# #——回声= FALSE,结果=“隐藏”,消息= FALSE ------------------------------- 需要(knitr)美元opts_chunk组(错误= FALSE,消息= FALSE,警告= FALSE) # #设置,呼应= FALSE,消息= FALSE ----------------------------------------- 库(残渣)库(BiocParallel)注册(SerialParam()) #避免fastMNN并行化问题。set.seed (100 ) ## ----------------------------------------------------------------------------- 南加州爱迪生公司库(scRNAseq) < - GrunPancreasData南加州爱迪生公司() ## ----------------------------------------------------------------------------- 库(天窗)qcstats < - perCellQCMetrics (sce) qcfilter < - quickPerCellQC南加州爱迪生公司(qcstats, percent_subsets =“altexps_ERCC_percent”)< -南加州爱迪生公司(,!qcfilter丢弃)总结(qcfilter抛弃美元 ) ## ----------------------------------------------------------------------------- 库(残渣)集群< - quickCluster南加州爱迪生公司(sce) < - computeSumFactors (sce、集群=集群)总结(sizeFactors (sce) sce < logNormCounts (sce ) ## ----------------------------------------------------------------------------- 12月< - modelGeneVar (sce)图(12月12月的意思是美元,美元,xlab =“意味着日志表达式”,ylab =“方差”)曲线(元数据(dec)美元趋势(x) =上校“蓝色”,添加= TRUE ) ## ----------------------------------------------------------------------------- 叫dec2 < - modelGeneVarWithSpikes (sce ERCC)情节(叫dec2的意思是美元,美元叫dec2, xlab =“意味着日志表达式”,ylab =“方差”)点(元数据(叫dec2)的意思是,美元的元数据(叫dec2) $ var坳=“红色”)曲线(元数据(叫dec2)美元趋势(x) =上校“蓝色”,添加= TRUE ) ## ---- fig.wide = TRUE, fig.asp = 1.5 ---------------------------------------------- # 为每个捐赠者关闭权重,以避免过度拟合。dec3 <- modelGeneVar(sce, block=sce$donor, density.weights=FALSE)Block <- dec3$per。block par(mfrow=c(3,2)) for (i in seq_along(per.block)) {decX <- per.block);块[[我]]阴谋(decX意味着美元,decX总美元,xlab =“意味着日志表达式”,ylab =“方差”,主要=名字(per.block)[我])曲线(元数据(decX)美元趋势(x) =上校“蓝色”,添加= TRUE ) } ## ----------------------------------------------------------------------------- # 得到10%的基因。上面。hvgs <- getTopHVGs(dec, prop=0.1) #获取前2000个基因。上面。hvgs2 <- getTopHVGs(dec, n=2000) # Get all genes with positive biological components. top.hvgs3 <- getTopHVGs(dec, var.threshold=0) # Get all genes with FDR below 5%. top.hvgs4 <- getTopHVGs(dec, fdr.threshold=0.05) ## ----------------------------------------------------------------------------- sce <- fixedPCA(sce, subset.row=top.hvgs) reducedDimNames(sce) ## ----------------------------------------------------------------------------- sced <- denoisePCA(sce, dec2, subset.row=getTopHVGs(dec2, prop=0.1)) ncol(reducedDim(sced, "PCA")) ## ----------------------------------------------------------------------------- output <- getClusteredPCs(reducedDim(sce)) npcs <- metadata(output)$chosen reducedDim(sce, "PCAsub") <- reducedDim(sce, "PCA")[,1:npcs,drop=FALSE] npcs ## ----------------------------------------------------------------------------- # In this case, using the PCs that we chose from getClusteredPCs(). g <- buildSNNGraph(sce, use.dimred="PCAsub") cluster <- igraph::cluster_walktrap(g)$membership # Assigning to the 'colLabels' of the 'sce'. colLabels(sce) <- factor(cluster) table(colLabels(sce)) ## ----------------------------------------------------------------------------- library(scater) sce <- runTSNE(sce, dimred="PCAsub") plotTSNE(sce, colour_by="label", text_by="label") ## ----------------------------------------------------------------------------- library(bluster) ratio <- pairwiseModularity(g, cluster, as.ratio=TRUE) library(pheatmap) pheatmap(log10(ratio+1), cluster_cols=FALSE, cluster_rows=FALSE, col=rev(heat.colors(100))) ## ----------------------------------------------------------------------------- ass.prob <- bootstrapStability(sce, FUN=function(x) { g <- buildSNNGraph(x, use.dimred="PCAsub") igraph::cluster_walktrap(g)$membership }, clusters=sce$cluster) pheatmap(ass.prob, cluster_cols=FALSE, cluster_rows=FALSE, col=colorRampPalette(c("white", "blue"))(100)) ## ----------------------------------------------------------------------------- subout <- quickSubCluster(sce, groups=colLabels(sce)) table(metadata(subout)$subcluster) # formatted as ' ． ' ## ----------------------------------------------------------------------------- # 使用集群信息从“colLabels (sce)默认:标记< - scoreMarkers (sce) colnames标记(标记[[1 ]]) ## ----------------------------------------------------------------------------- # 只显示前几列简洁。标记[[1]][顺序(标记[[1]]美元的意思。AUC,减少= TRUE), 1:4 ] ## ----------------------------------------------------------------------------- 标记< - scoreMarkers (sce full.stats = TRUE)标记[[1]]full.logFC美元。科恩 ## ----------------------------------------------------------------------------- # 使用第一个200 HVs,这是最有趣的。Of.interest <- top。hvgs [1:200] cor.pairs < - correlatePairs cor.pairs (, subset.row = of.interest) ## ----------------------------------------------------------------------------- cor.pairs2 < - correlatePairs南加州爱迪生公司(sce subset.row = of.interest块=美元捐赠 ) ## ----------------------------------------------------------------------------- cor.genes < - correlateGenes cor.genes (cor.pairs) ## ----------------------------------------------------------------------------- y < - convertTo(预计,type = "磨边机 ") ## ----------------------------------------------------------------------------- sessionInfo ()