-
Notifications
You must be signed in to change notification settings - Fork 0
/
Bcell_metabolism.r
172 lines (142 loc) · 7.82 KB
/
Bcell_metabolism.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
#!/usr/bin/Rscript
# 2021-3-25
# metabolism analysis CTN B cell
#=============================================================================================================================
################################### Metabolic#########################
library(Seurat)
library(scater)
library(stringr)
library("Rtsne")
library(pheatmap)
library(RColorBrewer)
library(reshape2)
library(scales)
library(ggplot2)
library(dplyr)
library(ggrepel)
options(stringsAsFactors=FALSE)
library(gtools)
library(scran)
source('/public/workspace/lily/software/SingleCellMetabolic/utils.R')
source('/public/workspace/lily/software/SingleCellMetabolic/runGSEA_preRank.R')
# read data
# all cell is tumor
# 2021-3-2 change some cells
dat <- readRDS("/public/workspace/lily/CTN/version_3_20/data/Bcell.RDS")
# tmp_data=subset(dat,cells=which(dat$refine.group%in%c("CTN.dominant","NC.dominant","SC.dominant")))
# 2021-3-2
tmp_data=dat
all_data=as.matrix(tmp_data[['RNA']]@data) # make sure your data is correct size which can use as.matrix
cell_type=paste0("C",as.vector(tmp_data$seurat_clusters)) # cell type which means cluster or cell type
tumor=unname(tmp_data$orig.ident) # this do not need to change ,this means all cells need to analysis
#=======================================================================================================================
col_data <- data.frame(tumor=tumor,cellType=as.character(cell_type),row.names=colnames(all_data))
pathways <- gmtPathways("/public/workspace/lily/software/SingleCellMetabolic/Data/KEGG_metabolism.gmt")
metabolics <- unique(as.vector(unname(unlist(pathways))))
row_data <- data.frame(metabolic=rep(FALSE,nrow(all_data)),row.names = rownames(all_data))
row_data[rownames(row_data)%in%metabolics,"metabolic"]=TRUE
#set SingleCellExperiment object which include expr matrix ,cluster info ,and gene info
sce <- SingleCellExperiment(
assays = all_data,
colData = col_data,
rowData = row_data
)
selected_tumor_sce <- sce #the example code use "selected_tumor_sce" as the name
selected_tumor_metabolic_sce <- sce[rowData(sce)$metabolic,] # dims :1506 37637; 1506 metabolic genes
###################################### scRNA_pathway_activity ##########################################
pathway_file <- "/public/workspace/lily/software/SingleCellMetabolic/Data/KEGG_metabolism.gmt"
pathways <- gmtPathways(pathway_file)
pathway_names <- names(pathways)
all_cell_types <- as.vector(selected_tumor_metabolic_sce$cellType)
cell_types <- unique(all_cell_types)
gene_pathway_number <- num_of_pathways(pathway_file,rownames(selected_tumor_metabolic_sce)[rowData(selected_tumor_metabolic_sce)$metabolic])
set.seed(123)
normalization_method <- "Deconvolution"
##Calculate the pathway activities
#mean ratio of genes in each pathway for each cell type
mean_expression_shuffle <- matrix(NA,nrow=length(pathway_names),ncol=length(cell_types),dimnames = list(pathway_names,cell_types))
mean_expression_noshuffle <- matrix(NA,nrow=length(pathway_names),ncol=length(cell_types),dimnames = list(pathway_names,cell_types))
###calculate the pvalues using shuffle method
pvalues_mat <- matrix(NA,nrow=length(pathway_names),ncol=length(cell_types),dimnames = (list(pathway_names, cell_types)))
norm_tpm <- all_data
for(p in pathway_names){
genes <- pathways[[p]]
genes_comm <- intersect(genes, rownames(norm_tpm))
if(length(genes_comm) < 5) next
pathway_metabolic_tpm <- norm_tpm[genes_comm, ]
pathway_metabolic_tpm <- pathway_metabolic_tpm[rowSums(pathway_metabolic_tpm)>0,]
mean_exp_eachCellType <- apply(pathway_metabolic_tpm, 1, function(x)by(x, all_cell_types, mean))
#remove genes which are zeros in any celltype to avoid extreme ratio value
