forked from zzwang1030/scRNA_Lep
-
Notifications
You must be signed in to change notification settings - Fork 0
/
s3_diff.R
296 lines (251 loc) · 18.2 KB
/
s3_diff.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
library(getopt)
command= matrix(c(
'help' ,"h", 0, "logical","No parameters: help document",
'count' ,"c", 2, "character","Optional parameter: gene expression matrix, row are genes, column are cells",
'project_name' ,"a", 2, "character","Optional parameter: Seurat object name, if this parameter is defined, all operations are for this object; default: project",
'tax' ,"t", 2, "character","Optional parameter: species information; default: human; human, mouse and rat can directly calculate the proportion of mitochondria, ribosomes, and red blood cells of each cell by specifying tax and filter them",
'nFeature_lowlimit' ,"n", 2, "integer","Optional parameter: one of the cell filtration criteria: the lower limit of the number of genes expressed in each cell; the default is 200",
'nFeature_highlimit' ,"N", 2, "integer","Optional parameter: one of the cell filtration criteria: the upper limit of the number of genes expressed in each cell: default 10000",
'mito_cutoff' ,"m", 2, "double","Optional parameter: one of the cell filtration criteria: the threshold of mitochondrial gene expression content of each cell: default 10",
'redcell_cutoff' ,"e", 2, "double","Optional parameter: one of the cell filtration criteria: the upper limit of the expression content of red blood cell marker genes in each cell: default 10",
'double_file' ,"d", 2,"character","Optional parameter: cell doublets prediction results",
'sample_list' ,"l", 1,"character","Required parameter: order of samples; input format A, B, C",
'group_list' ,"2", 2,"character","Optional parameter: sample grouping; input format case, control, case",
'step' ,"s", 2,"character","Optional parameter: operation steps; default 1, 2, 3, 4 (1,filter cells; 2,Seurat integration data, PCA dimensionality reduction; 3,Seurat clustering identification of marker genes; 4,cell cycle evaluation)",
'outdir' ,"o", 2, "character","Optional parameter: output directory: default current directory",
'batch' ,"b", 2, "logical","Optional parameter: whether to re-calibrate the batch: Default: TRUE",
'project.rdata' ,"R", 2, "character","Optional parameter: Seurat output result .rdata file",
'pcs_number' ,"M", 2, "integer","Optional parameter: PCA dimensionality reduction: default 30",
'resolution' ,"r", 2, "character","Optional parameter: input format: 0.2, 0.4, 0.6 default 0.9",
'top_num' ,"i", 2, "integer","Optional parameter: draw heat map, scatter map and violin map. Number of genes: Default: 10",
'cluster_number' ,"I", 2, "character","Optional parameter: cluster number for re-clustering: input format: 1, 2, 3",
'barcode_file' ,"F", 2, "character","Optional parameter: barcode ID for re-clustering: input file: barcode ID number in the first column",
'onlyplot' ,"p", 2, "logical","Optional parameter: only draw gene scatter plots, violin plots and bubble plots provided by --markers_file, default: FALSE",
'onlypos' ,"y", 2, "logical","Optional parameter: only detect positively expressed marker genes, default: FALSE",
