## ----选项，结果=“隐藏”，包括= false，cache = false，结果='hide'，message = false ---- knitr :: opts_chunk $ set（fig.align =“center”，cache = false，错误= false，#make它停止错误fidth = 7，fig.height = 7，autodep = true，out.width =“600px”，out.height =“600px”，结果=“marpup“，echo = true，eval = true）#kntr :: opts_knit $ set（stop_on_error = 2l）#really使它停止#knitr :: dep_auto（）选项（getClass.msg = false）#get摆脱了关于缓存的恼人消息直到内部固定在R set.seed（98883）##为再现性库（BioC2016SingleCell）库（Slingshot）库（Clustexperiment）库（GAM）## ---- Datain ----------------------------------------------------------------------------数据（'full_pca'）##检查维数减少#plot3d（pcax，aseque ='iso'）对（pcax [，1：3]，asp = 1）## ---- Line_Unsup ----------------------------------------------------------------------l1 < - get_lineages（pcax，clus）#plot_tree（pcax，clus，l1，threed = true）plot_tree（pcax，clus，l1，dim = 3）## ----线_sup_s酸 - - - - - - - - - - - - - - - - - - - - - - - - ----- l2 < - get_linegeges（pcax，clus，start.clus ='m10'）#plot_tree（pcax，clus，l2，threed = true）plot_tree（pcax，clus，l2，dim = 3）## ---lines_sup_end --------------------------------------- --------- l3 < - get_lineage（pcax，clus，start.clus ='m10'，end.clus ='m17'）#plot_tree（pcax，clus，l3，threed = true）plot_tree（pcax，clus，l3，dim = 3）## ----曲线-------------------------------------------------------- crv <- get_curves(pcaX, clus, l2) # plot_curves(pcaX, clus, c, threeD = TRUE) plot_curves(pcaX, clus, crv, dim = 3) ## ----genedata------------------------------------------------------------ data('var_genes') ## ----fitgam-------------------------------------------------------------- gam.pval <- vector("list",length(crv)) for(l in 1:length(crv)){ t <- crv[[l]]$pseudotime y <- vargenes[,! is.na(t)] t <- t[! is.na(t)] gam.pval[[l]] <- apply(y,1,function(z){ d <- data.frame(z=z, t=t) tmp <- gam(z ~ lo(t), data=d) p <- summary(tmp)[4][[1]][1,5] p }) } ## ----heatmaps------------------------------------------------------------ topgenes1 <- names(sort(gam.pval[[1]], decreasing = FALSE))[1:100] heatdata1 <- vargenes[rownames(vargenes) %in% topgenes1, order(crv[[1]]$pseudotime, na.last = NA)] heatclus1 <- clus[order(crv[[1]]$pseudotime, na.last = NA)] ce1 <- clusterExperiment(heatdata1, heatclus1, transformation=identity) plotHeatmap(ce1, clusterSamplesData="orderSamplesValue") topgenes2 <- names(sort(gam.pval[[2]], decreasing = FALSE))[1:100] heatdata2 <- vargenes[rownames(vargenes) %in% topgenes2, order(crv[[2]]$pseudotime, na.last = NA)] heatclus2 <- clus[order(crv[[2]]$pseudotime, na.last = NA)] ce2 <- clusterExperiment(heatdata2, heatclus2, transformation=identity) plotHeatmap(ce2, clusterSamplesData="orderSamplesValue") ## ----session------------------------------------------------------------- sessionInfo()