conv_annot <- merge.data.frame(x = up_entrez.conv_annot, 
+                                         y = T172h_vs_T124h_exp202205_light, 
+                                         by.x = "SYMBOL",
+                                         by.y = 0, all.x = T, sort = F)
+
+colnames(up_entrez.conv_annot)[5:6] <- c("logFC.exp202205","FDR.exp202205")
+
+# down genes
+dw_entrez.conv_annot <- merge.data.frame(x = dw_entrez.conv, 
+                                         y = T172h_vs_T124h_rd062_light, 
+                                         by.x = "SYMBOL",
+                                         by.y = 0, all.x = T, sort = F)
+colnames(dw_entrez.conv_annot)[3:4] <- c("logFC.rd062","FDR.rd062")
+
+dw_entrez.conv_annot <- merge.data.frame(x = dw_entrez.conv_annot, 
+                                         y = T172h_vs_T124h_exp202205_light, 
+                                         by.x = "SYMBOL",
+                                         by.y = 0, all.x = T, sort = F)
+
+colnames(dw_entrez.conv_annot)[5:6] <- c("logFC.exp202205","FDR.exp202205")
+
+up_dw_geneList <- rbind.data.frame(up_entrez.conv_annot, dw_entrez.conv_annot)
+# remove NA # previuos genes reconverted to alias, 
+# so backtracking do not work, useless for enrichment - pseudo or genes not available in enrichment terms lists
+up_dw_geneList <- up_dw_geneList[complete.cases(up_dw_geneList),]
+up_dw_geneList$logFC <- (up_dw_geneList$logFC.exp202205 + up_dw_geneList$logFC.rd062) / 2 # aritmetic mean just for sign up or down
+up_dw_geneList_symbol <- up_dw_geneList$logFC
+names(up_dw_geneList_symbol) <- up_dw_geneList$SYMBOL
+up_dw_geneList_entrez <- up_dw_geneList_symbol
+names(up_dw_geneList_entrez) <- up_dw_geneList$ENTREZID
+
+x <- enrichPathway(gene=names(up_dw_geneList_entrez), universe = universe_entrez, 
+                   pvalueCutoff = 0.05, readable=TRUE)
+
+saveRDS(object = list(enrichpath = x, up_dw_geneList = up_dw_geneList, up_dw_geneList_entrez = up_dw_geneList_entrez,
+                      universe_entrez = universe_entrez), 
+        file = "updown_plot_inputs.rds")
+
+enrichment_reactome <- as.data.frame(x = x)
+write.xlsx(x = enrichment_reactome, file = "Enrichment_reactome_from_concordant_genes_updown.xlsx")
+
+png(filename = "CNET_plot_Enrichment_reactome_from_concordant_genes_updown.png", width = 6, height = 6, units = "in", res = 300)
+cnetplot(x = x, foldChange = up_dw_geneList_entrez, showCategory = 15, 
+         cex_label_category = 0.6, cex_label_gene = 0.6, color_category = "darkgrey") + 
+  theme(legend.position = "bottom")
+dev.off()
+
+# Do barplot top categories
+
+enrichment_reactome$Description <- factor(enrichment_reactome$Description, levels = enrichment_reactome$Description)
+
+png("Barplot_ora_analysis_72vs24_common_concordant_genes.png", width = 9, height = 6, units = "in", res = 300)
