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[8e0848]: / preprocessing / Preprocessing_scRNA_PB_Citeseq.R

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library(Matrix)

library(Seurat)

source("/research/users/ppolonen/git_home/common_scripts/scRNA/functions.scRNA.analysis.R")



setwd("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/scRNA")



name="CD8_Citeseq"



options(future.globals.maxSize= 4991289600)



# files=list.files(".", "matrix.mtx", full.names = T, recursive = T)

files=list.files("/research/groups/sysgen/PROJECTS/students/citeseq/", "matrix.mtx", full.names = T, recursive = T)

files=gsub("matrix.mtx.gz", "", files)

ids=c("cd8_donor1", "cd8_donor2", "cd8_donor3", "cd8_donor4")

names(files)=ids



# Each patient:

# for(i in seq(ids)[c(1,2,3,4)]){

#   datas=Read10X(data.dir = files[i])

#   rownames(datas[[2]])=gsub("_TotalSeqC","", rownames(datas[[2]]))

#   names(datas)=c("RNA", "PROT")

#   test3=sc.data.analysis(scmat = datas, name = ids[i], nr.pcs = 50, check.pcs=F, plot.umap = T, nFeature.min = 200, nFeature.max = 4000, percent.mitoDNA = 10, cores = 6)

# }



data=Read10X(data.dir = files[c(1,2,3,4)])

rownames(data[[2]])=gsub("_TotalSeqC","", rownames(data[[2]]))

names(data)=c("RNA", "PROT")



# take data with enough both protein counts:

filt=colSums(data[[2]])>2647&colSums(data[[2]])<6664

filt.genes=rowSums(data[[1]]>0)>20



data[[1]]=data[[1]][filt.genes,filt]



data[[2]]=data[[2]][,filt]



batch=substr(colnames(data[[1]]), 1,10)



test1=sc.data.analysis(scmat = data, regress.cell.label = batch, batch.correction.method = "MNNcorrect", name="CD8_Citeseq", nr.pcs = 50, check.pcs=F, plot.umap = T, nFeature.min = 200, nFeature.max = 4000, percent.mitoDNA = 10, cores = 2)







# sbatch

library(Matrix)

library(Seurat)

source("functions.scRNA.analysis.R")



future::plan("multiprocess", workers = 10)

options(future.globals.maxSize= 8991289600)



name="CD8_Citeseq"



options(future.globals.maxSize= 4991289600)



# files=list.files(".", "matrix.mtx", full.names = T, recursive = T)

files=list.files(".", "matrix.mtx", full.names = T, recursive = T)

files=gsub("matrix.mtx.gz", "", files)

ids=c("cd8_donor1", "cd8_donor2", "cd8_donor3", "cd8_donor4")

names(files)=ids



# Each patient:

# for(i in seq(ids)[c(1,2,3,4)]){

#   datas=Read10X(data.dir = files[i])

#   rownames(datas[[2]])=gsub("_TotalSeqC","", rownames(datas[[2]]))

#   names(datas)=c("RNA", "PROT")

#   test3=sc.data.analysis(scmat = datas, name = ids[i], nr.pcs = 50, check.pcs=F, plot.umap = T, nFeature.min = 200, nFeature.max = 4000, percent.mitoDNA = 10, cores = 6)

# }



data=Read10X(data.dir = files[c(1,2,3,4)])

rownames(data[[2]])=gsub("_TotalSeqC","", rownames(data[[2]]))

names(data)=c("RNA", "PROT")



# take data with enough protein counts:

filt=colSums(data[[2]])>2647&colSums(data[[2]])<6664

filt.genes=rowSums(data[[1]]>0)>20



data[[1]]=data[[1]][filt.genes,filt]



data[[2]]=data[[2]][,filt]



batch=substr(colnames(data[[1]]), 1,10)



test1=sc.data.analysis(scmat = data, regress.cell.label = batch, batch.correction.method = "MNNcorrect", name="CD8_Citeseq", nr.pcs = 50, check.pcs=F, plot.umap = T, nFeature.min = 200, nFeature.max = 4000, percent.mitoDNA = 10, cores = 2)