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
297
298
299
300
301
302
303
304
305
306
307
| library(Matrix)
library(Seurat)
library(plyr)
library(dplyr)
library(patchwork)
library(purrr)
library(RColorBrewer)
library(ggplot2)
library(ggrepel)
blank_theme <- theme_minimal()+
theme(
axis.title.x = element_blank(),
axis.text.x=element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
panel.border = element_blank(),
panel.grid=element_blank(),
axis.ticks = element_blank(),
plot.title=element_text(size=14, face="bold",hjust = 0.5)
)
col_Paired <- colorRampPalette(brewer.pal(12, "Paired"))
f_pie <- function(lc_x, lc_main, lc_x_p = 1.3, lc_r = T){
lc_cols <- col_Paired(length(lc_x))
lc_v <- as.vector(100*lc_x)
lc_df <- data.frame(type = names(lc_x), nums = lc_v)
lc_df <- lc_df[order(lc_df$type),]
lc_percent = sprintf('%0.2f%%',lc_df$nums)
if(lc_r){
lc_df$pos <- with(lc_df, 100-cumsum(nums)+nums/2)
}else{
lc_df$pos <- with(lc_df, cumsum(nums)-nums/2)
}
lc_pie <- ggplot(data = lc_df, mapping = aes(x = 1, y = nums, fill = type)) + geom_bar(stat = 'identity')
# print(lc_df)
# print(lc_pie)
lc_pie <- lc_pie + coord_polar("y", start=0, direction = 1) + scale_fill_manual(values=lc_cols) + blank_theme
lc_pie <- lc_pie + geom_text_repel(aes(x = lc_x_p, y=pos),label= lc_percent, force = T,
arrow = arrow(length=unit(0.01, "npc")), segment.color = "#cccccc", segment.size = 0.5)
lc_pie <- lc_pie + labs(title = lc_main)
lc_pie
}
f_pie_metaN <- function(sObject, lc_group.by){
tp_data <- prop.table(table(sObject[[lc_group.by]]))
f_pie(tp_data, sprintf('Proportion of %s', lc_group.by))
}
f_UMAP_more <- function(sObject, lc_group.by, lc_reduction="umap"){
res <- (DimPlot(sObject, reduction = lc_reduction, group.by = lc_group.by[1], label = T, repel = T, label.size = 6) +
labs(title = lc_group.by[1]))
for(lc_i in 2:length(lc_group.by)){
res <- res/
(DimPlot(sObject, reduction = lc_reduction, group.by = lc_group.by[lc_i], label = T, repel = T, label.size = 6) +
labs(title = lc_group.by[lc_i]))
}
res
}
f_br_cluster_f <- function(sObject, lc_groupN){
lc_filter <- unlist(unique(sObject[[lc_groupN]]))
lc_filter <- lc_filter[!is.na(lc_filter)]
lc_filter
}
f_br_cluster <- function(sObject, lc_groupN, lc_labelN, lc_prop = F){
lc_all <- unique(sObject[[lc_labelN]])
rownames(lc_all) <- lc_all[[1]]
colnames(lc_all) <- "CB"
lc_tp <- SplitObject(subset(x = sObject, !!sym(lc_groupN)%in%f_br_cluster_f(sObject, lc_groupN)), split.by = lc_groupN)
for(lc_i in 1:length(lc_tp)){
if(lc_prop){
lc_tp[[lc_i]] <- prop.table(table(lc_tp[[lc_i]][[lc_labelN]]))
}else{
lc_tp[[lc_i]] <- table(lc_tp[[lc_i]][[lc_labelN]])
}
}
for(lc_name in names(lc_tp)){
lc_all[[lc_name]] = 0
lc_all[names(lc_tp[[lc_name]]), lc_name] = lc_tp[[lc_name]]
}
lc_all[,-1]
}
f_q2l <- function(lc_q){
res <- NULL
for(lc_c in colnames(lc_q)){
for(lc_r in rownames(lc_q)){
res <- rbind(res, c(group=lc_c, label=lc_r, value=lc_q[lc_r,lc_c]))
}
}
res <- data.frame(res)
res$value = as.numeric(res$value)
res
}
library(RColorBrewer)
library(ggplot2)
col_Paired <- colorRampPalette(brewer.pal(12, "Paired"))
f_q_frequnency <- function(lc_q){
ggplot(f_q2l(lc_q),mapping = aes(group,value,fill=label))+
geom_bar(stat='identity',position='fill') + scale_fill_manual(values= col_Paired(nrow(lc_q)))+
labs(x = 'group',y = 'frequnency') +
theme(axis.title =element_text(size = 16),axis.text =element_text(size = 14, color = 'black'))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+coord_flip()
}
f_DEG_Volcano <- function(lc_logFC, lc_p, lc_gene, Threshold_logFC = 1, Threshold_p = 0.05, lc_rep=1:10){
col_vector = rep(rgb(108, 200, 228, maxColorValue = 255), length(lc_logFC))
col_vector[lc_p < Threshold_p & lc_logFC > Threshold_logFC] = rgb(226, 61, 75, maxColorValue = 255)
col_vector[lc_p < Threshold_p & lc_logFC < -Threshold_logFC] = rgb(232, 168, 71, maxColorValue = 255)
