{"id":61742,"date":"2024-11-19T15:29:00","date_gmt":"2024-11-19T07:29:00","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61742"},"modified":"2024-11-19T15:29:00","modified_gmt":"2024-11-19T07:29:00","slug":"%e8%bf%98%e4%b8%8d%e4%bc%9a%e5%88%86%e6%9e%90rna-seq%e6%95%b0%e6%8d%ae%ef%bc%9f%e5%88%ab%e6%85%8c%ef%bc%8c%e5%b0%8f%e8%8a%b1%e5%b8%a6%e4%bd%a0%e5%ae%8c%e6%88%90%e6%95%b0%e6%8d%ae%e6%95%b4%e5%90%88","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61742","title":{"rendered":"\u8fd8\u4e0d\u4f1a\u5206\u6790RNA-seq\u6570\u636e\uff1f\u522b\u614c\uff0c\u5c0f\u82b1\u5e26\u4f60\u5b8c\u6210\u6570\u636e\u6574\u5408\u548c\u5dee\u5f02\u5206\u6790"},"content":{"rendered":"\n

<\/h1>\n\n\n\n

\u5927\u5bb6\u597d\u554a\uff0c\u76f8\u4fe1\u5927\u5bb6\u5df2\u7ecf\u5b66\u4f1a\u5982\u4f55\u4ece\u5934\u5904\u7406\u82af\u7247\u539f\u59cb\u6570\u636e\u4e86\uff0c\u5728\u516c\u5171\u6570\u636e\u5e93\u4e2d\u6211\u4eec\u8fd8\u7ecf\u5e38\u4f1a\u9047\u5230\u8f6c\u5f55\u7ec4\u6570\u636e\uff0c\u8f6c\u5f55\u7ec4\u6570\u636e\u662f\u65e0\u6cd5\u7528R\u5305\u4e0b\u8f7d\u7684\uff0c\u56e0\u4e3a\u8f6c\u5f55\u7ec4\u6570\u636e\u7684Series Matrix File(s)<\/a>\u6ca1\u6709\u57fa\u56e0\u8868\u8fbe\u91cf\u6570\u636e\uff0c\u53ea\u6709\u4e34\u5e8a\u6570\u636e\uff0c\u6240\u4ee5\u6bcf\u6b21\u5904\u7406\u8f6c\u5f55\u7ec4\u6570\u636e\u6211\u4eec\u90fd\u8981\u5728Supplementary file\u4e0b\u8f7d\u6587\u4ef6\u7136\u540e\u5904\u7406\uff0c\u4f46\u662f\u5f88\u4e0d\u5de7\u7684\u662f\u5f88\u5c11\u6709\u4f5c\u8005\u4f1a\u4e0a\u4f20\u4ed6\u4eec\u6574\u5408\u597d\u7684\u6570\u636e\uff0c\u5927\u90e8\u5206\u4f5c\u8005\u90fd\u4f1a\u4e0a\u4f20\u672a\u6574\u5408\u7684\u6570\u636e\uff0c\u8fd9\u65f6\u5019\u6211\u4eec\u9700\u8981\u4ece\u5934\u5f00\u59cb\u6574\u5408\u6570\u636e\u3002\u4e0b\u9762\u6211\u4eec\u5c31\u4ee5GSE220042\u4e3a\u4f8b\u6765\u6559\u5927\u5bb6\u5982\u4f55\u5904\u7406\u5e76\u5206\u6790RNA-seq\u7684\u6570\u636e\u3002\u8981\u662f<\/strong>\u60a8<\/strong>\u6709\u81ea\u5df1\u505a\u4e0d\u4e86\u7684\u751f\u4fe1\u5206\u6790\uff0c\u53ef\u4ee5\u8054\u7cfb<\/strong>\u6211\u3002<\/strong><\/p>\n\n\n\n

