GSE120706為含兩組（Mock和Hsv1）樣本的二代測序數(shù)據(jù)，平臺為GPL24247 Illumina NovaSeq 6000 (Mus musculus)，請進行數(shù)據(jù)加載、清洗，探針I(yè)D轉(zhuǎn)換和數(shù)據(jù)可視化（包括差異分析、富集分析和互作分析）。

參考鏈接： https://cloud.tencent.com/developer/article/2206152

############################GEO，單樣本數(shù)據(jù)，RNA-seq差異表達分析代碼（GSE120706）####################### load("GSE120706.Rdata") rownames(data) = data$GeneID # 加載必要的包 library(edgeR)

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library(DESeq2)

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library(FactoMineR) library(factoextra)

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## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(clusterProfiler)

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## clusterProfiler v4.12.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/ ##  ## Please cite: ##  ## G Yu. Thirteen years of clusterProfiler. The Innovation. 2024, ## 5(6):100722

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library(org.Mm.eg.db)

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library(stringr) library(tidyverse)

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library(ggplot2) library(patchwork) library(pheatmap) library(EnhancedVolcano)

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library(RColorBrewer) ## 1. 數(shù)據(jù)預(yù)處理 ------------------------------------------------------------ # 假設(shè)data是原始計數(shù)矩陣，包含兩列(Mock和HSV) rawcount <- data[,2:3]   # 更嚴(yán)格的基因過濾 - 至少在75%樣本中count>1 keep <- rowSums(rawcount > 1) >= floor(0.75 * ncol(rawcount)) filter_count <- rawcount[keep, ] # 創(chuàng)建DGEList對象 dge <- DGEList(counts = filter_count,                 group = rep(c("Mock", "HSV"), each = 1)) # TMM標(biāo)準(zhǔn)化并計算CPM值 dge <- calcNormFactors(dge) express_cpm <- cpm(dge, log = TRUE, prior.count = 1) ## 2. 質(zhì)控可視化 ----------------------------------------------------------- # 箱線圖函數(shù) plot_box <- function(expr_data, title = "Expression Distribution") {   expr_data %>%     as.data.frame() %>%     pivot_longer(everything(), names_to = "sample", values_to = "expression") %>%     ggplot(aes(x = sample, y = expression, fill = sample)) +     geom_boxplot() +     scale_fill_brewer(palette = "Set2") +     labs(x = NULL, y = "log2(CPM+1)", title = title) +     theme_bw() +     theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) } # PCA圖函數(shù) plot_pca <- function(expr_data, groups) {   pca_res <- prcomp(t(expr_data), scale. = TRUE)   fviz_pca_ind(pca_res,                geom.ind = "point",                col.ind = groups,                palette = c("#00AFBB", "#E7B800"),                addEllipses = TRUE,                ellipse.type = "confidence",                legend.title = "Groups",                title = "PCA - Sample Clustering") +     theme_bw() } # 繪制質(zhì)控圖 p_box <- plot_box(express_cpm) p_pca <- plot_pca(express_cpm, rep(c("Mock", "HSV"), each = 1)) # 使用patchwork組合圖形 p_box + p_pca + plot_layout(widths = c(1, 2))

## Warning: Computation failed in `stat_conf_ellipse()`. ## Caused by error in `if (scale[1] > 0) ...`: ## ! 需要TRUE/FALSE值的地方不可以用缺少值

## 3. 差異表達分析 --------------------------------------------------------- # 轉(zhuǎn)換基因ID (ENSEMBL到SYMBOL) convert_ids <- function(count_matrix) {   ids <- str_split(rownames(count_matrix), "\\.", simplify = TRUE)[,1]   id_map <- bitr(ids,                   fromType = "ENSEMBL",                  toType = "SYMBOL",                  OrgDb = org.Mm.eg.db) # 保留唯一映射的基因   id_map <- id_map[!duplicated(id_map$SYMBOL), ]   count_matrix %>%     as.data.frame() %>%     mutate(ENSEMBL = str_split(rownames(.), "\\.", simplify = TRUE)[,1]) %>%     inner_join(id_map, by = "ENSEMBL") %>%     dplyr::select(-ENSEMBL) %>%     column_to_rownames("SYMBOL") %>%     as.matrix() } # 轉(zhuǎn)換ID expr_set <- convert_ids(filter_count)

## 'select()' returned 1:many mapping between keys and columns

## Warning in bitr(ids, fromType = "ENSEMBL", toType = "SYMBOL", OrgDb = ## org.Mm.eg.db): 0.43% of input gene IDs are fail to map...

# 使用edgeR進行差異分析 run_edger <- function(count_matrix, groups) {   dge <- DGEList(counts = count_matrix, group = groups)   dge <- calcNormFactors(dge) # 當(dāng)沒有生物學(xué)重復(fù)時，使用預(yù)設(shè)dispersion if (ncol(count_matrix) <= 2) {     message("No replicates - using preset dispersion (0.1)")     bcv <- 0.1     et <- exactTest(dge, dispersion = bcv^2)   } else {     dge <- estimateDisp(dge)     et <- exactTest(dge)   }   topTags(et, n = nrow(count_matrix))$table %>%     as.data.frame() %>%     rownames_to_column("gene") %>%     mutate(regulated = case_when(       FDR < 0.01 & logFC > 1 ~ "up",       FDR < 0.01 & logFC < -1 ~ "down",       TRUE ~ "ns"     )) } # 執(zhí)行差異分析 de_results <- run_edger(expr_set, rep(c("Mock", "HSV"), each = 1))

## No replicates - using preset dispersion (0.1)

# 查看差異基因統(tǒng)計 table(de_results$regulated)

##  ## down   ns   up  ## 2134 9558 2001

## 4. 結(jié)果可視化 ----------------------------------------------------------- # 火山圖 EnhancedVolcano(de_results,                 lab = de_results$gene,                 x = "logFC",                 y = "FDR",                 pCutoff = 0.01,                 FCcutoff = 2,                 title = "HSV vs Mock",                 subtitle = "Differential Expression",                 legendPosition = "right")