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2026/4/20 17:07:50

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中国廉政建设网网站,广告公司简介介绍,广告设计与制作前景,宁波网站推广软件哪家强一、写在前面 pseudo-bulk#xff1a;把同一个细胞类型/同一群细胞的多个单细胞表达量直接相加/求和#xff0c;合成一个 “虚拟的bulk样本”。也许你会有疑惑#xff0c;我们花这么多钱测得单细胞#xff0c;不就是为了获得单细胞分辨率的转录组数据吗#xff1f;为何还…一、写在前面pseudo-bulk把同一个细胞类型/同一群细胞的多个单细胞表达量直接相加/求和合成一个 “虚拟的bulk样本”。也许你会有疑惑我们花这么多钱测得单细胞不就是为了获得单细胞分辨率的转录组数据吗为何还要将其再合并模拟生成bulk数据这其实为了解决单细胞稀疏性的问题pseudo-bulk对scRNA-seq数据求和后信号变强、噪声被平均掉。并且在scRNA-seq进行差异分析时细胞之间不是独立样本所以会干扰差异分析的准确度从而产生假阳性。故差异基因更可靠、更接近真实生物学。此前我们演示过pseudo bulk差异分析接下来只需要转换一下变量即可基于scRNA‑seq数据实现不同细胞类型间的pseudo bulk标记基因计算。本教程基于Linux环境的rstudio演示计算资源不足的同学可参考足够支持你完成硕博生涯的生信环境忘记宣传了独享用户连技术支持都是独享的访问链接https://biomamba.xiyoucloud.net/欢迎联系客服微信[Biomamba_zhushou]获取帮助如果需要单细胞数据分析教学、生信热点全文复现、自测数据个性化分析辅导、常态化实验学习欢迎联系客服微信[Biomamba_zhushou]。二、实操流程1、数据基本情况# 加载R包 library(Seurat)## Loading required package: SeuratObject## Loading required package: sp## SeuratObject was built with package Matrix 1.7.1 but the current ## version is 1.7.2; it is recomended that you reinstall SeuratObject as ## the ABI for Matrix may have changed## ## Attaching package: SeuratObject## The following objects are masked from package:base: ## ## intersect, tlibrary (dplyr)## ## Attaching package: dplyr## The following objects are masked from package:stats: ## ## filter, lag## The following objects are masked from package:base: ## ## intersect, setdiff, setequal, union# 读取数据 scRNA - readRDS(test_data/T1D_scRNA.rds) # 这个数据包含24个样本 levels(scRNA$sample)## NULL# 包含两个组别的数据 DimPlot(scRNA,split.by Group)# 取五种细胞类型用于下游演示 select_ct - paste0(celltype,1:5) scRNA - scRNA[,scRNA$cell_type %in% select_ct] DimPlot(scRNA,split.by Group)2、循环添加细胞类型专属变量for (run_ct in select_ct) { target_col - paste0(run_ct,_var)# 细胞类型专属变量的slot名称 scRNAmeta.data[,target_col] - scRNA$cell_type# 复制原始细胞变量 scRNAmeta.data[[target_col]][scRNAmeta.data[[target_col]] %% as.character() ! run_ct] - OtherS # 除了目标细胞类型外其它的都叫OtherS }3、循环生成pseudo-bulk数据并差异计算3.1 生成拟bulk对象bulk - AggregateExpression(scRNA, return.seurat T, slot counts, assays RNA, group.by c(cell_type, sample, Group,paste0(select_ct,_var))# 分别填写细胞类型、样本变量、分组变量的slot名称 )## Names of identity class contain underscores (_), replacing with dashes (-) ## Centering and scaling data matrix ## ## This message is displayed once every 8 hours.# 生成的是一个新的Seurat对象 bulk## An object of class Seurat ## 41056 features across 120 samples within 1 assay ## Active assay: RNA (41056 features, 0 variable features) ## 3 layers present: counts, data, scale.data3.2 pseudo-bulk计算细胞marker# 差异分析会用到bulk分析常用到的DESeq2 if(!require(DESeq2))BiocManager::install(DESeq2)## Loading required package: DESeq2## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics## ## Attaching package: BiocGenerics## The following objects are masked from package:dplyr: ## ## combine, intersect, setdiff, union## The following object is masked from package:SeuratObject: ## ## intersect## The following objects are masked from package:stats: ## ## IQR, mad, sd, var, xtabs## The following objects are masked from package:base: ## ## anyDuplicated, aperm, append, as.data.frame, basename, cbind, ## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, ## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, ## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, ## Position, rank, rbind, Reduce, rownames, sapply, saveRDS, setdiff, ## table, tapply, union, unique, unsplit, which.max, which.min## ## Attaching package: S4Vectors## The following objects are masked from package:dplyr: ## ## first, rename## The following object is masked from package:utils: ## ## findMatches## The following objects are masked from package:base: ## ## expand.grid, I, unname## Loading required package: IRanges## ## Attaching package: IRanges## The following objects are masked from package:dplyr: ## ## collapse, desc, slice## The following object is masked from package:sp: ## ## %over%## Loading required package: GenomicRanges## Loading required package: