day5单细胞单样本处理（from 花花）

一、数据下载

示例数据是GSE231920的第2个样本

找到数据集，点击custom

勾选样本后点击下方的download 

下载后好后是一个tar的压缩包，需要手动或者用代码解压后使用。

1.解压缩 

 在工作目录下解压GSE231920_RAW.tar

untar("GSE231920_RAW.tar",exdir = "input")

#修改文件名去掉GSM7306055_sample2_前缀
library(stringr)
nn = str_remove(dir("input/"),"GSM7306055_sample2_")
nn

#file.rename来给文件改名
file.rename(paste0("input/",dir("input/")),
            paste0("input/",nn))

#检查是否成功
dir("input/")

2.读取并创建Seurat对象 

创建Seurat对象
rm(list = ls()) #清空右上角的所有变量，方便反复调试代码
library(Seurat)
library(patchwork)
library(tidyverse)
ct = Read10X("input/")
dim(ct)

seu.obj <- CreateSeuratObject(counts = ct, 
                              min.cells = 3, #一个基因至少要在3个细胞里有表达才被保留
                              min.features = 200) #一个细胞里至少要200个基因有表达才被保留
dim(seu.obj)

#设计随机种子（节省资源，跑需要的数据不用设置这个）
set.seed(1234)
seu.obj = subset(seu.obj,downsample = 3000)

3.质控 

#质控开始
seu.obj[["percent.mt"]] <- PercentageFeatureSet(seu.obj, pattern = "^MT-")
head(seu.obj@meta.data)

VlnPlot(seu.obj, 
        features = c("nFeature_RNA",
                     "nCount_RNA", 
                     "percent.mt"), 
        ncol = 3,pt.size = 0.5)
#数据过滤
seu.obj = subset(seu.obj,
                 nFeature_RNA < 8500 &
                   nCount_RNA < 50000 &
                   percent.mt < 40)
dir()

4.降维聚类分析

#开始降维聚类分群
f = "obj.Rdata"
if(!file.exists(f)){
  seu.obj = seu.obj %>% 
    NormalizeData() %>%  
    FindVariableFeatures() %>%  
    ScaleData(features = rownames(.)) %>%  
    RunPCA(features = VariableFeatures(.))  %>%
    FindNeighbors(dims = 1:15) %>% 
    FindClusters(resolution = 0.5) %>% 
    RunUMAP(dims = 1:15) %>% 
    RunTSNE(dims = 1:15)
  save(seu.obj,file = f)
}
load(f)
ElbowPlot(seu.obj)
p1 <- DimPlot(seu.obj, reduction = "umap",label = T)+NoLegend();p1