单细胞数据分析中实现 ElbowPlot 函数

 

前期分析参考：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[["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)
plot1
plot1

all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
print(pbmc[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(pbmc, dims = 1:2, reduction = "pca")
DimPlot(pbmc, reduction = "pca")

DimHeatmap(pbmc, dims = 1, cells = 500, balanced = TRUE)
DimHeatmap(pbmc, dims = 1:15, cells = 500, balanced = TRUE)

pbmc <- JackStraw(pbmc, num.replicate = 100)
pbmc <- ScoreJackStraw(pbmc, dims = 1:20)

 

002、ElbowPlot 函数的实现

00a、使用plot函数

dat <- pbmc[["pca"]]@stdev[1:20]            ## 绘图数据
dat
dat <- data.frame(a = 1:20, b = dat)
plot(dat$a, dat$b)                          ## 绘图

 

 

00b、使用ggplot2

dat <- pbmc[["pca"]]@stdev[1:20]
dat
dat <- data.frame(a = 1:20, b = dat)                           ## 绘图数据
library(ggplot2)
library(cowplot) 
ggplot(data = dat) +                                           ## 绘图
  geom_point(mapping = aes_string(x = 'a', y = 'b')) +
  labs(x = "PC", y = "stdev_top20") + theme_cowplot()