# #——包括= FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - knitr:: opts_chunk设置美元(崩溃= TRUE,评论= " # >”)图书馆(ggplot2) theme_set (theme_classic()) # # - - - - -设置,消息= FALSE, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #库(修)#库(schex) # # - - - - -负载,eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # cbmc。rna < as.sparse(阅读。csv(文件= # " . ./新功能/数据/ GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv。广州”,# 9 = ","头= TRUE, # # cbmc row.names = 1))。rna < - CollapseSpeciesExpressionMatrix (cbmc.rna) # # cbmc。adt < as.sparse(阅读。csv(文件= # " . ./新功能/数据/ GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv。广州”,# 9 = ","头= TRUE, # # cbmc row.names = 1))。adt < - cbmc。adt [setdiff (rownames (x = cbmc.adt), # c (“CCR5”、“其”,“CD10”)),) # #——preprocess-gene, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # cbmc < - CreateSeuratObject(数量= cbmc.rna) # # cbmc < - NormalizeData (cbmc) # cbmc < - FindVariableFeatures (cbmc) # cbmc <——ScaleData (cbmc) # #——cluster-gene eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # cbmc < - RunPCA (cbmc verbose = FALSE) # cbmc <——RunTSNE (cbmc = 1:25, dim方法=“FIt-SNE”) # #——preprocess-protein, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # cbmc [[“adt”]] < - CreateAssayObject(数量= cbmc.adt) # # cbmc < NormalizeData (cbmc化验=“adt”,规范化。方法= CLR) # cbmc < - ScaleData (cbmc化验= ADT) # #——calc-hexbin, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # cbmc <——make_hexbin (cbmc nbins = 25日# dimension_reduction =“tsne use_dims = c (1、2) # #——plot-density fig.height = 7, fig.width = 7, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - # plot_hexbin_density (cbmc) # #——plot-feature fig.height = 7, fig.width = 7, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - # plot_hexbin_feature (cbmc mod =“ADT type = "规模。数据”,功能= " CD14”, # action = "的意思是",xlab = " TSNE1”, ylab = " TSNE2”, #标题= paste0 (“CD14蛋白表达的意思”))# #——plot-interact fig.height = 7, fig.width = 7,消息= FALSE,警告= FALSE, eval = FALSE - - - - - # plot_hexbin_interact (cbmc类型= c(“规模。数据”、“scale.data”), # mod = c (“RNA”、“ADT)特性= c (“CD14”、“CD14”),交互= " corr_spearman”, # ylab = " TSNE2”, xlab = " TSNE1”, # title =“相互作用蛋白质和基因表达CD14”) + # scale_fill_gradient2(中点= 0,低=“蓝色”=“白色”,中期#高=“红色”,空间=“实验室”)# #——protein-pca,消息= FALSE,警告= FALSE, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - # DefaultAssay (cbmc) < -“ADT”# cbmc < RunPCA (cbmc特性= rownames (cbmc) reduction.name =“pca_adt”, #减少。关键= " pca_adt_ " verbose = FALSE) # cbmc <——make_hexbin (cbmc nbins = 25日# dimension_reduction =“pca_adt use_dims = c (1、2) # #——plot-feature-a fig.height = 7, fig.width = 7, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - # plot_hexbin_feature (cbmc mod =“ADT type = "规模。数据”,功能= " CD14”, # action = "的意思是",xlab = " TSNE1”, ylab = " TSNE2”, # title = paste0 (“CD14蛋白表达的意思”))