首先，

将需要合并的矩阵的文件名称导出到.txt.

image.png

git bash
cd 到文件所在的文件夹

 ls | grep ".txt.gz" > list.txt

image.png

然后R语言读取合并矩阵:

方法一：（别人的代码）

library(data.table)
library(stringr)
library(tidyverse) 
df_empty <- data.frame()
allDat <- lapply(list.files('500more_dge/',pattern='*_dge.txt.gz'),function(f){
  # f="NeonatalPancreas_dge.txt.gz";
  print(f);
  # tmp=fread(file.path('500more_dge/',f))
  tmp=read.table(file.path('500more_dge/',f),sep = ",", header = T)
  df_empty=merge(df_empty,tmp, all.y=TRUE)  
  return(df_empty)
})

方法二：（自己写的）

a=read.table('list.txt',sep = '\t',stringsAsFactors = F)
c <- list(a$V1)
c[[1]][1]
class(a)
df_empty <- read.table(c[[1]][1])
class(df_empty)
for(i in 2:3) {
  tmp=read.table(c[[1]][i])
  df_empty=merge(df_empty,tmp, all.y=TRUE)
  return(df_empty)
}

image.png

这个报错令我倒下了，读不了不早说？？？？
但是这个对于一般的小数据是有用的。

明天试试这个py代码

#!/usr/bin/python
import pandas as pd
import os
dict={}
#import sys
#args=sys.argv

name_list=os.listdir('D:/cell_Complexity/111')
print(type(name_list))
print(name_list)#列出countdir下的文件
data0=pd.read_table('D:/cell_Complexity/111/%s' % name_list[0], names=['gene', name_list[0]])       #pd.merge好像要指定两个参数才可以merge，所以先指定一个data0
print(type(data0))
for i in range(1,len(name_list)):                                                      #因为第一个count已经被读取了，所以从第二个看起建立词典，注意列表和字典的索引方式的不同
    dict[i]=pd.read_table('D:/cell_Complexity/111/%s' % name_list[i], names=['gene', name_list[i]])    #建立了所有count文件的索引，keys为数字
    data0=pd.merge(data0, dict[i], on='gene', how='outer')                               #记得建立交集，
    data0.to_csv('result.csv')

10x合并多个样品

#
#合并多个样品
#
library(Seurat)
a.data <- Read10X(data.dir = "Bladder-10X_P4_3")
a <- CreateSeuratObject(counts = a.data, project = "a")
a
list <-c(list.files('droplet/',pattern='*-10x*'))
list[28]
for (i in 1:10) {
  b.data <- Read10X(data.dir =list[2])
  b <- CreateSeuratObject(counts = b.data, project = "b")
  a <- merge(a, y = b, add.cell.ids = c("Bladder-10X_P4_3", list[i]), project = "list[i]")
}

---

All FACS analysis

---
title: "Tabula Muris: All FACS analysis"
output: html_notebook
---

# Preprocessing

Load the requisite packages and some additional helper functions.

```{r}
library(Seurat)
library(dplyr)
library(Matrix)
library(stringr)
library(readr)
library(here)

FACS_files = list.files(here("00_data_ingest","00_facs_raw_data","FACS"), full.names = TRUE)

raw.data.list = list()
for (file in FACS_files){
  raw.data <- read.csv(file, row.names = 1)
  raw.data <- Matrix(as.matrix(raw.data), sparse = TRUE)
  raw.data.list <- append(raw.data.list, raw.data)
}

raw.data <- do.call(cbind, raw.data.list)

cell_order_FACS <- order(colnames(raw.data))
raw.data = raw.data[,cell_order_FACS]

meta.data <- read.csv(here("00_data_ingest","00_facs_raw_data", "metadata_FACS.csv"))

plates <- str_split(colnames(raw.data),"[.]", simplify = TRUE)[,2]

rownames(meta.data) <- meta.data$plate.barcode
cell.meta.data <- meta.data[plates,]
rownames(cell.meta.data) <- colnames(raw.data)

# 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)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(counts = raw.data)

tiss <- AddMetaData(object = tiss, cell.meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'

ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@assays$RNA@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@assays$RNA@counts[ribo.genes, ])/Matrix::colSums(tiss@assays$RNA@counts)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")

percent.Rn45s <- tiss@assays$RNA@counts['Rn45s', ]/Matrix::colSums(tiss@assays$RNA@counts)
tiss <- AddMetaData(object = tiss, metadata = percent.Rn45s, col.name = "percent.Rn45s")

阶段性保存数据

save(tiss, file = "tiss_FACS.Rdata")
rm(list = ls())
load("tiss_FACS.Rdata")

---

All droplet analysis

---
title: "Tabula Muris: All droplet analysis"
output: html_notebook
---

Load the requisite packages and some additional helper functions.

```{r}
library(Seurat)
library(dplyr)
library(stringr)
library(readr)
library(Matrix)
library(here)

Load the count data for all organ and add it to the Seurat object.

channel_folders = list.dirs(here("00_data_ingest","01_droplet_raw_data","droplet"), recursive = FALSE)

n = length(strsplit(channel_folders[1],"[/]")[[1]])

raw.data.list = list()
channel.list = list()
for (channel_folder in channel_folders){
  raw.data <- Read10X(channel_folder)
  channel = str_split(str_split(channel_folder,"/", simplify = TRUE)[1,n], "-", simplify = TRUE)[1,2]
  colnames(raw.data) <-  lapply(colnames(raw.data), function(x) paste0(channel, '_', x))
  raw.data.list <- append(raw.data.list, raw.data)
  channel.list <- append(channel.list, rep(channel, length(colnames(raw.data))))
}

raw.data <- do.call(cbind, raw.data.list)
cell.channels <- unlist(channel.list)

Order cells lexicographically.

ordered_cell_names = order(colnames(raw.data))
raw.data = raw.data[,ordered_cell_names]

meta.data <- read.csv(here("00_data_ingest","01_droplet_raw_data", "metadata_droplet.csv"))
rownames(meta.data) <- meta.data$channel

channel_regex = "(.*?_.*?_.*?)_"
cell.channels <- str_match(colnames(raw.data), channel_regex)[,2]

cell.meta.data <- meta.data[cell.channels,]
rownames(cell.meta.data) <- colnames(raw.data)

# 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)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data)

tiss <- AddMetaData(object = tiss, cell.meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")

ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")

percent.Rn45s <- tiss@raw.data[c('Rn45s'), ]/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.Rn45s, col.name = "percent.Rn45s")