+ggplot(data = enrichment_reactome, mapping = aes(x = Count, y = Description, fill = p.adjust)) + 
+  geom_bar(stat = "identity", color = "black") +   
+  scale_y_discrete(labels = label_wrap(70)) + 
+  ggtitle(label = "ORA analysis from 72h vs 24h DEGs") +
+  theme_bw() +
+  scale_fill_distiller(palette = "YlGnBu", direction = 1) +
+  theme(axis.text.y = element_text(vjust = 0.5), plot.title = element_text(hjust = 0))
+dev.off()
+
+
+enrichment_reactome <- read.xlsx(xlsxFile = "Enrichment_reactome_from_concordant_genes_updown.xlsx")
+enrichment_reactome$Description <- factor(enrichment_reactome$Description, levels = enrichment_reactome$Description)
+
+pdf("Barplot_ora_analysis_72vs24_common_concordant_genes_paper.pdf", width = 7, height = 8)
+ggplot(data = enrichment_reactome, mapping = aes(x = Count, y = Description, fill = p.adjust)) + 
+  geom_bar(stat = "identity", color = "black") +   
+  scale_y_discrete(labels = label_wrap(60)) + 
+  ggtitle(label = "ORA analysis from 72h vs 24h DEGs") +
+  theme_bw() +
+  scale_fill_distiller(palette = "YlGnBu", direction = 1) +
+  theme(axis.text.y = element_text(vjust = 0.5, size = 12), 
+        axis.title = element_blank(),legend.position = "bottom",
+        plot.title = element_text(hjust = 2.5))
+dev.off()
+
+
+# senescence signatures ####
+
+sasp <- read.gmt(gmtfile = "sasp.gmt")
+senescence <- read.gmt(gmtfile = "senescence_full.gmt")
+senescence_literature <- read.gmt(gmtfile = "senescence_signatures.gmt")
+senescence.signatures <- rbind.data.frame(sasp, senescence)
+senescence.signatures <- rbind.data.frame(senescence.signatures, senescence_literature)
+
+# create heatmap with genes belonging to terms provided in .gmt files ####
+
+dim(normalized_data$T172h_vs_T124h_rd062)
+#[1] 21005     2
+dim(normalized_data$T172h_vs_T124h_202205)
+#[1] 21314     2
+
+# pickup common genes 
+common_genes_expressed <- intersect(rownames(normalized_data$T172h_vs_T124h_202205),
+                                    rownames(normalized_data$T172h_vs_T124h_rd062))
+
+normalized_data$T172h_vs_T124h_202205 <- normalized_data$T172h_vs_T124h_202205[common_genes_expressed,]
+normalized_data$T172h_vs_T124h_rd062 <- normalized_data$T172h_vs_T124h_rd062[common_genes_expressed,]
+
+# converting alias to gene symbols both signatures and genes in matrix expression
+
+rownames(normalized_data$T172h_vs_T124h_rd062) == rownames(normalized_data$T172h_vs_T124h_202205)
+genes_matrix_alias_to_symbol_conversion <- bitr(geneID = rownames(normalized_data$T172h_vs_T124h_rd062), 
+                                                fromType = "ALIAS", toType = "SYMBOL", OrgDb = "org.Hs.eg.db")
+# retrieve duplicated genes in which an alias poit to 2 or more symbols
+duplicated_genes <- unique(genes_matrix_alias_to_symbol_conversion[which(duplicated(genes_matrix_alias_to_symbol_conversion$ALIAS)),]$ALIAS)
