R语言中使用prcomp函数对单细胞数据进行PCA分析

前期参考：https://www.jianshu.com/p/4f7aeae81ef1

001、

library(dplyr)
library(Seurat)
library(patchwork)
pbmc.data <- Read10X(data.dir = "C:/Users/75377/Desktop/r_test2/hg19")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
pbmc
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2

pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- NormalizeData(pbmc)

pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
top10 <- head(VariableFeatures(pbmc), 10)
top10
plot1 <- VariableFeaturePlot(pbmc)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)

all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)

002、利用R函数prcomp对单细胞数据进行PCA分析

genes = VariableFeatures(object = pbmc)
length(genes)
head(genes)
dat <- pbmc[["RNA"]]@scale.data[genes,]    ## pca分析用到的数据
dat <- t(dat)                           
pca <- prcomp(dat,center = F,scale. = F)   ## pca分析
plot(pca$x[,1], pca$x[,2], pch = 19)       ## 利用前两个主成分绘图