\u9996\u5148\u4e0b\u8f7d\u8fd9\u91cc\u7684\u8865\u5145\u6587\u4ef6\uff0c<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u8fd9\u662f\u6837\u672c\u91cf\u6bd4\u8f83\u5c0f\u7684\u6570\u636e\uff0c\u6709\u7684\u6837\u672c\u91cf\u6bd4\u8f83\u5927\u7684\u6570\u636e\u53ef\u80fd\u4f1a\u6709\u597d\u51e0\u4e2aG\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u4f7f\u7528\u670d\u52a1\u5668\u6765\u5904\u7406\uff0c\u5982\u679c\u60a8\u7684\u6570\u636e\u91cf\u6bd4\u8f83\u5927\u53ef\u4ee5\u8054\u7cfb\u5c0f\u82b1\u79df\u8d41\u9ad8\u6027\u4ef7\u6bd4\u7684\u670d\u52a1\u5668\u54e6<\/strong>\u3002<\/strong>\u56e0\u4e3a\u670d\u52a1\u5668\u4e00\u822c\u7528\u547d\u4ee4\u884c\u6765\u4e0b\u8f7d\u6570\u636e\uff0c\u6240\u4ee5\u8fd9\u65f6\u5019\u9700\u8981\u7a0d\u5fae\u64cd\u4f5c\u4e00\u4e0b\uff0c\u5efa\u8bae\u4f7f\u7528axel\u591a\u7ebf\u7a0b\u5730\u4e0b\u8f7d\u6570\u636e\uff0caxel\u53ef\u4ee5\u901a\u8fc7conda\u5b89\u88c5\uff1a<\/p>\n\n\n\n

axel -n 20 ftp:\/\/ftp.ncbi.nlm.nih.gov\/geo\/series\/GSE\u53f7\u524d\u51e0\u4f4dnnn\/GSE\u53f7\/suppl\/\u6587\u4ef6\u540d<\/p>\n\n\n\n

\u4ee5\u6211\u4eec\u7684\u793a\u4f8b\u6570\u636e\u4e3a\u4f8b\uff1a<\/p>\n\n\n\n

axel -n 20 ftp:\/\/ftp.ncbi.nlm.nih.gov\/geo\/series\/GSE220nnn\/GSE220042\/suppl\/GSE220042_RAW.tar<\/p>\n\n\n\n

\u4e0b\u9762\u6211\u4eec\u5c31\u6765\u770b\u4e00\u4e0b\u5982\u4f55\u5904\u7406\u8fd9\u4e2a\u6570\u636e\u5427\u3002\u9996\u5148\u5c06\u6570\u636e\u4e0b\u8f7d\u4e0b\u6765\u5e76\u89e3\u538b\uff0c<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u7136\u540e\u7528\u6211\u63d0\u4f9b\u7684\u8fd9\u4e2a\u4ee3\u7801\u8fdb\u884c\u6570\u636e\u5408\u5e76\uff1a<\/p>\n\n\n\n

## \u83b7\u53d6\u6837\u672c\u4fe1\u606f<\/p>\n\n\n\n

library(GEOquery)<\/p>\n\n\n\n

GSE_id = “GSE220042”<\/p>\n\n\n\n

gset <- getGEO(GSE_id, destdir=”..\/output\/”,<\/p>\n\n\n\n

AnnotGPL = T,     ## \u6ce8\u91ca\u6587\u4ef6<\/p>\n\n\n\n

getGPL = T)       ## \u5e73\u53f0\u6587\u4ef6<\/p>\n\n\n\n

pd = pData(gset[[1]])<\/p>\n\n\n\n

## \u8bfb\u53d6\u539f\u59cb\u6570\u636e<\/p>\n\n\n\n

#### \u5148\u770b\u4e00\u4e0b\u6211\u4eec\u60f3\u8981\u54ea\u4e9b\u5217<\/p>\n\n\n\n

library(data.table)<\/p>\n\n\n\n

files = list.files(paste0(“..\/input\/”, GSE_id, “_RAW\/”), full.names = TRUE)<\/p>\n\n\n\n

samples = str_extract(files, “(GSM\\\\d+)_”, group = 1)<\/p>\n\n\n\n

tmp = fread(files[1])<\/p>\n\n\n\n

head(tmp)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u8fd9\u4e2a\u6570\u636e\u53ea\u6709\u4e00\u4e2aV1:ensembl_id\u548cV2:count\u3002\u4e0b\u9762\u6211\u4eec\u6765\u5408\u5e76\u8fd9\u4e9b\u6570\u636e\uff1a<\/p>\n\n\n\n

