单细胞学习小组第二天

untar("GSE231920_RAW.tar",exdir = "input")
dir("input")
library(stringr)
nn = str_remove(dir("input/"),"GSM7306054_sample1_")
file.rename(paste0("input/",dir("input/")),
            paste0("input/",nn))
dir("input/")

library(Seurat)
library(patchwork)
library(tidyverse)
ct = Read10X("input/")
dim(ct)
class(ct)
seu.obj <- CreateSeuratObject(counts = ct, 
                           min.cells = 3, 
                           min.features = 200)
dim(seu.obj)

set.seed(1234)
seu.obj = subset(seu.obj,downsample = 3000)

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)
seu.obj = subset(seu.obj,
                nFeature_RNA < 6000 &
                nCount_RNA < 30000 &
                percent.mt < 18)
                
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