+
+View(genes_matrix_alias_to_symbol_conversion[genes_matrix_alias_to_symbol_conversion$ALIAS %in% duplicated_genes,])
+
+## converting gmt genes to official symbols ####
+
+senescence.signatures_alias_to_symbol_conversion <- bitr(geneID = senescence.signatures$gene, 
+                                                fromType = "ALIAS", toType = "SYMBOL", OrgDb = "org.Hs.eg.db")
+
+# looking at common genes (symbol) between senescence terms and gene expression matrix rownames
+
+common_symbols <- intersect(senescence.signatures_alias_to_symbol_conversion$SYMBOL, genes_matrix_alias_to_symbol_conversion$SYMBOL)
+common_alias <- intersect(senescence.signatures_alias_to_symbol_conversion$ALIAS, genes_matrix_alias_to_symbol_conversion$ALIAS)
+common_symbols_alias_gmatrix <- intersect(senescence.signatures_alias_to_symbol_conversion$SYMBOL, genes_matrix_alias_to_symbol_conversion$ALIAS)
+
+senescence.signatures_alias_to_symbol_conversion$Conversion <- "Not"
+senescence.signatures_alias_to_symbol_conversion$Conversion[which(senescence.signatures_alias_to_symbol_conversion$ALIAS != senescence.signatures_alias_to_symbol_conversion$SYMBOL)] <- "Converted"
+
+genes_matrix_alias_to_symbol_conversion$Conversion <- "Not"
+genes_matrix_alias_to_symbol_conversion$Conversion[which(genes_matrix_alias_to_symbol_conversion$ALIAS != genes_matrix_alias_to_symbol_conversion$SYMBOL)] <- "Converted"
+
+# using symbol converted genes for signatures, whereas maintain alias (original genes) for expression gene matrix geneset
+
+senescence.signatures_fix <- merge.data.frame(x = senescence.signatures, 
+                                              y = senescence.signatures_alias_to_symbol_conversion, 
+                                              by.x = "gene", 
+                                              by.y = "ALIAS", all.x = T, sort = F)
+
+library(dplyr)
+
+senescence.signatures_fix <- subset(senescence.signatures_fix, SYMBOL %in% common_symbols_alias_gmatrix)
+senescence.signatures_fix_format_ok <- senescence.signatures_fix
+senescence.signatures_fix_format_ok$gene <- NULL
+senescence.signatures_fix_format_ok$Conversion <- NULL
+colnames(senescence.signatures_fix_format_ok) <- c("term","gene")
+senescence.signatures_fix_format_ok <- senescence.signatures_fix_format_ok %>% dplyr::arrange(term, gene) 
+
+# creating geneList for GSEA from mean value of rd062 and Exp2022_05
+
+geneList <- cbind.data.frame(normalized_data$T172h_vs_T124h_rd062, normalized_data$T172h_vs_T124h_202205)
+geneList <- geneList + 1
+geneList$mean_ratio <- (geneList$TI72h_rd062 / geneList$TI24h_rd062 + geneList$TI72h_202205 / geneList$TI24h_202205) / 2 # mean for GSEA ranking
+geneList$log2_mean_ratio <- log2(geneList$mean_ratio)
+geneList <- geneList %>% dplyr::arrange(-mean_ratio)