all_data = list()<\/p>\n\n\n\n

for(i in 1:length(files)){<\/p>\n\n\n\n

all_data[[i]] = as.data.frame(fread(files[i]))<\/p>\n\n\n\n

rownames(all_data[[i]]) = all_data[[i]]$V1<\/p>\n\n\n\n

all_data[[i]] = all_data[[i]][, -1, drop = FALSE]<\/p>\n\n\n\n

colnames(all_data[[i]])[1] = samples[i]<\/p>\n\n\n\n

}<\/p>\n\n\n\n

data_count = Reduce(cbind, all_data)<\/p>\n\n\n\n

head(data_count)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u7136\u540e\u5f00\u59cb\u5bf9\u57fa\u56e0\u8fdb\u884c\u6ce8\u91ca\uff0c\u5e76\u4e14\u76f8\u540c\u57fa\u56e0\u53d6\u5747\u503c\uff1a<\/p>\n\n\n\n

## \u6ce8\u91ca\u57fa\u56e0\u540d<\/p>\n\n\n\n

library(clusterProfiler)<\/p>\n\n\n\n

library(org.Mm.eg.db)<\/p>\n\n\n\n

library(stringr)<\/p>\n\n\n\n

ori_id = data.frame(id = rownames(data_count))<\/p>\n\n\n\n

ori_id$ori = str_extract(ori_id$id, “(.*?)\\\\.”, group = 1)<\/p>\n\n\n\n

geneName = bitr(ori_id$ori,<\/p>\n\n\n\n

fromType = ‘ENSEMBL’,<\/p>\n\n\n\n

toType = ‘SYMBOL’,<\/p>\n\n\n\n

OrgDb = ‘org.Mm.eg.db’)<\/p>\n\n\n\n

geneName = merge(ori_id, geneName, by.x = “ori”, by.y = “ENSEMBL”)<\/p>\n\n\n\n

data_count$id = rownames(data_count)<\/p>\n\n\n\n

data_count = merge(data_count, geneName, by.x = “id”, by.y = “id”)<\/p>\n\n\n\n

data_count_unique = aggregate(data_count[,c(2:13)], by = list(data_count$SYMBOL), mean)<\/p>\n\n\n\n

rownames(data_count_unique) = data_count_unique$gene<\/p>\n\n\n\n

data_count_unique = data_count_unique[,-1]<\/p>\n\n\n\n

## \u53ea\u8981\u5747\u503c\u5927\u4e8e1\u7684\u57fa\u56e0<\/p>\n\n\n\n

data_count_unique = data_count_unique[which(rowMeans(data_count_unique)>1),]<\/p>\n\n\n\n

write.csv(data_count_unique, “..\/output\/data_GSE220042_count.csv”)<\/p>\n\n\n\n

\u7136\u540e\u8fdb\u884c\u5dee\u5f02\u5206\u6790\uff1a<\/p>\n\n\n\n

## \u5dee\u5f02\u5206\u6790<\/p>\n\n\n\n

brefInfo = data.frame(sample = rownames(pd), type1 = pd$`genotype:ch1`)<\/p>\n\n\n\n

brefInfo$group = ifelse(brefInfo$type1==”WT”, “control”, “disease”)<\/p>\n\n\n\n

write.csv(brefInfo, “..\/output\/brefInfo_GSE220042.csv”)<\/p>\n\n\n\n

library(DESeq2)<\/p>\n\n\n\n

## \u56e0\u4e3a\u524d\u9762\u53d6\u4e86\u5747\u503c\uff0c\u6240\u4ee5\u4f1a\u6709\u5c0f\u6570\uff0c\u8fd9\u91cc\u7528round\u53ea\u4fdd\u7559\u6574\u6570\u90e8\u5206<\/p>\n\n\n\n

brefInfo$group = factor(brefInfo$group, levels = c(“control”, “disease”))  ## \u611f\u5174\u8da3\u7684\u7c7b\u522b\u653e\u540e\u9762<\/p>\n\n\n\n

dds <- DESeqDataSetFromMatrix(countData = round(data_count_unique), colData = brefInfo, design = ~ group)<\/p>\n\n\n\n

dds1 <- DESeq(dds, fitType = ‘mean’, minReplicatesForReplace = 7, parallel = FALSE)<\/p>\n\n\n\n

res <- results(dds1)<\/p>\n\n\n\n

summary(res)<\/p>\n\n\n\n

res1 <- data.frame(res, stringsAsFactors = FALSE, check.names = FALSE)<\/p>\n\n\n\n

res1 <- res1[order(res1$log2FoldChange, res1$pvalue, decreasing = c(TRUE, FALSE)), ]<\/p>\n\n\n\n

