## ----包括= false ---------------------------------------------------------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(ggplot2) theme_set(theme_classic()) ## ----设置，消息= false，eval = false -------------------------------------------------------------------------------------------------------------------------＃library（seurat）＃library（schex）## ----加载，eval = false ----------------------------------------------------------------------------------------------------------------------------------------------------＃cbmc.rna < - as.sparse（read.csv（file =＃” ../ new函数/data/gse100866_cbmc_8k_13ab_10x-rna_umi.csv.gz“，＃sep =”，“，header = true，row.names = 1））＃＃cbmc.rna <-colapsespeceSpeceSpeceSpeceSpeceSpeceSpeceSpeceSpeceSpeceSpeceSpeceSpeceSpecepressexpressionmatrix（cbmc.rna）＃cbmc.rna）- as.sparse（read.csv（file =＃“ ../new functions/data/gse100866_cbmc_8k_8k_13ab_13ab_10x-adt_umi.csv.gz”，＃sep =“，”，“，”，header = true，row.names = 1））cbmc.adt <-cbmc.adt [setDiff（rownames（x = cbmc.adt），＃c（“ ccr5”，“ ccr7”，“ cd10”）），] ## -----------------------------------------------------------------------FALSE---------------------------------------------- # cbmc <-CreateSeUratObject（counts = cbmc.rna）＃＃＃cbmc <-SuromentizedAta（CBMC）＃CBMc < - findVariableFeatures（CBMC）＃cbmc < - scaledata（cbmc）## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------＃cbmc <-runpca（cbmc，verbose = false）＃cbmc <-runtsne（cbmc，dims = 1：25，method =“ fit-sne”）## ---- - - - - - - - - ----------------------------------------------------------------------------------------------------------＃cbmc [[“ ADT”]] <-screateAssayObject（counts = cbmc.adt）＃＃＃cbmc <-normolyizedata（cbmc，assay，assay =“ adt”，normolization.method =“ clr”）＃cbmc <-Scaledata（cbmc，assay =“ adt”）## ---- calc-hexbin，eval = false----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------＃cbmc <-sake_hexbin（cbmc，nbins = 25，＃dimension_reduction=“ tsne”，use_dims = c（1,2））## ----情节密度，图。Height= 7，图Width = 7，eart = false = false -------------------------------------------------------------------------------------＃plot_hexbin_dense（cbmc）## ----绘图 - 特征，图。height= 7，图。------------- # plot_hexbin_feature(cbmc, mod="ADT", type="scale.data", feature="CD14", # action="mean", xlab="TSNE1", ylab="TSNE2", # title=paste0("Mean of protein expression of CD14")) ## ----plot-interact, fig.height=7, fig.width=7, message=FALSE, warning=FALSE, eval=FALSE---- # plot_hexbin_interact(cbmc, type=c("scale.data", "scale.data"), # mod=c("RNA", "ADT"), feature=c("CD14", "CD14"), interact="corr_spearman", # ylab="TSNE2", xlab="TSNE1", # title="Interaction protein and gene expression CD14") + # scale_fill_gradient2(midpoint=0, low="blue", mid="white", # high="red", space ="Lab") ## ----protein-pca, message=FALSE, warning=FALSE, eval=FALSE-------------------- # DefaultAssay(cbmc) <- "ADT" # cbmc <- RunPCA(cbmc, features = rownames(cbmc), reduction.name = "pca_adt", # reduction.key = "pca_adt_", verbose = FALSE) # cbmc <- make_hexbin(cbmc, nbins = 25, # 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="scale.data", feature="CD14", # action="mean", xlab="TSNE1", ylab="TSNE2", # title=paste0("Mean of protein expression of CD14"))