GenomeInfoDb## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats## ## Attaching package: matrixStats## The following object is masked from package:dplyr: ## ## count## ## Attaching package: MatrixGenerics## The following objects are masked from package:matrixStats: ## ## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, ## colCounts, colCummaxs, colCummins, colCumprods, colCumsums, ## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, ## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, ## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, ## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, ## colWeightedMeans, colWeightedMedians, colWeightedSds, ## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, ## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, ## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, ## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, ## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, ## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, ## rowWeightedMads, rowWeightedMeans, rowWeightedMedians, ## rowWeightedSds, rowWeightedVars## Loading required package: Biobase## Welcome to Bioconductor ## ## Vignettes contain introductory material; view with ## browseVignettes(). To cite Bioconductor, see ## citation(Biobase), and for packages citation(pkgname).## ## Attaching package: Biobase## The following object is masked from package:MatrixGenerics: ## ## rowMedians## The following objects are masked from package:matrixStats: ## ## anyMissing, rowMedians## ## Attaching package: SummarizedExperiment## The following object is masked from package:Seurat: ## ## Assays## The following object is masked from package:SeuratObject: ## ## Assays# 创建空的数据框 mk_df - data.frame() for (run_ct in select_ct) { # 改变默认分类变量 Idents(bulk) - paste0(run_ct,_var) # 下面的计算依赖DESeq2做过Bulk RNA-Seq的同学都知道 bulk_deg - FindMarkers(bulk, ident.1 run_ct, ident.2 OtherS, # 这样算出来的Fold Change就是run_ct/OtherS slot counts, test.use DESeq2,# 这里可以选择其它算法 verbose F# 关闭进度提示 ) bulk_deg$Celltype - run_ct# 添加一列收录当前进行差异计算的细胞类型 bulk_deg$gene - rownames(bulk_deg)# 添加基因列 mk_df - rbind(mk_df,bulk_deg)# 纵向合并数据框 }## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates# 计算top marker top3_mk - mk_df %% group_by(Celltype) %% top_n(n 3, wt avg_log2FC) top3_mk## # A tibble: 15 × 7 ## # Groups: Celltype [5] ## p_val avg_log2FC pct.1 pct.2 p_val_adj Celltype gene ## dbl dbl dbl dbl dbl chr chr ## 1 6.52e- 37 3.45 1 0.823 2.68e- 32 celltype1 ENSG00000227240 ## 2 3.00e- 20 3.89 0.958 0.25 1.23e- 15 celltype1 ENSG00000041515 ## 3 3.25e- 14 3.87 0.958 0.281 1.33e- 9 celltype1 ENSG00000163629 ## 4 1.74e- 65 4.50 1 0.396 7.15e- 61 celltype2 ENSG00000156395 ## 5 8.25e- 53 4.10 1 0.177 3.39e- 48 celltype2 ENSG00000238241 ## 6 3.55e- 16 4.22 0.875 0.25 1.46e- 11 celltype2 ENSG00000158966 ## 7 0 7.18 1 0.615 0 celltype3 ENSG00000144645 ## 8 0 7.40 1 0.625 0 celltype3 ENSG00000196092 ## 9 5.37e-116 7.18 1 0.156 2.20e-111 celltype3 ENSG00000150625 ## 10 3.34e- 18 4.05 1 0.604 1.37e- 13 celltype4 ENSG00000170624 ## 11 4.24e- 18 4.05 0.833 0.333 1.74e- 13 celltype4 ENSG00000276241 ## 12 9.19e- 10 3.61 0.667 0.073 3.77e- 5 celltype4 ENSG00000244649 ## 13 8.73e-126 6.45 1 0.229 3.58e-121 celltype5 ENSG00000165061 ## 14 1.17e- 57 5.45 0.958 0.229 4.80e- 53 celltype5 ENSG00000169744 ## 15 2.17e- 35 5.40 1 0.427 8.91e- 31 celltype5 ENSG00000149403# 更改默认标签为细胞类型 Idents(bulk) - bulk$cell_type # 小提琴图看看我们拟bulk获得的基因效果如何 VlnPlot(bulk,top3_mk$gene,stack T)4、热图绘制为了更有拟bulk的味道热图也可以用平均值来绘制#获得表达均值矩阵 avg - AverageExpression(scRNA,features top3_mk$gene)[[RNA]]## As of Seurat v5, we recommend using AggregateExpression to perform pseudo-bulk analysis. ## This message is displayed once per session.avg - as.data.frame(avg) library(pheatmap) # 绘制热图 pheatmap(avg[,select_ct], cluster_rows F,cluster_cols F, #行和列的聚类都关掉方便我们按顺序观察结果 show_colnames T, #展示横坐标的细胞分群 color colorRampPalette(c(navy, white, firebrick3))(50), #修改渐变色 scale row )三、演示环境一切不给测试文件和分析教程都是耍流氓本推送的代码和测试文件可以在以下链接中下载通过网盘分享的文件链接: https://pan.baidu.com/s/1RHUJksXVSHQKn8orPbiQmw提取码: yxr9