+
+# run GSEA ####
+
+geneList_input <- geneList[,"log2_mean_ratio", drop = F]
+geneList_input_vector <- geneList_input$log2_mean_ratio
+names(geneList_input_vector) <- rownames(geneList_input)
+
+saveRDS(geneList_input_vector, file = "geneList_input_vector.rds")
+gsea_res <- list()
+
+for(gset in c("c2","c3","c4","c5","c6","c7","c8","h")){
+  print(paste0("Analyzing geneset: ", gset))
+  t2gene <- read.gmt(gmtfile = paste0(gset,".all.v2023.1.Hs.symbols.gmt"))
+  ## Selecting only gene names and log2foldchange for GSEA ##
+  gsea_res[[gset]] <- GSEA(geneList_input_vector, TERM2GENE = t2gene,
+                           seed = 1234,
+                           maxGSSize = 1000, 
+                           verbose = FALSE,
+                           pvalueCutoff = 0.1)
+}
+
+gsea_res[["senescence_signatures"]] <- GSEA(geneList_input_vector, 
+                                            TERM2GENE = senescence.signatures_fix_format_ok,
+                                            seed = 1234,
+                                            maxGSSize = 1000, 
+                                            verbose = FALSE,
+                                            pvalueCutoff = 0.1)
+
+saveRDS(object = gsea_res, file = "GSEA_analysis.rds")
+
+df_list <- list()
+
+for(j in names(gsea_res)){
+  
+  t <- as.data.frame(gsea_res[[j]])
+  df_list[[j]] <- t
+  
+}
+
+write.xlsx(df_list, file = "TI72h_vs_24h_GSEA_analysis.xlsx")
+
+myplot <- cnetplot(x = gsea_res$senescence_signatures, foldChange = geneList_input_vector, showCategory = 15, 
+         cex_label_category = 0, cex_label_gene = 1.2,layout = "kk",cex_category = 2,
+         color_label_gene = "lightgrey", 
+         #color_gene = "lightgrey", 
+         color_category = c("black","#f7cb4d","#41b375","#7baaf7","#ba67c8", rep("grey",127))
+) + 
+  ggtitle(label = "GSEA senescence signatures", subtitle = "72h vs 24h - Core Enrichment Genes") +
+  theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5), 
+        legend.position = "bottom", legend.text = element_text(size = 16, hjust = 0.5))
+
+# red color: #e67c73
+myplot_v2 <- cnetplot(x = gsea_res$senescence_signatures, foldChange = geneList_input_vector, showCategory = 15, 
+                   cex_label_category = 1.5, cex_label_gene = 1.2,layout = "kk",
+                   color_label_gene = "lightgrey", cex_category = 2,
+                   #color_gene = "lightgrey", 
+                   color_category = c("black","#f7cb4d","#41b375","#7baaf7","#ba67c8", rep("grey",127))
+) + 
+  ggtitle(label = "GSEA senescence signatures", subtitle = "72h vs 24h - Core Enrichment Genes") +
+  theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5), 
+        legend.position = "bottom", legend.text = element_text(size = 16, hjust = 0.5))
+
+