'compare_list' ,"C", 2, "character","Optional parameter: Differential expression analysis group list: compare each cluster's internal case&control comparison, input format: control_vs_case,A_vs_B",
'compare_list2' ,"x", 2, "character","Optional parameter: Differential expression analysis grouping list: case and control overall comparison, input format: control_vs_case,A_vs_B",
'FindAllMarkers' ,"f", 2, "logical","Optional parameter: whether to perform marker gene detection, default: TRUE",
'print' ,"P", 2, "character","Optional parameter: whether to output the gene cell expression matrix: counts, data, or scale.data; not output by default",
'markers_file' ,"g", 2,"character","Commonly used marker genes: input file: tabel segmentation, two columns: the first column is the cell type; the second column is the gene name"
), byrow=TRUE, ncol=5)
args=getopt(command)
if(!is.null(args$help) | (is.null(args$project.rdata) & is.null(args$count))) {
cat(paste(getopt(command, usage = T), "\n"))
q()
}
initial.options <- commandArgs(trailingOnly = FALSE)
file.arg.name <- "--file="
script.name <- sub(file.arg.name, "", initial.options[grep(file.arg.name, initial.options)])
yy<-unlist(strsplit(script.name,split="/"))
Bin=paste(yy[1:(length(yy)-1)],collapse="/")
source(paste(Bin,"/function.R",sep=""))
library(Seurat)
library(data.table)
library(RColorBrewer)
library(dplyr)
library(cowplot)
library(reticulate)
use_python("/public/analysis/hefei/xten/software/Anaconda3/envs/my5/bin/python")
py_module_available(module = 'umap')
if ( is.null(args$tax) ) {args$tax="human"}
if ( is.null(args$outdir) ) {args$outdir=getwd()}
if ( is.null(args$nFeature_lowlimit) ) {args$nFeature_lowlimit=200}
if ( is.null(args$nFeature_highlimit) ) {args$nFeature_highlimit=10000}
if ( is.null(args$mito_cutoff) ) {args$mito_cutoff=10}
if ( is.null(args$redcell_cutoff) ) {args$redcell_cutoff=10}
if ( is.null(args$step) ) {args$step="1,2,3,4"}
if ( is.null(args$pcs_number) ) {args$pcs_number=30}
if ( is.null(args$resolution) ) {args$resolution="0.9"}
if ( is.null(args$batch) ) {args$batch="TRUE"}
if ( is.null(args$top_num) ) {args$top_num=10}
if ( is.null(args$onlyplot) ) {args$onlyplot=FALSE}
if ( is.null(args$onlypos) ) {args$onlypos=FALSE}
if ( is.null(args$FindAllMarkers) ) {args$FindAllMarkers="TRUE"}
if ( is.null(args$print) ) {args$print="FALSE"}
if ( args$onlyplot){args$step="NA"}
if(!is.null(args$group_list)){groups <- as.character(unlist(strsplit(args$group_list,split=",")))}
if(!is.null(args$compare_list)){compares <- as.character(unlist(strsplit(args$compare_list,split=",")))}
if(!is.null(args$compare_list2)){compares2 <- as.character(unlist(strsplit(args$compare_list2,split=",")))}
#cat(args$onlyplot)
if(args$onlyplot){
if(is.null(args$project.rdata) | is.null(args$markers_file)){
cat("if only plots,please provide --project.rdata and --markers_file\n")
q()
}
}
mito_cutoff <- args$mito_cutoff
redcell_cutoff <- args$redcell_cutoff
nFeature_lowlimit <- args$nFeature_lowlimit
nFeature_highlimit <- args$nFeature_highlimit
steps <- as.integer(unlist(strsplit(args$step,split=",")))
samples <- as.character(unlist(strsplit(args$sample_list,split=",")))
outdir <- args$outdir
resolutions <- unlist(strsplit(args$resolution,split=","))
resolutions <- as.numeric(resolutions)
pcs_number <- args$pcs_number