keep <- colnames(mean_exp_eachCellType)[colAlls(mean_exp_eachCellType>0.001)]
if(length(keep)<3) next
#using the loweset value to replace zeros for avoiding extreme ratio value
pathway_metabolic_tpm <- pathway_metabolic_tpm[keep,]
pathway_metabolic_tpm <- t( apply(pathway_metabolic_tpm,1,function(x) {x[x<=0] <- min(x[x>0]);x} ))
pathway_number_weight = 1 / gene_pathway_number[keep,]
#
mean_exp_eachCellType <- apply(pathway_metabolic_tpm, 1, function(x)by(x, all_cell_types, mean))
ratio_exp_eachCellType <- t(mean_exp_eachCellType) / colMeans(mean_exp_eachCellType)
#exclude the extreme ratios
col_quantile <- apply(ratio_exp_eachCellType,2,function(x) quantile(x,na.rm=T))
col_q1 <- col_quantile["25%",]
col_q3 <- col_quantile["75%",]
col_upper <- col_q3 * 3
col_lower <- col_q1 / 3
outliers <- apply(ratio_exp_eachCellType,1,function(x) {any( (x>col_upper)|(x<col_lower) )} )
if(sum(!outliers) < 3) next
keep <- names(outliers)[!outliers]
pathway_metabolic_tpm <- pathway_metabolic_tpm[keep,]
pathway_number_weight = 1 / gene_pathway_number[keep,]
mean_exp_eachCellType <- apply(pathway_metabolic_tpm, 1, function(x)by(x, all_cell_types, mean))
ratio_exp_eachCellType <- t(mean_exp_eachCellType) / colMeans(mean_exp_eachCellType)
mean_exp_pathway <- apply(ratio_exp_eachCellType,2, function(x) weighted.mean(x, pathway_number_weight/sum(pathway_number_weight)))
mean_expression_shuffle[p, ] <- mean_exp_pathway[cell_types]
mean_expression_noshuffle[p, ] <- mean_exp_pathway[cell_types]
##shuffle 5000 times:
##define the functions
group_mean <- function(x){
sapply(cell_types,function(y) rowMeans(pathway_metabolic_tpm[,shuffle_cell_types_list[[x]]==y,drop=F]))
}
column_weigth_mean <- function(x){
apply(ratio_exp_eachCellType_list[[x]],2, function(y) weighted.mean(y, weight_values))
}
#####
times <- 1:5000
weight_values <- pathway_number_weight/sum(pathway_number_weight)
shuffle_cell_types_list <- lapply(times,function(x) sample(all_cell_types))
names(shuffle_cell_types_list) <- times
mean_exp_eachCellType_list <- lapply(times,function(x) group_mean(x))
ratio_exp_eachCellType_list <- lapply(times,function(x) mean_exp_eachCellType_list[[x]] / rowMeans(mean_exp_eachCellType_list[[x]]))
mean_exp_pathway_list <- lapply(times,function(x) column_weigth_mean(x))
shuffle_results <- matrix(unlist(mean_exp_pathway_list),ncol=length(cell_types),byrow = T)
rownames(shuffle_results) <- times
colnames(shuffle_results) <- cell_types
for(c in cell_types){
if(is.na(mean_expression_shuffle[p,c])) next
if(mean_expression_shuffle[p,c]>1){
pval <- sum(shuffle_results[,c] > mean_expression_shuffle[p,c]) / 5000
}else if(mean_expression_shuffle[p,c]<1){
pval <- sum(shuffle_results[,c] < mean_expression_shuffle[p,c]) / 5000
}
if(pval>0.01) mean_expression_shuffle[p, c] <- NA ### NA is blank in heatmap
pvalues_mat[p,c] <- pval
}
}
all_NA <- rowAlls(is.na(mean_expression_shuffle))
mean_expression_shuffle <- mean_expression_shuffle[!all_NA,]
#heatmap
dat <- mean_expression_shuffle
sort_row <- c()
sort_column <- c()
for(i in colnames(dat)){
select_row <- which(rowMaxs(dat,na.rm = T) == dat[,i])
tmp <- rownames(dat)[select_row][order(dat[select_row,i],decreasing = T)]
sort_row <- c(sort_row,tmp)
}
sort_column <- apply(dat[sort_row,],2,function(x) order(x)[nrow(dat)])
sort_column <- names(sort_column)
dat[is.na(dat)] <- 1
#================================================================================
write.table(mean_expression_noshuffle,file="/public/workspace/lily/CTN/version_3_20/metabolism/Bcell/KEGGpathway_activity_noshuffle_OV.txt",row.names=T,col.names=T,quote=F,sep="\t")
write.table(mean_expression_shuffle,file="/public/workspace/lily/CTN/version_3_20/metabolism/Bcell/KEGGpathway_activity_shuffle_OV.txt",row.names=T,col.names=T,quote=F,sep="\t")
write.table(pvalues_mat,file="/public/workspace/lily/CTN/version_3_20/metabolism/Bcell/KEGGpathway_activity_shuffle_pvalue_OV.txt",row.names=T,col.names=T,quote=F,sep="\t")