## \u8bbe\u7f6e\u5dee\u5f02\u9608\u503cpvalue<0.05\u4e14abs(log2FC)>1<\/p>\n\n\n\n

res1$regulation = ifelse(res1$pvalue<0.05 & abs(res1$log2FoldChange)>1,<\/p>\n\n\n\n

ifelse(res1$log2FoldChange>0, “up”, “down”), “stable”)<\/p>\n\n\n\n

head(res1)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u4f46\u662f\u6709\u65f6\u5019\u7ed3\u679c\u91cc\u4f1a\u6709\u4e00\u4e9bNA\uff0c\u53ef\u80fd\u662f\u56e0\u4e3a\u8fd9\u4e9b\u539f\u56e0\uff1a<\/p>\n\n\n\n

    \n
  1. \u6240\u6709\u6837\u672c\u8ba1\u6570\u4e3a\u96f6\uff1a\u5982\u679c\u5728\u4e00\u884c\u4e2d\uff0c\u6240\u6709\u6837\u672c\u7684\u8ba1\u6570\u90fd\u4e3a\u96f6\uff0c\u5219\u57fa\u7840\u5e73\u5747\u503c\uff08baseMean\uff09\u5217\u5c06\u4e3a\u96f6\uff0clog2\u6298\u53e0\u53d8\u5316\u4f30\u8ba1\u3001p\u503c\u548c\u8c03\u6574\u540e\u7684p\u503c\u90fd\u5c06\u88ab\u8bbe\u7f6e\u4e3aNA\u3002<\/li>\n\n\n\n
  2. \u4f4e\u5e73\u5747\u5f52\u4e00\u5316\u8ba1\u6570\uff1a\u5982\u679c\u4e00\u884c\u7684\u5e73\u5747\u5f52\u4e00\u5316\u8ba1\u6570\u8f83\u4f4e\uff0c\u5b83\u53ef\u80fd\u4f1a\u88abDESeq2\u81ea\u52a8\u72ec\u7acb\u8fc7\u6ee4\u6389\uff0c\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u53ea\u6709\u8c03\u6574\u540e\u7684p\u503c\u5c06\u88ab\u8bbe\u7f6e\u4e3aNA\u3002<\/li>\n\n\n\n
  3. \u8fc7\u6ee4\u5668\u79fb\u9664\uff1a\u5728DESeq2\u7684\u9884\u8fc7\u6ee4\u6b65\u9aa4\u4e2d\uff0c\u53ef\u80fd\u4f1a\u79fb\u9664\u8868\u8fbe\u91cf\u6781\u4f4e\u7684\u57fa\u56e0\uff0c\u8fd9\u4e5f\u53ef\u80fd\u5bfc\u81f4p\u503c\u4e3aNA\u3002<\/li>\n\n\n\n
  4. \u5206\u6563\u4f30\u8ba1\u95ee\u9898\uff1a\u5728\u4f30\u8ba1\u57fa\u56e0\u8868\u8fbe\u7684\u5206\u6563\u5ea6\u65f6\uff0c\u5982\u679c\u65e0\u6cd5\u51c6\u786e\u4f30\u8ba1\uff0c\u4e5f\u53ef\u80fd\u5bfc\u81f4p\u503c\u4e3aNA\u3002<\/li>\n\n\n\n
  5. \u6837\u672c\u5927\u5c0f\u4e0d\u8db3\uff1a\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u5982\u679c\u6837\u672c\u91cf\u592a\u5c0f\uff0c\u53ef\u80fd\u65e0\u6cd5\u8fdb\u884c\u6709\u6548\u7684\u7edf\u8ba1\u63a8\u65ad\uff0c\u8fd9\u4e5f\u53ef\u80fd\u5bfc\u81f4p\u503c\u4e3aNA\u3002<\/li>\n<\/ol>\n\n\n\n