scfvalue <- 10000
if (file.exists(outdir)){
setwd(outdir)
} else {
dir.create(outdir)
setwd(outdir)
}
##################
star<-paste(paste(rep("*",100),collapse=""),paste(rep("*",100),collapse=""),paste(rep("*",100),collapse=""),sep="\n")
write.table(paste(star,"Seurat's operating environment variables",sep="\n"),file="Seurat.log",sep="\t",quote=FALSE,row.names=FALSE,col.names=FALSE,append=FALSE)
begins<-paste("the Seurat step begins at the time of ",date(),sep=" ");
write.table(begins,file="Seurat.log",sep="\t",quote=FALSE,row.names=FALSE,col.names=FALSE,append=TRUE)
packages.list<-sessionInfo()
R.version=packages.list$R.version$version.string
packages=NULL
for(i in 1:length(packages.list$otherPkgs)){
package_name=packages.list$otherPkgs[[i]]$Package
package_version=packages.list$otherPkgs[[i]]$Version
packages<-c(packages,paste(package_name,package_version,sep=" "))
}
packages_list<-paste(packages,collapse=",")
packages_list2<-paste("R packages: ",packages_list,sep="")
write.table(R.version,file="Seurat.log",sep="\t",quote=FALSE,row.names=FALSE,col.names=FALSE,append=TRUE)
write.table(packages_list2,file="Seurat.log",sep="\t",quote=FALSE,row.names=FALSE,col.names=FALSE,append=TRUE)
parmaters <- NULL
if(!is.null(args$sample_list)){parmaters <-c(parmaters,paste("--sample_list ",args$sample_list,sep=""))}
if(!is.null(args$project_name)){parmaters <-c(parmaters,paste("--project_name ",args$project_name,sep=""))}
if(!is.null(args$cluster_file)){parmaters <-c(parmaters,paste("--cluster_file ",args$cluster_file,sep=""))}
if(!is.null(args$cluster_number)){parmaters <-c(parmaters,paste("--cluster_number ",args$cluster_number,sep=""))}
if(!is.null(args$barcode_file)){parmaters <-c(parmaters,paste("--barcode_file ",args$barcode_file,sep=""))}
if(!is.null(args$gene_file)){parmaters <-c(parmaters,paste("--gene_file ",args$gene_file,sep=""))}
if(!is.null(args$group_list)){parmaters <-c(parmaters,paste("--group_list ",args$group_list,sep=""))}
if(!is.null(args$compare_list)){parmaters <-c(parmaters,paste("--compare_list ",args$compare_list,sep=""))}
if(!is.null(args$compare_list2)){parmaters <-c(parmaters,paste("--compare_list2 ",args$compare_list,sep=""))}
parmaters <-c(parmaters,paste("--mito_cutoff ",mito_cutoff,sep=""),paste("--redcell_cutoff ",redcell_cutoff,sep=""),paste("--nFeature_lowlimit ",nFeature_lowlimit,sep=""),paste("--nFeature_highlimit ",nFeature_highlimit,sep=""),paste("--resolution ",args$resolution,sep=""),paste("--pcs_number ",args$pcs_number,sep=""),paste("--tax ",args$tax,sep=""),paste("--batch ",args$batch,sep=""),"--normalization_method LogNormalize")
parmaters_list<-paste(parmaters,collapse=",")
parmaters_list2<-paste("Seurat parmaters: ",parmaters_list,sep="")
write.table(parmaters_list2,file="Seurat.log",sep="\t",quote=FALSE,row.names=FALSE,col.names=FALSE,append=TRUE)
#####################################################################
extDiffGene <- function (project=NULL,diffs=NULL){
lists<-sort(unique(as.numeric(as.vector([email protected]))))
data_mat_frame <- project@assays$RNA@data
data_mat_frame <- expm1(data_mat_frame)
allcluster_mean <- NULL
groupname <- NULL
for(i in 1:length(lists)){
subdiff <- diffs[diffs$cluster == lists[i],]
subdata <- data_mat_frame[subdiff$gene,]
