# clusterProfiler：GSEA基本绘图演示

## GSEA分析

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    require(clusterProfiler)
    require(enrichplot)
    rres <- readRDS('DEGs_X1.OE.DMSO_X2.OE.DMSO_vs._X1.control.DMSO_X2.control.DMSO_DESeq2.rds')
    lgene <- rres$log2FoldChange
    names(lgene) <- rres$symbol
    WP <- readRDS('WP.hsa.rds')
    WP$TERM2NAME$name <- gsub('%.*%Homo sapiens', '', WP$TERM2NAME$name)
    set.seed(0)
    gse.WP <- GSEA(gene = lgene,
                 pAdjustMethod = "fdr",
                 eps = 0,
                 pvalueCutoff = 0.1,
                 TERM2GENE = WP$TERM2GENE,
                 TERM2NAME = WP$TERM2NAME)
    require(tidyverse)
    require(enrichplot)
    require(DOSE)
    f_kegg_p <- function(keggr2, n = 15){
        keggr <- subset(keggr2@result, p.adjust < 0.05)
        keggr[['-log(Padj)']] <- -log10(keggr[['p.adjust']])
        keggr[['geneRatio']] <- parse_ratio(keggr[['GeneRatio']])
        keggr$Description <- factor(keggr$Description,
                                           levels=keggr[order(keggr$geneRatio),]$Description)
        ggplot(head(keggr,n),aes(x=geneRatio,y=Description))+
            geom_point(aes(color=`-log(Padj)`,
                     size=`Count`))+
            theme_bw()+
            scale_color_gradient(low="blue1",high="brown1")+
            labs(y=NULL) +
            theme(axis.text.x=element_text(angle=90,hjust = 1,vjust=0.5, size = 12),
                 axis.text.y=element_text(size = 15))
    }
    f_title <- function(gp,title){
        gp + labs(title = title) + theme(plot.title = element_text(hjust = 0.5))
    }
    gse.WP@result

## 通路总图

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    require(ggplot2)
    require(stringr)
    options(repr.plot.width=8, repr.plot.height=8)
    gse.WP@result['Ratio'] <- with(gse.WP@result, ((str_count(core_enrichment, '/') + 1) / setSize))
    gse.WP@result$Description <- with(gse.WP@result,factor(Description, levels = Description[order(NES)]))
    p <- ggplot(subset(gse.WP@result, NES>0),aes(NES,Description))
    p <- p + geom_point(aes(size=Ratio,color=-log10(qvalues))) + scale_size(range = c(5,10))
    p <- p + scale_color_continuous(low=rgb(1,0,0,0.5) ,high=rgb(0,0,1,0.5))
    p <- p + theme_bw() + theme(axis.text=element_text(size=20),axis.title=element_text(size=20))
    p <- p + scale_y_discrete(labels=function(y){str_wrap(y,width = 20)})
    p

## GSEA图

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    options(repr.plot.width=8, repr.plot.height=8)
    p <- gseaplot2(gse.WP, geneSetID=27, color = 'gray', ES_geom = 'dot', base_size = 20)
    p

## 通路网络图

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    gse.WP2 <- pairwise_termsim(gse.WP)
    p <- emapplot(gse.WP2, showCategory=subset(gse.WP@result, NES>0)$Description)
    p

## 通路基因图

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    #  Layout of the map, e.g. 'star', 'circle', 'gem', 'dh', 'graphopt', 'grid', 'mds', 'randomly', 'fr', 'kk', 'drl' or 'lgl'.
    options(repr.plot.width=8, repr.plot.height=8, ggrepel.max.overlaps = Inf)
    p <- cnetplot(gse.WP2, foldChange=lgene, showCategory=subset(gse.WP@result, NES>0)$Description, layout = "dh")
    p

通路富集
clusterProfiler：GSEA基本绘图演示
https://occdn.limour.top/2142.html
Author
Limour
Posted on
July 22, 2022
Licensed under
WGCNA：概念初探 Previous
从差异基因到RRA聚合 Next

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