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DEseq2_pipelin_DT.may_2021.r
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# DESeq2 piple ------------------------------------------------------------
setwd('/home/big/tanlikai/Demo_projects/DT/CleanData/')
library(DESeq2)
library(ggplot2)
library(ggrastr)
library(shadowtext)
library(ggrepel)
library(purrr)
library(cowplot)
library(magrittr)
library(dplyr)
library(stringr)
library(parallel)
library(vroom)
library(tibble)
library(limma)
library(clusterProfiler)
library(openxlsx)
# install.packages('rJava')
figsave <- function (p, filename, w =50, h = 60, device = cairo_pdf,
path = 'result/fig/',
scale = 1, units = 'mm', dpi = 300
) {
ggsave2( plot = p, filename = filename,device = device,
path = path, scale = scale, width = w, height = h,
units = units, dpi = dpi, limitsize = TRUE)
}
# creat count table -------------------------------------------------------
allcsv <- list.files(path = 'result', pattern = '.csv')
allcounts <- map(allcsv, ~ vroom(paste0('result/',.x)) %>% select(1,7,8) %>%
`colnames<-`(c('GeneID', 'GeneSymbol', str_remove(.x, '.csv'))) ) %>% set_names(allcsv)
allcounts$BCMI_1.count.csv
allcounts %<>% reduce(full_join, by = c('GeneID', 'GeneSymbol'))
allcounts %>% colnames
# remove duplicated genes
allcounts %<>% distinct(GeneSymbol, .keep_all = T)
allcounts %<>% column_to_rownames('GeneSymbol')
colnames(allcounts) <- str_remove_all(colnames(allcounts), '.count')
head(allcounts)
dim(allcounts)
rowmeans <- rowMeans(allcounts[,2:23]) %>% sort(decreasing = T)
rowsum <- rowSums(allcounts[,2:23]) %>% sort(decreasing = T)
rowmeans %>% quantile()
rowsum %>% quantile()
rowmeans[rowmeans>0]
# filter low read gene
after <- apply(allcounts[,2:17],1,function(x) length(x[x>10])>=3)
qcn <- after[after == 1] %>% names
allcounts_f <- allcounts[qcn,2:17]
allcounts
coldata <- data_frame(sample= colnames(allcounts_f), group = str_extract(colnames(allcounts_f), '.+(?=_\\d+)') %>% as.vector() %>% as.factor())
coldata
dds <- DESeqDataSetFromMatrix(countData = allcounts_f,
colData = coldata,
design= ~ group)
dds <- DESeq(dds)
dds@assays$mu
res <- results(dds, name="group_BCNMI_vs_BCMI")
res$Symbol <- rownames(res)
write.xlsx(res, 'BCNMI_vs_BCMI.xlsx')
res %>% as_tibble()%>% dplyr::filter(padj <0.05 & abs(log2FoldChange >=0.5)) %>% pull(Symbol)
plotMA(resLFC, ylim=c(-2,2))
summary(res)
plotCounts(dds, gene=which.min(res$padj), intgroup="group")
res
# GSEA --------------------------------------------------------------------
BCNMI_vs_BCMI<- clusterProfiler::bitr(res$Symbol, fromType="SYMBOL",
toType="ENTREZID", OrgDb="org.Hs.eg.db") %>% `colnames<-`(c('Symbol', 'ENTREZID')) %>%
right_join(as_tibble(res), by = 'Symbol')
BCNMI_vs_BCMI %<>% dplyr::mutate(significancy = case_when(padj >=0.05 | abs(log2FoldChange) < 1 ~ 'Unsignificant',
padj < 0.05 & log2FoldChange >=1 ~'BCNMI upreg',
padj < 0.05 & log2FoldChange <= -1 ~'BCMI upreg')) %>%
dplyr::filter(!is.na(padj))