+pdf(file = "CNET_plot_GSEA_senescence_signatures_wide_nolabels.pdf", width = 12, height = 10)
+myplot
+dev.off()
+
+pdf(file = "CNET_plot_GSEA_senescence_signatures_wide_labels.pdf", width = 10, height = 10)
+myplot_v2
+dev.off()
+
+

sasp.gmt

+SASP_SCHLEICH	https://doi.org/10.1038/s41586-021-03995-1	ANG	AREG	ATF4	BHLHE40	CCL1	CCL11	CCL12	CCL2	CCL20	CCL25	CD55	CD55B	CD9	CPE	CSF2	CSF2RB	CSF2RB2	CSF3	CTSB	CXCL1	CXCL12	CXCL13	CXCL2	CXCL5	CXCR2	EGF	EGFR	EREG	ETS1	ETS2	FAS	FGF2	FGF7	FN1	GEM	GMFG	HGF	ICAM1	ID1	IFNG	IGF1	IGF2	IGF2R	IGFBP1	IGFBP2	IGFBP3	IGFBP4	IGFBP5	IGFBP6	IGFBP7	IL13	IL15	IL1A	IL1B	IL6	IL6ST	IL7	ITGA2	ITPKA	KITLG	MIF	MMP10	MMP12	MMP14	MMP1A	MMP3	NGF	PECAM1	PIGF	PLAT	PLAU	PLAUR	PTGES	SERPINB2	SERPINE1	TGFB1	TIMP1	TIMP2	TNFRSF11B	TNFRSF1A	TNFRSF1B	VEGFA
+CLASSICAL_SASP	https://doi.org/10.1038/s41586-021-03995-1	BGN	CCL2	CCL20	COL1A1	CXCL12	DCN	EGF	EFEMP1	FGF2	FGF7	FN1	CSF1	CTGF	CXCL1	GDNF	IGFBP2	IGFBP6	IFNA1	IFNB1	IFNG	IL1A	IL1B	IL6	IL7	CXCL8	IL13	IL15	KITLG	CCL2	CCL9	MMP2	MMP3	MMP9	SERPINE1	TGFB1	THBS1	TIMP2	TNF	VEGFA

senescence_full.gmt

+GO_CELLULAR_SENESCENCE	> A cell aging process stimulated in response to cellular stress, whereby normal cells lose the ability to divide through irreversible cell cycle arrest. [GOC:BHF]	ABL1	AKT3	ARG2	ARNTL	BCL2L12	BCL6	BMPR1A	C2orf40	CALR	CDK6	CDKN1A	CDKN2A	CDKN2B	CGAS	EEF1E1	FBXO5	H2AFX	HLA-G	HMGA1	HMGA2	HRAS	ID2	ING2	KAT6A	KIR2DL4	KRAS	MAGEA2	MAGEA2B	MAP2K1	MAP3K3	MAPK14	MAPKAPK5	MIR10A	MIR146A	MIR17	MIR188	MIR20B	MIR217	MIR22	MIR34A	MIR543	MIR590	NAMPT	NEK4	NEK6	NSMCE2	NUAK1	OPA1	PAWR	PLA2R1	PLK2	PML	PNPT1	PRKCD	PRKDC	PRMT6	RBL1	RSL1D1	SIRT1	SLC30A10	SMC5	SMC6	SRF	TBX2	TBX3	TERC	TERF2	TERT	TP53	TWIST1	ULK3	VASH1	WNT16	YPEL3	ZKSCAN3	ZMPSTE24	ZNF277
+GO_STRESS_INDUCED_PREMATURE_SENESCENCE	> A cellular senescence process associated with the dismantling of a cell as a response to environmental factors such as hydrogen peroxide or X-rays. [GOC:BHF]	ARNTL	CDKN1A	MAPK14	MAPKAPK5	PLA2R1	SIRT1	TP53	WNT16
+REACTOME_CELLULAR_SENESCENCE	> Cellular Senescence	ACD	AGO1	AGO3	AGO4	AL096870.1	ANAPC1	ANAPC10	ANAPC11	ANAPC15	ANAPC16	ANAPC2	ANAPC4	ANAPC5	ANAPC7	ASF1A	ATM	BMI1	CABIN1	CBX2	CBX4	CBX6	CBX8	CCNA1	CCNA2	CCNE1	CCNE2	CDC16	CDC23	CDC26	CDC27	CDK2	CDK4	CDK6	CDKN1A	CDKN1B	CDKN2A	CDKN2B	CDKN2C	CDKN2D	CEBPB	CXCL8	E2F1	E2F2	E2F3	EED	EHMT1	EHMT2	EP400	ERF	ETS1	ETS2	EZH2	FOS	FZR1	H1F0	H2AFB1	H2AFJ	H2AFV	H2AFX	H2AFZ	H2BFS	H3F3A	H3F3B	HIRA	HIST1H1A	HIST1H1B	HIST1H1C	HIST1H1D	HIST1H1E	HIST1H2AB	HIST1H2AC	HIST1H2AD	HIST1H2AE	HIST1H2AJ	HIST1H2BA	HIST1H2BB	HIST1H2BC	HIST1H2BD	HIST1H2BE	HIST1H2BF	HIST1H2BG	HIST1H2BH	HIST1H2BI	HIST1H2BJ	HIST1H2BK	HIST1H2BL	HIST1H2BM	HIST1H2BN	HIST1H2BO	HIST1H3A	HIST1H3D	HIST1H3E	HIST1H3F	HIST1H3G	HIST1H3H	HIST1H3I	HIST1H3J	HIST1H4A	HIST1H4B	HIST1H4C	HIST1H4D	HIST1H4E	HIST1H4F	HIST1H4H	HIST1H4I	HIST1H4J	HIST1H4K	HIST1H4L	HIST2H2AA3	HIST2H2AA4	HIST2H2AC	HIST2H2BE	