    \u505a\u5b8c\u5dee\u5f02\u5206\u6790\u6211\u4eec\u8fd8\u6709\u540e\u7eed\u5206\u6790\uff0c\u4e00\u822c\u9700\u8981\u63d0\u53d6\u51faDESeq\u7ed3\u679c\u4e2d\u7684\u6807\u51c6\u5316\u6570\u636e\uff0cDESeq2\u5728\u6807\u51c6\u5316\u8f6c\u5f55\u7ec4\u6570\u636e\u65f6\uff0c\u91c7\u7528\u7684\u662f\u4e00\u79cd\u79f0\u4e3a\u201csize factor\u201d\u7684\u65b9\u6cd5\u3002\u8fd9\u79cd\u65b9\u6cd5\u7684\u76ee\u7684\u662f\u6d88\u9664\u6837\u672c\u95f4\u7531\u4e8e\u6d4b\u5e8f\u6df1\u5ea6\u4e0d\u540c\u5bfc\u81f4\u7684\u7cfb\u7edf\u504f\u5dee\u3002DESeq2\u4f1a\u8ba1\u7b97\u6bcf\u4e2a\u6837\u672c\u7684size factor\uff0c\u5e76\u7528\u5b83\u6765\u8c03\u6574\u6bcf\u4e2a\u6837\u672c\u4e2d\u57fa\u56e0\u7684\u8ba1\u6570\u503c\uff0c\u4ece\u800c\u4f7f\u5f97\u4e0d\u540c\u6837\u672c\u4e4b\u95f4\u7684\u8ba1\u6570\u503c\u53ef\u6bd4\u3002\u8fd9\u79cd\u65b9\u6cd5\u5047\u8bbe\u5927\u591a\u6570\u57fa\u56e0\u7684\u8868\u8fbe\u5728\u6240\u6709\u6837\u672c\u4e2d\u662f\u4e0d\u53d8\u7684\uff0c\u56e0\u6b64\u53ef\u4ee5\u901a\u8fc7\u6bd4\u8f83\u6240\u6709\u6837\u672c\u4e2d\u57fa\u56e0\u8868\u8fbe\u7684\u4e2d\u4f4d\u6570\u6765\u4f30\u8ba1\u6d4b\u5e8f\u6df1\u5ea6\u7684\u53d8\u5316\u3002\u8fd9\u6837\uff0c\u5373\u4f7f\u4e0d\u540c\u6837\u672c\u7684\u6d4b\u5e8f\u91cf\u4e0d\u540c\uff0c\u901a\u8fc7size factor\u6807\u51c6\u5316\u540e\uff0c\u4e5f\u53ef\u4ee5\u5728\u6837\u672c\u95f4\u8fdb\u884c\u516c\u5e73\u7684\u6bd4\u8f83\u3002DESeq2\u7684\u8fd9\u79cd\u6807\u51c6\u5316\u65b9\u6cd5\u662f\u4e13\u95e8\u4e3a\u8ba1\u6570\u6570\u636e\u8bbe\u8ba1\u7684\uff0c\u5b83\u8003\u8651\u4e86\u8ba1\u6570\u6570\u636e\u7684\u79bb\u6563\u6027\u8d28\uff0c\u5e76\u4e14\u80fd\u591f\u9002\u5e94\u4e0d\u540c\u7684\u6d4b\u5e8f\u6df1\u5ea6\u548c\u6837\u672c\u590d\u6742\u6027\uff0c\u8fd9\u5728\u8f6c\u5f55\u7ec4\u5b66\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u6709\u7528\u3002<\/p>\n\n\n\n

    ## \u63d0\u53d6\u6807\u51c6\u5316\u540e\u7684\u6570\u636e<\/p>\n\n\n\n

    data_norm = counts(dds1, normalized=TRUE)<\/p>\n\n\n\n

    boxplot(data_norm)<\/p>\n\n\n\n

    boxplot(log2(data_norm+1))<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n
    \"\"<\/figure>\n\n\n\n

    \u6839\u636e\u4f60\u7684\u9700\u6c42\u53d6\u6807\u51c6\u5316\u540e\u7684\u6570\u636e\u6216\u8005log2(\u6807\u51c6\u5316\u6570\u636e)\u505a\u540e\u7eed\u5206\u6790\u5c31\u53ef\u4ee5\u4e86\u3002<\/p>\n\n\n\n

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