# 蛋白质组RPPA抗体与基因信息映射

之前通过TCGAbiolinks下载了蛋白质组数据，而其中peptide_target给的是抗体的名字，进行富集时需要进行ID转换，我们先来构造一个映射表。RPPA的抗体与基因映射关系在这里。

## 准备映射表

从官网下载手工版的映射表，整理成下面的格式，RPPA_Expanded_Ab_List_Updated、RPPA_Standard_Ab_List_Updated和the list of Updated Gene Names都需要。

## 清洗合并映射表

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    ad2g1 <- read.csv('sp_Ab2Gene.CSV')
    ad2g2 <- read.csv('sp_Ab2Gene_old.CSV')
    ad2g3 <- read.csv('sp_Ab2Gene_new.CSV')
    ad2g <- ad2g2[(ad2g2$Ab %in% ad2g1$Ab) == F,]
    ad2g <- rbind(ad2g, ad2g1, ad2g3)
    ad2g <- unique(ad2g)
    ad2g[order(ad2g$Gene),]

    ad2g_d <- list()
    ad2g_d$gene <- unique(ad2g$Gene)
    ad2g_d$raw <- ad2g
    ad2g_d$g2ad <- list()
    for (gene in ad2g_d$gene){
        ad2g_d$g2ad[[gene]] <- with(ad2g, Ab[Gene == gene])
    }
    ad2g_d$g2ad_c <- list()
    ad2g$Ab <- stringr::str_replace_all(string = tolower(ad2g$Ab), pattern = '(_-\\. /)', replacement = '')
    for (gene in ad2g_d$gene){
        ad2g_d$g2ad_c[[gene]] <- with(ad2g, Ab[Gene == gene])
    }

## 查看遗漏数据

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    load('PRAD_TCPA_DE.rdata')
    tmp <- stringr::str_replace_all(string = tolower(r1$peptide_target), pattern = '(_-\\. /)', replacement = '')
    r1[(tmp %in% ad2g$Ab) == F,]

通过搜索补全遗漏的映射表，添加到映射表中，重新清洗合并映射表

## 更改富集数据库的SYMBOL为Antibody

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    KEGG <- readRDS('../../../GSEA/kk_SYMBOL.rds')
    tmp_1 <- KEGG$TERM2GENE[ KEGG$TERM2GENE$gene %in% ad2g_d$gene, ]
    tmp_2 <- KEGG$TERM2GENE[ (KEGG$TERM2GENE$gene %in% ad2g_d$gene)==F, ]
    tmp_3 <- data.frame()
    for(i in 1:nrow(tmp_1)){
        tmp_4 <- ad2g_d$g2ad_c[[tmp_1[i, 'gene']]]
        tmp_5 <- rep(tmp_1[i, 'gsid'], length(tmp_4))
        tmp_6 <- data.frame(gsid=tmp_5, gene=tmp_4)
        tmp_3 <- rbind(tmp_3, tmp_6)
    }
    tmp_3 <- unique(tmp_3)
    KEGG_ab <- list()
    KEGG_ab$TERM2NAME <- KEGG$TERM2NAME[KEGG$TERM2NAME$gsid %in% unique(tmp_3$gsid),]
    KEGG_ab$TERM2GENE <- tmp_3
    saveRDS(KEGG_ab, 'KEGG_Ab.rds')

通路富集 数据清洗
蛋白质组RPPA抗体与基因信息映射
https://occdn.limour.top/2329.html
Author
Limour
Posted on
September 11, 2022
Licensed under
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