HIST2H3A	HIST2H3C	HIST2H3D	HIST2H4A	HIST2H4B	HIST3H2BB	HIST3H3	HIST4H4	HMGA1	HMGA2	ID1	IFNB1	IGFBP7	IL1A	IL6	JUN	KAT5	KDM6B	LMNB1	MAP2K3	MAP2K4	MAP2K6	MAP2K7	MAP3K5	MAP4K4	MAPK1	MAPK10	MAPK11	MAPK14	MAPK3	MAPK7	MAPK8	MAPK9	MAPKAPK2	MAPKAPK3	MAPKAPK5	MDM2	MDM4	MINK1	MIR24-1	MIR24-2	MOV10	MRE11	NBN	NFKB1	PHC1	PHC2	PHC3	POT1	RAD50	RB1	RBBP4	RBBP7	RELA	RING1	RNF2	RPS27A	RPS6KA1	RPS6KA2	RPS6KA3	SCMH1	SP1	STAT3	SUZ12	TERF1	TERF2	TERF2IP	TFDP1	TFDP2	TNIK	TNRC6A	TNRC6B	TNRC6C	TP53	TXN	UBA52	UBB	UBC	UBE2C	UBE2D1	UBE2E1	UBN1
+REACTOME_ONCOGENE_INDUCED_SENESCENCE	> Oncogene Induced Senescence	AGO1	AGO3	AGO4	CDK4	CDK6	CDKN2A	CDKN2B	CDKN2C	CDKN2D	E2F1	E2F2	E2F3	ERF	ETS1	ETS2	ID1	MAPK1	MAPK3	MDM2	MDM4	MIR24-1	MIR24-2	MOV10	RB1	RPS27A	SP1	TFDP1	TFDP2	TNRC6A	TNRC6B	TNRC6C	TP53	UBA52	UBB	UBC
+REACTOME_OXIDATIVE_STRESS_INDUCED_SENESCENCE	> Oxidative Stress Induced Senescence	AGO1	AGO3	AGO4	BMI1	CBX2	CBX4	CBX6	CBX8	CDK4	CDK6	CDKN2A	CDKN2B	CDKN2C	CDKN2D	E2F1	E2F2	E2F3	EED	EZH2	FOS	H2AFB1	H2AFJ	H2AFV	H2AFX	H2AFZ	H2BFS	H3F3A	H3F3B	HIST1H2AB	HIST1H2AC	HIST1H2AD	HIST1H2AE	HIST1H2AJ	HIST1H2BA	HIST1H2BB	HIST1H2BC	HIST1H2BD	HIST1H2BE	HIST1H2BF	HIST1H2BG	HIST1H2BH	HIST1H2BI	HIST1H2BJ	HIST1H2BK	HIST1H2BL	HIST1H2BM	HIST1H2BN	HIST1H2BO	HIST1H3A	HIST1H3D	HIST1H3E	HIST1H3F	HIST1H3G	HIST1H3H	HIST1H3I	HIST1H3J	HIST1H4A	HIST1H4B	HIST1H4C	HIST1H4D	HIST1H4E	HIST1H4F	HIST1H4H	HIST1H4I	HIST1H4J	HIST1H4K	HIST1H4L	HIST2H2AA3	HIST2H2AA4	HIST2H2AC	HIST2H2BE	HIST2H3A	HIST2H3C	HIST2H3D	HIST2H4A	HIST2H4B	HIST3H2BB	HIST4H4	IFNB1	JUN	KDM6B	MAP2K3	MAP2K4	MAP2K6	MAP2K7	MAP3K5	MAP4K4	MAPK1	MAPK10	MAPK11	MAPK14	MAPK3	MAPK8	MAPK9	MAPKAPK2	MAPKAPK3	MAPKAPK5	MDM2	MDM4	MINK1	MIR24-1	MIR24-2	MOV10	PHC1	PHC2	PHC3	RBBP4	RBBP7	RING1	RNF2	RPS27A	SCMH1	SUZ12	TFDP1	TFDP2	TNIK	TNRC6A	TNRC6B	TNRC6C	TP53	TXN	UBA52	UBB	UBC
+GO_CELL_AGING	> An aging process that has as participant a cell after a cell has stopped dividing. Cell aging may occur when a cell has temporarily stopped dividing through cell cycle arrest (GO:0007050) or when a cell has permanently stopped dividing, in which case it is undergoing cellular senescence (GO:0090398). May precede cell death (GO:0008219) and succeed cell maturation (GO:0048469). [GOC:PO_curators]	ABL1	AKT3	ARG2	ARNTL	ATM	ATR	BCL2	BCL2L12	BCL6	BGLAP	BMPR1A	BRCA2	C2orf40	CALR	CDK1	CDK6	CDKN1A	CDKN2A	CDKN2B	CGAS	CHEK1	CHEK2	CTC1	DNAJA3	EEF1E1	ENG	ERCC1	FBXO5	FOXM1	FZR1	H2AFX	HLA-G	HMGA1	HMGA2	HRAS	ICAM1	ID2	ILK	ING2	KAT6A	KIR2DL4	KRAS	LIMS1	LMNA	MAGEA2	MAGEA2B	MAP2K1	MAP3K3	MAPK14	MAPKAPK5	MARCH5	MIF	MIR10A	MIR146A	MIR17	MIR188	MIR20B	MIR21	MIR217	MIR22	MIR34A	MIR543	MIR590	MME	MNT	MORC3	MTOR	NAMPT	NEK4	NEK6	NOX4	NPM1	NSMCE2	NUAK1	NUP62	OPA1	PAWR	PDCD4	PLA2R1	PLK2	PML	PNPT1	PRELP	PRKCD	PRKDC	PRMT6	PTEN	RBL1	ROMO1	RSL1D1	RWDD1	SERPINE1	SIRT1	SLC30A10	SMC5	SMC6	SOD1	SRF	TBX2	TBX3	TERC	TERF2	TERT	TP53	TP63	TWIST1	ULK3	VASH1	WNT1	WNT16	WRN	YPEL3	ZKSCAN3	ZMIZ1	ZMPSTE24	ZNF277