cluster1_mean <- Matrix::rowMeans(subdata[,[email protected] == lists[i]])
cluster1_other_mean <- Matrix::rowMeans(subdata[,[email protected] != lists[i]])
cluster_mean <- data.frame(Symbol=names(cluster1_mean),Cluster_1_Mean_UMI_Counts=cluster1_mean,Cluster_1_Other_Mean_UMI_Counts=cluster1_other_mean,Cluster_1_Log2_Fold_Change=log2(cluster1_mean+1)-log2(cluster1_other_mean+1))
subdiff_mean <- merge(subdiff, cluster_mean, by.x = "gene", by.y = "Symbol", all.x = TRUE,sort=FALSE)
colnames(subdiff_mean)[2] <- "Cluster_1_Pvalue"
colnames(subdiff_mean)[6] <- "Cluster_1_AdjustPvalue"
subdiff_mean <- subdiff_mean[,c("gene","pct.1","pct.2","Cluster_1_Mean_UMI_Counts","Cluster_1_Other_Mean_UMI_Counts","Cluster_1_Log2_Fold_Change","Cluster_1_Pvalue","Cluster_1_AdjustPvalue")]
colnames(subdiff_mean)[1] <- "Symbol"
colnames(subdiff_mean) <- c("Symbol","pct.1","pct.2",paste("Cluster_",lists[i],"_Mean_UMI_Counts",sep=""),paste("Cluster_",lists[i],"_Other_Mean_UMI_Counts",sep=""),paste("Cluster_",lists[i],"_Log2_Fold_Change",sep=""),paste("Cluster_",lists[i],"_Pvalue",sep=""),paste("Cluster_",lists[i],"_AdjustPvalue",sep=""))
otfile <- paste("Cluster",lists[i],"_diff.xls", sep="")
write.table(subdiff_mean, sep="\t", file=otfile, row.names=FALSE, quote=FALSE)
n <- dim(subdiff_mean)[2]
otfile <- paste("Cluster",lists[i], "_diff_significant.xls", sep="")
write.table(subdiff_mean[subdiff_mean[,(n-1)] <= 0.05 & abs(subdiff_mean[,(n-2)]) >= 0.585,], sep="\t", file=otfile, row.names=FALSE, quote=FALSE)
tmpname <- paste("Cluster", lists[i] , sep="")
cluster_mean <- Matrix::rowMeans(data_mat_frame[,[email protected] == lists[i]])
allcluster_mean <- cbind(allcluster_mean, cluster_mean)
groupname <- c(groupname, tmpname)
}
colnames(allcluster_mean) <- groupname
write.table(data.frame(Symbol=rownames(allcluster_mean),allcluster_mean), file="Cluster_level_mean.xls", sep="\t", row.names=FALSE, quote=FALSE)
return(allcluster_mean)
}
Compare_Of_Clusters <- function(compares,project){
data_mat_frame <- project@assays$RNA@data
data_mat_frame <- expm1(data_mat_frame)
clusters<-sort(unique([email protected]))
for(m in 1:length(compares)){
pairs <- as.character(unlist(strsplit(compares[m],split="_vs_")))
number<-NULL
for(i in 1:length(clusters)){
length1<-length(which([email protected] == clusters[i] & project$Group %in% pairs[1]))
length2<-length(which([email protected] == clusters[i] & project$Group %in% pairs[2]))
if(length1<3 | length2<3){number<-c(number,0);next}
subdiff <- FindMarkers(project,subset.ident =clusters[i],ident.1=pairs[1],ident.2=pairs[2],min.pct = 0.01,logfc.threshold = 0.1,group.by="Group",
test.use = "bimod",assay = "RNA",min.cells.group =1)
subdata <- data_mat_frame[rownames(subdiff),]
group1_mean <- Matrix::rowMeans(subdata[,[email protected] == clusters[i] & project$Group %in% pairs[1]])
group2_mean <- Matrix::rowMeans(subdata[,[email protected] == clusters[i] & project$Group %in% pairs[2]])
name1 <- paste(pairs[1],"_Mean_UMI_Counts",sep="")
name2 <- paste(pairs[2],"_Mean_UMI_Counts",sep="")
cluster_mean <- data.frame(Symbol=names(group1_mean), name1=group1_mean,name2=group2_mean,Log2_Fold_Change=log2(group1_mean+1)-log2(group2_mean+1))
colnames(cluster_mean)<-c("Symbol",name1,name2,"Log2_Fold_Change")
