Seurat单细胞处理流程之二：数据质控

1.简介

单细胞RNA测序（scRNA-seq）数据质量控制（Quality Control, QC）是分析流程中的关键步骤。由于单细胞测序技术存在一定的噪音和技术误差，质控的目的是去除低质量细胞和异常数据，提高后续分析的可靠性和生物学意义。

2.数据读入

以pbmc示例数据为例：

rm(list = ls())
setwd("/mnt/DEV_8T/zhaozm/seurat全流程/数据质控")

##禁止转化为因子
options(stringsAsFactors = FALSE)

library(Seurat)
library(dplyr)
library(readr)
library(Matrix)
library(ggplot2)
library(patchwork)
library(ggplot2)

载入需要的程序包：SeuratObject

载入需要的程序包：sp


载入程序包：‘SeuratObject’


The following objects are masked from ‘package:base’:

    intersect, t



载入程序包：‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

#读入数据
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")

## 创建seurat对象
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)

Warning message:
“Feature names cannot have underscores ('_'), replacing with dashes ('-')”

#初步查看Seurat对象
pbmc

An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
 1 layer present: counts

3.质控数据及可视化

## 计算线粒体比例
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-") 
#人源的数据为MT，鼠源的需要换成mt
head(pbmc@meta.data,5)

A data.frame: 5 × 4

	orig.ident	nCount_RNA	nFeature_RNA	percent.mt
	<fct>	<dbl>	<int>	<dbl>
AAACATACAACCAC-1	pbmc3k	2419	779	3.0177759
AAACATTGAGCTAC-1	pbmc3k	4903	1352	3.7935958
AAACATTGATCAGC-1	pbmc3k	3147	1129	0.8897363
AAACCGTGCTTCCG-1	pbmc3k	2639	960	1.7430845
AAACCGTGTATGCG-1	pbmc3k	980	521	1.2244898

VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) 

Warning message:
“Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.”

plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
if(!require(patchwork))install.packages("patchwork")
CombinePlots(plots = list(plot1, plot2)) 

Warning message in CombinePlots(plots = list(plot1, plot2)):
“CombinePlots is being deprecated. Plots should now be combined using the patchwork system.”

## 过滤
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)   
ncol(as.data.frame(GetAssayData(pbmc, slot = "counts")))
## [1] 2638

Warning message:
“The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
ℹ Please use the `layer` argument instead.”

2638

## 过滤之后的图片
plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
if(!require(patchwork))install.packages("patchwork")
#CombinePlots这步需要你的绘图窗口足够大
CombinePlots(plots = list(plot1, plot2)) 

Warning message in CombinePlots(plots = list(plot1, plot2)):
“CombinePlots is being deprecated. Plots should now be combined using the patchwork system.”

VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) 

Warning message:
“Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.”

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