单细胞专题 | 6.单细胞下游分析——不同类型的数据读入

rm(list=ls())
options(stringsAsFactors = F)
library(Seurat)
# Load data
dir='./'
Sys.time()
raw.data <- read.csv(paste(dir,"Data_input/csv_files/S01_datafinal.csv", sep=""), header=T, row.names = 1)
Sys.time()
dim(raw.data)
raw.data[1:4,1:4]
head(colnames(raw.data))
# Load metadata
metadata <- read.csv(paste(dir,"Data_input/csv_files/S01_metacells.csv", sep=""), row.names=1, header=T)
head(metadata)

# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
fivenum(percent.ercc)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
dim(raw.data)

# Create the Seurat object with all the data (unfiltered)
main_tiss <- CreateSeuratObject(counts = raw.data)
# add rownames to metadta
row.names(metadata) <- metadata$cell_id
# add metadata to Seurat object
main_tiss <- AddMetaData(object = main_tiss, metadata = metadata)
main_tiss <- AddMetaData(object = main_tiss, percent.ercc, col.name = "percent.ercc")
# Head to check
head(main_tiss@meta.data)

# Calculate percent ribosomal genes and add to metadata
ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]", x = rownames(x = main_tiss@assays$RNA@data), value = TRUE)
percent.ribo <- Matrix::colSums(main_tiss@assays$RNA@counts[ribo.genes, ])/Matrix::colSums(main_tiss@assays$RNA@data)
fivenum(percent.ribo)
main_tiss <- AddMetaData(object = main_tiss, metadata = percent.ribo, col.name = "percent.ribo")
main_tiss

# Filter cells so that remaining cells have nGenes >= 500 and nReads >= 50000
main_tiss_filtered <- subset(x=main_tiss, subset = nCount_RNA > 50000 & nFeature_RNA > 500)
main_tiss_filtered

############### 10X标准输出的3个文件 ##############
# 列出当前目录下所有开头是GSM的文件
fs <- list.files('./data/GSE106273_RAW/','^GSM')

# 然后获取四个样本信息
library(stringr)
samples <- str_split(fs,'_', simplify = T)[,1]

# 设置一个循环，对每个样本信息做同样的事：
#（1）找到包含这个样本的文件(用grepl)
# (2)设置对应的目录名（str_split+paste）然后创建目录（用dir.create）
# (3)将文件放到对应目录(采用的是file.rename)并重命名文件
setwd("data/GSE106273_RAW/")
library(R.utils)

lapply(unique(samples),function(x){
  y <- fs[grepl(x,fs)]
  folder <- paste(str_split(y[1],'_',simplify = T)[,2:3],
               collapse = '')
  # 创建文件夹
  dir.create(folder, recursive = T)
  # 文件重命名：
  file.rename(y[1],file.path(folder,"barcodes.tsv.gz"))
  file.rename(y[2],file.path(folder,"genes.tsv.gz"))
  file.rename(y[3],file.path(folder,"matrix.mtx.gz"))
  # 解压，其实不解压也行
  sapply(paste0(folder,c("/barcodes.tsv.gz","/genes.tsv.gz","/matrix.mtx.gz")),
         gunzip)
})
# 批量读取成10X对象:
# 因为Read10X函数需要对目录进行操作，所以先把目录名提取出来
folders=list.files('./',pattern='[12]$')
folders
# [1] "G1"  "G2"  "L1"  "L2"  "NP1" "NP2" "PI1" "PI2"

# 然后使用lapply进行循环(之前出过一期apply系列函数教程，可以查阅一下，
# lapply是对列表或向量进行循环，而apply是对数据框或矩阵操作)
library(Seurat)
sceList <- lapply(folders,function(folder){
  CreateSeuratObject(counts = Read10X(folder),
                     project = folder )
})

# 此时的sceList是一个包含8个10X对象的集合，下一步需要将其合并
sceList
# 合并：
sce_big <- merge(sceList[[1]],
                 y = c(sceList[[2]],sceList[[3]],sceList[[4]],
                       sceList[[5]],sceList[[6]],
                       sceList[[7]],sceList[[8]]),
                 add.cell.ids = folders,
                 project = "mouse8")

table(sce_big$orig.ident)
# G_1  G_2  L_1  L_2 NP_1 NP_2 PI_1 PI_2
# 2915 3106 5906 3697 2249 2127 1500 4306

# 保存：
save(sce_big,file = 'sce_big.Rdata') # 保存的数据

###### step1:导入数据 ######
rm(list=ls())
options(stringsAsFactors = F)
library(Seurat)
library(ggplot2)
library(clustree)
library(cowplot)
library(dplyr)

library(data.table)
samples=list.files('GSE129139_RAW//')
samples
sceList = lapply(samples,function(pro){
  #pro=samples[1]
  folder=file.path('GSE129139_RAW',pro )
  print(pro)
  print(folder)
  print(list.files(folder))
  print( Sys.time() )
  sce=CreateSeuratObject(counts = Read10X(folder),
                         project = pro )
  print( Sys.time() )
  return(sce)
})
sceList
samples
sce.all = merge(sceList[[1]], y = sceList[-1],
                add.cell.ids = samples,
                merge.data = TRUE)
as.data.frame(sce.all@assays$RNA@counts[1:10, 1:2])
head(sce.all@meta.data, 10)
table(sce.all@meta.data$orig.ident)
library(stringr)
table(sce.all$orig.ident)

############### h5格式文件 ##############
# 读取单个
sce <- Read10X_h5(filename = "GSM4107899_LH16.3814_raw_gene_bc_matrices_h5.h5")
sce <- CreateSeuratObject(counts = sce)

# 批量读取并合并：
library(hdf5r)
setwd("../GSE138433_RAW/")
files <- list.files('./')
files
names <- str_split(files,'_', simplify = T)[,2]

# 批量读取：
sceList <- lapply(files, function(files){
  CreateSeuratObject(counts = Read10X_h5(files),
                     project = names)
})

# 合并：
sce_big <- merge(sceList[[1]],
                 y = c(sceList[[2]],sceList[[3]],sceList[[4]],
                       sceList[[5]],sceList[[6]]),
                 add.cell.ids = names)

table(sce_big$orig.ident)
# LH16.3814 LH17.3222 LH17.3554  LH17.364  LH17.530  LH18.277
# 737280    737280    737280    737280    737280    737280

# 保存：
save(sce_big,file = 'sce_big.Rdata') # 保存的数据

############### h5ad格式文件 ##############
setwd("../GSE153643_RAW/")
# remotes::install_github("mojaveazure/seurat-disk")
library(SeuratDisk)

# 先将后`h5ad`格式转换为`h5seurat`格式:
Convert("GSM4648565_liver_raw_counts.h5ad", "h5seurat",
        overwrite = TRUE, assay = "RNA")

# 再使用`LoadH5Seurat()`函数读取Seurat对象。
sce <- LoadH5Seurat("GSM4648565_liver_raw_counts.h5seurat")