lc_p[lc_p < 1e-10] = 1e-10
lc_p[lc_p > 1 is.na(lc_p)] = 1
df = data.frame(logFC <- lc_logFC, `-log10(P)` <- -log10(lc_p), col <- col_vector, gene <- lc_gene)
colnames(df) <- c('logFC', '-log10(P)', "col", "gene")
lc_tp_logFC <- df$logFC
lc_tp_logFC[lc_p>=Threshold_p] = 0
lc_idx <- order(lc_tp_logFC)[c(lc_rep, length(lc_gene)+1-lc_rep)]
df$logFC[df$logFC > 10] = 10
df$logFC[df$logFC < -10] = -10
res <- ggplot() + geom_point(aes(logFC, `-log10(P)`, col=I(col)), data = df)
res <- res + theme_bw() + theme(panel.grid=element_line(colour=NA))
res <- res + geom_hline(yintercept=-log10(Threshold_p), linetype="longdash")
res <- res + geom_vline(xintercept=c(Threshold_logFC, -Threshold_logFC), linetype="longdash")
res <- res + geom_text_repel(data=df[lc_idx,],aes(logFC,`-log10(P)`,label=gene), force=T, max.overlaps=Inf)
res
}
f_cluster_averages <- function(lc_scRNA, lc_metaN='ident'){
# 切分出Clusters
lc_clusters <- SplitObject(lc_scRNA, split.by = lc_metaN)
for (lc_i in 1:length(lc_clusters)){
lc_clusters[[lc_i]] <- lc_clusters[[lc_i]][[lc_clusters[[lc_i]]@active.assay]]@scale.data
}
for (lc_i in 1:length(lc_clusters)){
lc_clusters[[lc_i]] <- apply(lc_clusters[[lc_i]],1,mean)
}
lc_clusters <- data.frame(lc_clusters)
scale(lc_clusters)
}
library(clusterProfiler)
library(pheatmap)
library(ggdendro)
f_DEG_hclust <- function(lc_counts){
ggdendrogram(hclust(dist(t(lc_counts))), rotate = T, size = 3)+theme(axis.text = element_text(size=14,face = "bold"))
}
f_DEG_pheatmap_choose_matrix <- function(lc_tp_d, lc_significant_markers, lc_n = 120, Threshold_logFC = 1){
res <- subset(lc_significant_markers, abs(avg_log2FC) > Threshold_logFC)
res <- res[order(abs(res$avg_log2FC), decreasing = T),]
res <- head(unique(res$gene), n = lc_n)
res <- lc_tp_d[res,]
res
}
require(ggplotify)
f_DEG_pheatmap <- function(choose_matrix){
choose_matrix = t(scale(t(choose_matrix)))
as.ggplot(pheatmap(choose_matrix))
}
f_prepare4CSOmap <- function(lc_scRNA, lc_csomap_data_dir, lc_className){
lc_csomap_data_dir <- system(paste("echo", lc_csomap_data_dir), intern = T)
if(!file.exists(lc_csomap_data_dir)){dir.create(lc_csomap_data_dir)}
# 导出label.txt
labels <- lc_scRNA[[lc_className]]
labels$cells <- gsub("-", "." ,rownames(labels)) # TPM的colnames 不知为何导出时被替换了，这里也替换一下
labels$labels <- as.character(labels[[lc_className]])
rownames(labels) <- NULL
labels = labels[,c("cells", "labels")]
write.table(labels, file.path(lc_csomap_data_dir, "label.txt"), row.names = F, sep = "\t", quote = F) # 不要引号
# copy LR_pairs.txt
file.copy(from = file.path(lc_csomap_data_dir,"..","demo","LR_pairs.txt"), to = file.path(lc_csomap_data_dir, "LR_pairs.txt"))
# 导出TPM.txt
tpm <- exp(lc_scRNA[['RNA']]@data)
tpm <- tpm - 1
tpm <- tpm*100 # 1E4 to 1E6
colnames(tpm)[1] = paste0('T', colnames(tpm)[1]) # 预留\t位置
write.table(tpm, file.path(lc_csomap_data_dir, "TPM.txt"), sep = "\t", quote = F) # 不要引号
lc_fix <- tempfile()
lc_py <- sprintf('
import mmap, os
def mapfile(filename, *args, size=None, **kwargs):
file = open(filename, *args, **kwargs)
if size is None: size = os.path.getsize(filename)
return mmap.mmap(file.fileno(), size)
path = "%s"
print(path, "%s")
f = mapfile(path,"r+", size=10)
f[0:1] = b"\t"
print(f[:])
f.close()
print("Done")
', file.path(lc_csomap_data_dir, "TPM.txt"), lc_fix)
print(lc_py)
cat(file=lc_fix, lc_py)
print(system(paste("python3", lc_fix), intern = T))
}
f_prepare4cellphoneDB <- function(lc_scRNA, lc_dir, lc_className){
if (!file.exists(lc_dir)){dir.create(lc_dir)}
# 生成 count.txt
write.table(as.matrix(lc_scRNA@assays$RNA@data), file.path(lc_dir,'cellphonedb_count.txt'), sep='\t', quote=F)
# 生成 meta.txt
lc_meta_data <- cbind(rownames(lc_scRNA@meta.data), lc_scRNA@meta.data[, lc_className, drop=F])
lc_meta_data <- as.matrix(lc_meta_data)
lc_meta_data[is.na(lc_meta_data)] = "Unkown" # 细胞类型中不能有NA