subdiff_mean <- merge(data.frame(Symbol=rownames(subdiff),subdiff), cluster_mean, by.x = "Symbol", by.y = "Symbol", all.x = TRUE,sort=FALSE)
colnames(subdiff_mean)[2] <- "Pvalue"
colnames(subdiff_mean)[6] <- "AdjustPvalue"
subdiff_mean2 <- subdiff_mean[,c("Symbol","pct.1","pct.2",name1,name2,"Log2_Fold_Change","Pvalue","AdjustPvalue")]
otfile <- paste("Cluster",clusters[i],"_diff_","between_",pairs[1],"_",pairs[2],".xls", sep="")
write.table(subdiff_mean2, sep="\t", file=otfile, row.names=FALSE, quote=FALSE)
n <- dim(subdiff_mean2)[2]
otfile <- paste("Cluster",clusters[i],"_diff_significant_","between_",pairs[1],"_",pairs[2],".xls", sep="")
write.table(subdiff_mean2[subdiff_mean2[,(n-1)] <= 0.05 & abs(subdiff_mean2[,(n-2)]) >= 0.585,], sep="\t", file=otfile, row.names=FALSE, quote=FALSE)
number<-c(number,dim(subdiff_mean2[subdiff_mean2[,n] <= 0.05 & abs(subdiff_mean2[,(n-2)]) > 0.5,])[1])
}
}
}
if(3 %in% steps){
cat("Step3:FindClusters and FindAllMarkers\n")
if(!is.null(args$project.rdata) & !(1 %in% steps) & !(2 %in% steps)){
load(args$project.rdata)
if(!is.null(args$project_name)){project<-get(args$project_name)}
}
if(args$batch){
project <- ScaleData(object = project, verbose = TRUE,features=rownames(project@assays$RNA@data),assay="RNA")
}
for(j in 1:length(resolutions)){
output<-paste("clusters_resolution",resolutions[j],sep="")
dir.create(output)
setwd(output)
if(args$FindAllMarkers){
if(args$batch){
DefaultAssay(object = project) <- "integrated"
project <- FindClusters(object = project, resolution = resolutions[j])
}else{
if("integrated" %in% names(project@assays)){
DefaultAssay(object = project) <- "integrated"
}else{
DefaultAssay(object = project) <- "RNA"
}
project <- FindClusters(object = project, resolution = resolutions[j])
}
DefaultAssay(object = project) <- "RNA"
idents<-as.factor(as.numeric(as.vector([email protected]))+1)
names(idents) <- rownames([email protected])
[email protected] <- idents
printClusterFiles(project=project,sample_list=args$sample_list)
diffs <- tryCatch({
diffs <- FindAllMarkers(object = project, logfc.threshold = 0.1, only.pos = args$onlypos, test.use="bimod", min.pct = 0.01,assay="RNA")
}, error = function(e) {
set.seed(1)
random<-sample(1:dim(project)[2],100000)
project2<-subset(project,cells=colnames(project[,random]))
diffs <- FindAllMarkers(object = project2, logfc.threshold = 0.1, only.pos = args$onlypos, test.use="bimod", min.pct = 0.01,assay="RNA")
return(diffs)
})
#diffs <- FindAllMarkers(object = project, logfc.threshold = 0.1, only.pos = args$onlypos, test.use="bimod", min.pct = 0.01,assay="RNA")
write.table(diffs, sep="\t", file="project.FindAllMarkers.xls",quote=FALSE, row.names=FALSE)
project[['Cluster']] <- [email protected]
save(list=c("project","diffs"),file='project.rdata')
index<-grep("^RP[L|S]",diffs$gene, ignore.case = FALSE)
if(length(index)>1){
diffs2<-diffs[-index,]
}else{
diffs2<-diffs
}
allcluster_mean <- extDiffGene(project=project,diffs=diffs)
}
cat("perform diff gene analysis\n")
if(is.null(args$group_list)){setwd(outdir);next}
project[["Group"]]<-rep(0,dim(project)[2])
for(i in 1:length(groups)){
yy<-paste("-",i,"$",sep="")
project[["Group"]][grep(yy,colnames(project)),]<-groups[i]
}
if(!is.null(args$compare_list)){Compare_Of_Clusters(compares,project)}
setwd(outdir)
}
}