BCNMI_vs_BCMI$log10padj = -log10(BCNMI_vs_BCMI$padj)
table(BCNMI_vs_BCMI$significancy)
top20genes <- BCNMI_vs_BCMI %>% filter(significancy != 'Unsignificant' & !grepl('^ENS|^RP|^LINC|-AS', Symbol)) %>%
group_by(significancy) %>%
top_n(20, abs(log2FoldChange)) %>% pull(Symbol)
BCNMI_vs_BCMI$top20 <- NULL
BCNMI_vs_BCMI %<>% mutate(top20 = case_when(Symbol %in% top20genes ~ Symbol))
Vol_DT <- Feature_rast(BCNMI_vs_BCMI,g = "significancy",
d1 = "log2FoldChange", d2 = "log10padj", noaxis = F, axis.number = T, do.label = F)+
color_m(c('#9B1B30','#2A4B7C','lightgrey'))+
ggtitle('BCMI vs BCNMI')+ylab('-log10(adjusted p value)')+
geom_hline(yintercept = -log10(0.05),linetype="dashed", size = 0.1)+
geom_vline(xintercept = c(-1,1),linetype="dashed", size = 0.1)+
geom_text_repel(aes(label=top20, fontface = 'italic'),show.legend = F, size = gs(8),
max.overlaps = 30,
segment.size = 0.3, segment.color = 'grey50',
bg.color = 'white', bg.r = 0.2,
na.rm = T)
figsave(Vol_DT, 'Volcano_DT.pdf', 120, 120)
BCNMI_vs_BCMI_heat <- BCNMI_vs_BCMI %>% filter(padj < 0.05 & !is.na(Symbol)) %>% distinct(Symbol, .keep_all = T) %>%
select(paste0('KO',1:3),paste0('WT',1:3),'Symbol') %>% `rownames<-`(NC_vs_Sup_top100$Symbol)
NC_vs_Sup_heat$Symbol <- NULL
pheatmap::pheatmap(NC_vs_Sup_heat,scale = 'row')
genelist <- BCNMI_vs_BCMI$log2FoldChange
names(genelist) <- BCNMI_vs_BCMI$ENTREZID
genelist <- sort(genelist, decreasing = T)
genelist
DT_GSEA_GO <- gseGO(geneList = genelist,
OrgDb = org.Hs.eg.db,
ont = "BP",
pAdjustMethod = "BH",
nPerm = 1000000,
minGSSize = 10,
pvalueCutoff = 0.05,
verbose = FALSE)%>%
clusterProfiler::simplify(by = 'p.adjust', select_fun = min) %>%
setReadable( OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
DT_GSEA_GO@result <- DT_GSEA_GO@result %>% arrange(desc(NES))
write.xlsx(DT_GSEA_GO, 'DT_GSEA_GO.xlsx')
library(msigdbr)
as.data.frame(msigdbr::msigdbr_collections())
C4_CGN <- msigdbr(category = 'C4') %>% dplyr::select(gs_name, entrez_gene)
c7 <- msigdbr(category = 'C7') %>% dplyr::select(gs_name, entrez_gene)
C6_onco_sig <- msigdbr(category = 'C6') %>% dplyr::select(gs_description, entrez_gene)
Hallmark <- msigdbr(category = 'H') %>% dplyr::select(gs_name, entrez_gene)
REACTOME <- msigdbr(category = 'C2', subcategory = 'CP:REACTOME') %>% dplyr::select(gs_name, entrez_gene)
C8 <- msigdbr(category = 'C8') %>% dplyr::select(gs_name, entrez_gene)
DT_GSEA_MsigDB_c7 <- GSEA(geneList = genelist, TERM2GENE=c7,
minGSSize = 20,
pvalueCutoff = 0.01, pAdjustMethod = "BH") %>%
setReadable( OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
DT_GSEA_MsigDB_c7@result <- DT_GSEA_MsigDB_c7@result %>% arrange(desc(NES))
DT_GSEA_MsigDB_c7@result %>% write.xlsx('DT_GSEA_MsigDB_c7result.xlsx')
DT_GSEA_MsigDB_c7@result %>% view
DT_GSEA_MsigDB_Hallmark <- GSEA(geneList = genelist, TERM2GENE=Hallmark,
minGSSize = 20,
pvalueCutoff = 0.01, pAdjustMethod = "BH") %>%
setReadable( OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