write.table(lc_meta_data, file.path(lc_dir,'cellphonedb_meta.txt'), sep='\t', quote=F, row.names=F)
}
f_image_output <- function(fileName, image, width=1920, height=1080, lc_pdf=T, lc_resolution=72){
if(lc_pdf){
width = width / lc_resolution
height = height / lc_resolution
pdf(paste(fileName, ".pdf", sep=""), width = width, height = height)
}else{
png(paste(fileName, ".png", sep=""), width = width, height = height)
}
print(image)
dev.off()
}
# Rearrange data column sequence
library(dplyr)
f_cDB_order_sequence <- function(lc_df){
da <- data.frame()
df <- subset(lc_df, receptor_a == 'True' & receptor_b == 'False' receptor_a == 'False' & receptor_b == 'True')
for(i in 1:length(df$gene_a)){
sub_data <- df[i, ]
if(sub_data$receptor_b=='False'){
if(sub_data$receptor_a=='True'){
old_names <- colnames(sub_data)
my_list <- strsplit(old_names[-c(1:11)], split="\\")
my_character <- paste(sapply(my_list, '[[', 2L), sapply(my_list, '[[', 1L), sep='')
new_names <- c(names(sub_data)[1:4], 'gene_b', 'gene_a', 'secreted', 'receptor_b', 'receptor_a', "annotation_strategy", "is_integrin", my_character)
sub_data = dplyr::select(sub_data, new_names)
# print('Change sequence!!!')
names(sub_data) <- old_names
da = rbind(da, sub_data)
}
}else{
da = rbind(da, sub_data)
}
}
return(da)
}
f_cDB_mergePandM <- function(means_order, pvals_order){
means_sub <- means_order[, c('interacting_pair', colnames(means_order)[-c(1:11)])]
pvals_sub <- pvals_order[, c('interacting_pair', colnames(means_order)[-c(1:11)])]
means_gather <- tidyr::gather(means_sub, celltype, mean_expression, names(means_sub)[-1])
pvals_gather <- tidyr::gather(pvals_sub, celltype, pval, names(pvals_sub)[-1])
mean_pval <- dplyr::left_join(means_gather, pvals_gather, by = c('interacting_pair', 'celltype'))
mean_pval
}
f_readcellphoneDB <- function(lc_dir){
res = list()
res$pvals <- f_cDB_order_sequence(read.delim(file.path(lc_dir, "out","pvalues.txt"), check.names = FALSE))
res$means <- f_cDB_order_sequence(read.delim(file.path(lc_dir, "out", "means.txt"), check.names = FALSE))
res$s_means <- read.delim(file.path(lc_dir, "out", "significant_means.txt"), check.names = FALSE)
res$m_p <- f_cDB_mergePandM(res$means, res$pvals)
lc_tp <- res$m_p %>% dplyr::select(interacting_pair, celltype, pval) %>% tidyr::spread(key=celltype, value=pval)
lc_sig_pairs <- lc_tp[which(rowSums(lc_tp<=0.05)!=0), ]
res$s_m_p <- subset(res$m_p, interacting_pair %in% lc_sig_pairs$interacting_pair)
res
}
f_cDB_dotplot <- function(lc_m_p){
lc_m_p %>% ggplot(aes(x=interacting_pair, y=celltype)) +
# geom_point(aes(color=log2(mean_expression), size=pval)) +
# scale_size(trans = 'reverse') +
geom_point(aes(color=log2(mean_expression), size=-log10(pval+1*10^-3)) ) +
guides(colour = guide_colourbar(order = 1),size = guide_legend(order = 2)) +
labs(x='', y='') +
scale_color_gradientn(name='Expression level \n(log2 mean expression \nmolecule1, molecule2)', colours = terrain.colors(100)) +
# scale_color_gradient2('Expression level \n(log2 mean expression \nmolecule1, molecule2)', low = 'blue', mid = 'yellow', high = 'red') +
theme(axis.text.x= element_text(angle=45, hjust=1)) +
# coord_flip() +
theme(
panel.border = element_rect(color = 'black', fill = NA),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank()
# plot.title = element_text(hjust = 0.5),
# legend.position = 'bottom' # guides(fill = guide_legend(label.position = "bottom"))
# legend.position = "bottom"
# axis.text.y.right = element_text(angle=270, hjust=0.5)
) +
theme(legend.key.size = unit(0.4, 'cm'), #change legend key size
# legend.key.height = unit(1, 'cm'), #change legend key height
# legend.key.width = unit(1, 'cm'), #change legend key width
legend.title = element_text(size=9), #change legend title font size
legend.text = element_text(size=8)) #change legend text font size
}
|