--- a
+++ b/preprocessing/Preprocessing_scRNA_PB_Citeseq.R
@@ -0,0 +1,83 @@
+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)