+Reactome_Cellular_Senescence	Reactome_Cellular_Senescence	CDKN1A	CDKN2A	CDKN2B	CEBPB	EED	ETS2	EZH2	IGFBP7	IL1A	IL6	IL8	KDM6B	MAP4K4	MINK1	MIR24-1	MIR24-2	SUZ12	TNIK
+Reactome_Cellular_responses_to_stress	Reactome_Cellular_responses_to_stress	CA9	CDKN1A	CDKN2A	CDKN2B	CEBPB	COL4A6	CRYBA4	DEDD2	DNAJB1	DNAJB6	EED	EPO	ETS2	EZH2	FKBP4	GML	HIGD1A	HSBP1	HSBP2	HSPA1A	HSPA1B	HSPA1L	HSPA6	HSPH1	IGFBP7	IL1A	IL6	IL8	KDM6B	MAP4K4	MINK1	MIR24-1	MIR24-2	MRPL18	RLN1	SERPINH1	SUZ12	TNFRSF21	TNIK	UBB	VEGFA
+Reactome_Oxidative_Stress_Induced_Senescence	Reactome_Oxidative_Stress_Induced_Senescence	CDKN2A	EED	EZH2	KDM6B	MAP4K4	MINK1	MIR24-1	MIR24-2	SUZ12	TNIK
+KEGG_SENESCENCE	KEGG_SENESCENCE	AKT1	AKT2	AKT3	ATM	ATR	BTRC	CACNA1D	CALM1	CALM2	CALM3	CALML3	CALML4	CALML5	CALML6	CAPN1	CAPN2	CCNA1	CCNA2	CCNB1	CCNB2	CCNB3	CCND1	CCND2	CCND3	CCNE1	CCNE2	CDC25A	CDK1	CDK2	CDK4	CDK6	CDKN1A	CDKN2CDKN2B	CHEK1	CHEK2	CXCL8	E2F1	E2F2	E2F3	E2F4	E2F5	EIF4EBP1	ETS1	FBXW11	FOXM1	FOXO1	FOXO3	GADD45A	GADD45B	GADD45G	GATA4	HIPK1	HIPK2	HIPK3	HIPK4	HLA-A	HLA-B	HLA-C	HLA-E	HLA-F	HLA-G	HRAS	HUS1	IGFBPIL1A	IL6	ITPR1	ITPR2	ITPR3	KIR2DL1	KIR2DL2	KIR2DL3	KIR2DL4	KIR2DL5A	KRAS	LIN37	LIN52	LIN54	LIN9	MAP2K1	MAP2K2	MAP2K3	MAP2K6	MAPK1	MAPK11	MAPK12	MAPK13	MAPK14	MAPK3	MAPKAPK2	MCU	MDM2	MRAS	MRE11	MTOR	MYBL2	MYC	NBN	NFATC1	NFATC2	NFATC3	NFATC4	NFKB1	NRAS	PIK3CA	PIK3CB	PIK3CD	PIK3R1	PIK3R2	PIK3R3	PPID	PPP1CA	PPP1CB	PPP1CC	PPP3CA	PPP3CB	PPP3CC	PPP3R1	PPP3R2	PTEN	RAD1	RAD50	RAD9A	RAD9B	RAF1	RASSF5	RB1	RBBP4RBL1	RBL2	RELA	RHEB	RRAS	RRAS2	SERPINE1	SIRT1	SLC25A31	SLC25A4	SLC25A5	SLC25A6	SMAD2	SMAD3	SQSTM1	TGFB1	TGFB2	TGFB3	TGFBR1	TGFBR2	TP53	TRAF3IP2	TRPM7	TRPV4	TSC1	TSC2	VDAC1	VDAC2	VDAC3	ZFP36L1	ZFP36L2
+Senescence_Demaria	Senescence_Demaria	PLK3	SPATA6	NFIA	TAF13	GSTM4	KCTD3	MEIS1	TMEM87B	GBE1	STAG1	SCOC	ICE1	RAI14	GDNF	P4HA2	PDLIM4	B4GALT7	TSPAN13	ZNHIT1	SMO	CNTLN	CHMP5	FAM214B	USP6NL	TRDMT1	ASCC1	TOLLIP	CCND1	RHNO1	C2CD5	ARID2	DGKA	PDS5B	UFM1	BCL2L2	SUSD6	KLC1	ADPGK	CREBBP	NOL3	ACADVL	EFNB3	SLC16A3	ZBTB7A	DDA1	ARHGAP35	ZC3H4	PCIF1	POFUT2	PATZ1	DYNLT3	SPIN4	PLXNA3	SLC10A3	MT-CYB

senescence_signatures.gmt

+Casella	https://doi.org/10.1093/nar/gkz555	TMEM159	CHPF2	SLC9A7	PLOD1	FAM234B	DHRS7	SRPX	SRPX2	TNFSF13B	PDLIM1	ELMOD1	CCND3	TMEM30A	STAT1	RND3	TMEM59	SARAF	SLCO2B1	ARRDC4	PAM	WDR78	CLSTN2	WDR63	NCSTN	SLC16A14	GPR155	CLDN1	JCAD	BLCAP	FILIP1L	TAP1	TNFRSF10C	SAMD9L	SMCO3	POFUT2	KIAA1671	LRP10	BMS1P9	MT-TA	MT-TN	MT-TC	MT-TY	DIO2	MAP4K3-DT	AC002480.1	LINC02154	TM4SF1-AS1	PTCHD4	H2AFJ	PURPL	MCUB	FBL	HIST1H1D	HIST1H1A	FAM129A	ANP32B	PARP1	LBR	SSRP1	TMSB15A	CBS	CDCA7L	HIST1H1E	CBX2	HIST2H2AB	PTMA	ITPRIPL1	AC074135.1	P16	P21	TP53