DT_GSEA_MsigDB_Hallmark@result %<>% arrange(desc(NES))
DT_GSEA_MsigDB_Hallmark %>% write.xlsx('DT_GSEA_MsigDB_Hllmarkresult.xlsx')
DT_GSEA_MsigDB_c6 <- GSEA(geneList = genelist, TERM2GENE=C6_onco_sig,
minGSSize = 20,
pvalueCutoff = 0.01, pAdjustMethod = "BH") %>%
setReadable( OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
DT_GSEA_MsigDB_c6@result %<>% arrange(desc(NES))
DT_GSEA_MsigDB_c6 %>% write.xlsx('DT_GSEA_MsigDB_C6_onco_sig.xlsx')
DT_GSEA_MsigDB_c4 <- GSEA(geneList = genelist, TERM2GENE=C4_CGN,
minGSSize = 20,
pvalueCutoff = 0.01, pAdjustMethod = "BH") %>%
setReadable( OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
DT_GSEA_MsigDB_c4@result %<>% arrange(desc(NES))
DT_GSEA_MsigDB_c4 %>% write.xlsx('DT_GSEA_MsigDB_C4_CGN.xlsx')
DT_GSEA_MsigDB_c8<- GSEA(geneList = genelist, TERM2GENE=C8,
minGSSize = 20,
pvalueCutoff = 0.01, pAdjustMethod = "BH") %>%
setReadable( OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
DT_GSEA_MsigDB_c8@result %<>% arrange(desc(NES))
DT_GSEA_MsigDB_c8 %>% write.xlsx('DT_GSEA_MsigDB_C8.xlsx')
DT_GSEA_MsigDB_c8$ID
# hallmarker dot plot
DT_GSEA_MsigDB_Hallmark_dot <- DT_GSEA_MsigDB_Hallmark@result %>%
mutate(group = case_when(NES > 0 ~ 'BCNMI', NES < 0 ~'BCMI')) %>%
mutate(Description = factor(Description, levels = rev(Description)))
DT_GSEA_MsigDB_Hallmark_dot$NES_abs <-abs( DT_GSEA_MsigDB_Hallmark_dot$NES)
DTDOT <- ggplot(DT_GSEA_MsigDB_Hallmark_dot, aes(x = group, y = Description))+
geom_point(aes(size = NES_abs, color = -log10(p.adjust)))+
scale_color_continuous(low = "blue", high = "red")+
# ggtitle('Enriched Gene Ontology')+
theme_minimal()+scale_y_discrete(position = "left")+
theme(axis.title = element_blank(),
axis.text = element_text(size = 8),
legend.key.width = unit(3,'mm'),
axis.text.y = element_text(size=8),
axis.text.x = element_text(size=8, angle = 45, hjust = 1),
# legend.position = 'bottom', legend.key.height = unit(2, 'mm'),
legend.title = element_text(size = 8), title = element_text(size = 8),
legend.text = element_text(size = 8))+
guides(size = guide_legend(title.position = 'top',title = 'absolute NES'),
color = guide_colorbar(title.position = 'top'))
figsave(DTDOT, 'GSEA_enrich_Hallmarks.pdf', 150, 150)
# heatmap
library(pheatmap)
library(ComplexHeatmap)
select <- order(rowMeans(counts(dds,normalized=TRUE)),
decreasing=TRUE)
select <- BCNMI_vs_BCMI %>% filter(significancy != 'Unsignificant' & abs(log2FoldChange) >3 ) %>% pull(Symbol)
ntd <- normTransform(dds)
color_group <- c('blue', 'red')
names(color_group) <- c('BCNMI', 'BCMI')
anc <- list(color_group = color_group)
df <- as.data.frame(colData(dds))[,c('sample', 'group')]
dt_heat<- pheatmap(assay(ntd)[select,], cluster_rows=T, show_rownames=T,
cluster_cols=T, annotation_col=df, annotation_colors = anc)
dt_heat
ggplotify::as.ggplot(dt_heat)
figsave(ggplotify::as.ggplot(dt_heat), 'heatmap.pdf', 150,270)
Heatmap(as.array(ntd))
as.numeric(assay(ntd))
assay(ntd)['CD3D',]