+Hernandez	https://doi.org/10.1016/j.cub.2017.07.033	ACADVL	ADPGK	ARHGAP35	ARID2	ASCC1	B4GALT7	BCL2L2	C2CD5	CCND1	CHMP5	CNTLN	CREBBP	DDA1	DGKA	DYNLT3	EFNB3	FAM214B	GBE1	GDNF	GSTM4	ICE1	KCTD3	KLC1	MEIS1	MT-CYB	NFIA	NOL3	P4HA2	PATZ1	PCIF1	PDLIM4	PDS5B	PLK3	PLXNA3	POFUT2	RAI14	RHNO1	SCOC	SLC10A3	SLC16A3	SMO	SPATA6	SPIN4	STAG1	SUSD6	TAF13	TMEM87B	TOLLIP	TRDMT1	TSPAN13	UFM1	USP6NL	ZBTB7A	ZC3H4	ZNHIT1
+Purcell	10.4161/15384101.2014.973327	APOL1	APOL3	ATF3	BATF2	BIRC3	C3	C9ORF47	S1PR3	CCDC80	CD163L1	CD82	CLDN1	COL17A1	MIR936	CTSS	CXCL2	CYP1A1	DUSP6	EPSTI1	GBP4	GCA	GMPR	GNG11	HERC5	ICAM1	IDO1	IFI27	IFI30	IL1A	IL1B	IL1RN	IL32	ISG20	ITGB3	LCP1	LGALS9	MIR4632	TNFRSF1B	MLKL	MMP12	MYD88	NEGR1	OASL	ODZ2	PLAU	PMAIP1	RSPO3	RTP4	SCN3A	TNFAIP3	TNFAIP6
+SenMayo	https://www.biorxiv.org/content/10.1101/2021.12.10.472095v1.full	ACVR1B	ANG	ANGPT1	ANGPTL4	AREG	AXL	BEX3	BMP2	BMP2	C3	CCL1	CCL13	CCL16	CCL2	CCL20	CCL24	CCL26	CCL3	CCL3L1	CCL4	CCL5	CCL7	CCL8	CD55	CD9	CSF1	CSF2	CSF2RB	CST4	CTNNB1	CTSB	CXCL1	CXCL10	CXCL12	CXCL16	CXCL2	CXCL3	CXCL8	CXCR2	DKK1	EDN1	EGF	EGFR	EREG	ESM1	ETS2	FAS	FGF1	FGF2	FGF7	GDF15	GEM	GMFG	HGF	HMGB1	ICAM1	ICAM3	IGF1	IGFBP1	IGFBP2	IGFBP3	IGFB4	IGFBP5	IGFBP6	IGFBP7	IL10	IL13	IL15	IL18	IL1A	IL1B	IL2	IL32	IL6	IL6ST	IL7	INHA	IQGAP2	ITGA2	ITPKA	JUN	KITLG	LCP1	MIF	MMP1	MMP10	MMP12	MMP13	MMP14	MMP2	MMP3	MMP9	NAP1L4	NRG1	PAPPA	PEMA1	PGF	PIGF	PLAT	PLAU	PLAUR	PTNP1	PTGER2	PTGES	RPS6KA5	SCAMP4	SELPLG	SEMA3F	SERPINB4	SERPUNINE1	SERPINE2	SPP1	SPX	TIMP2	TNF	TNFRSF10C	TNFRSF11B	TNFRSF1A	TNFRSF1B	TUBGCP2	VEGFA	VEGFC	VGF	WNT16	WNT2
+Sencan	Sencan	LCN2	0CST1	CST2	KLK5	MMP1	IL6	SAA1	SAA2	CRYAB	LUM	ACE2	CXCL10	CXCL11	EBI3	MMP10	IL1B	IL1A	CSF2	SERPINB2	MMP3	CLCA2	SBSN	CH3L2	TCN1	IGFL2	CILP	TAC3	COL10A1	ADAMTS6	EPYC	PSG6	PSG2	PSG7	CCL3	FOLR3	MXRA5	MMP12	IL368	TREM2	CD163	JCHAN	C5ORF46	SLAMF1	IGFL1	PPBP	MUC17	COL6A5	RPTN	IGHG1	BPIFC
+FRIDMAN_SENESCENCE_UP	Genes	ALDH1A3	AOPEP	CCN2	CCND1	CD44	CDKN1A	CDKN1C	CDKN2A	CDKN2B	CDKN2D	CITED2	CLTB	COL1A2	CREG1	CRYAB	CXCL14	CYP1B1	EIF2S2	ESM1	F3	FILIP1L	FN1	GSN	GUK1	HBS1L	HPS5	HSPA2	HTATIP2	IFI16	IFNG	IGFBP1	IGFBP2	IGFBP3	IGFBP4	IGFBP5	IGFBP6	IGFBP7	IGSF3	ING1	IRF5	IRF7	ISG15	MAP1LC3B	MAP2K3	MDM2	MMP1	NDN	NME2	NRG1	OPTN	PEA15	RAB13	RAB31	RAB5B	RABGGTA	RAC1	RBL2	RGL2	RHOB	RRAS	S100A11	SERPINB2	SERPINE1	SMPD1	SMURF2	SOD1	SPARC	STAT1	TES	TFAP2A	TGFB1I1	THBS1	TNFAIP2	TNFAIP3	TP53	TSPYL5	VIM
+Pribluda_SENESCENCE_INFLAMMATORY_GENES	Pribluda	ANG	ATF4	ATF5	AXL	BHLHE40	CCDC33	CCL20	CCL3	CD14	CD276	CD40	CD55	CD9	CDKN1A	CDKN1B	CDKN2B	CPA2	CPE	CSF2	CSF2RB	CXCL1	CXCL10	CXCL2	CXCL3	CXCL5	CXCL9	ETS1	ETS2	ETV5	FAIM2	FAM129A	FAS	GEM	GMFG	HAMP	HGF	HIF3A	ICAM1	ID1	IFIT1	IFIT2	IFIT3	IFITM3	IGF1	IGF2BP1	IGF2R	IGFBP1	IGFBP3	IGFBP4	IGFBP5	IGFBP6	IGFBP7	IL1A	IL1B	IL1F9	IL1RN	IL6	IL7	IL8	IL13	IL15	IQGAP2	ITGA2	ITPKA	JUN	LASS3	LPO	LSG15	MAPK11	MCP2	MCP4	MIF	MSX2	MX1	MX2	NXN	OAS2	OAS3	OLR1	PECAM1	PHLDA1	PIGF	PLA2G2A	PLA2G2F	PRSS22	PTGES	REL	RPS6KA5	RUNX1	SERPINE1	SLC7A11	SOX17	SOX4	TGFB1	TIMP2	TIRAP	TLR2	TNFRSF10B	TNFRSF19	TNFRSF8	TNIP2	USP18	WWC2	XAF1