This repository has been archived by the owner on Mar 9, 2023. It is now read-only.
/
zheng17_cellranger_utils.R
263 lines (257 loc) · 10.8 KB
/
zheng17_cellranger_utils.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
#
# Copyright (c) 2016 10x Genomics, Inc. All rights reserved.
#
# ----------------------
# load (pure types)
# ----------------------
load_purified_pbmc_types<-function(pure_select_file,pure_gene_ens) {
pure_select_id <- pure_select_file$pure_id # from pure_select_file
pure_select_avg <- pure_select_file$pure_avg # from pure_select_file
pure_use_genes <- pure_select_file$pure_use_genes # from pure_select_file
pure_use_genes_ens<-pure_gene_ens[pure_use_genes]
avg<-data.frame(t(pure_select_avg))
rownames(avg)<-pure_use_genes_ens
names(avg)<-pure_select_id
return(avg)
}
# -----------------------------------
# train a multinomial (averging)
# -----------------------------------
.train_multinomial <- function(x) {
(1+colSums(x)) / sum(1+colSums(x))
}
# -----------------------------------
# train a multinomial (averging)
# -----------------------------------
.compare_by_cor<-function(m_filt,use_gene_ids,dmap_data) {
sig_genes <- intersect(use_gene_ids, rownames(dmap_data))
m_forsig <- as.matrix(m_filt[,which(use_gene_ids %in% sig_genes)])
sig_data_filt <- dmap_data[match(use_gene_ids[which(use_gene_ids %in% sig_genes)], rownames(dmap_data)),]
z <- lapply(1:ncol(sig_data_filt), function(j) sapply(1:nrow(m_forsig), function(i) cor(m_forsig[i,], sig_data_filt[,j], method='spearman')))
z <- do.call(cbind, z)
colnames(z) <- colnames(sig_data_filt)
z
}
# -------------------------------------
# normalize expression of one gene
# -------------------------------------
# v - expression vector of one gene
.normalize_by_gene<-function(v) {
m <- log(1+v)
m<-m-mean(m)
m<-m/sd(m)
}
# -------------------------------
# get cluster specific genes
# -------------------------------
.get_cluster_specific_genes<-function(km_avg,use_genes,clus_id,n=10) {
gene_scores <- do.call(rbind,lapply(1:nrow(km_avg), function(i) {
data.frame(subclu=i,
gene=use_genes,
abs_score=colMeans(abs(sweep(log2(km_avg[-i,]), 2, log2(km_avg[i,]), '-'))),
pos_score=colMeans(sweep(log2(km_avg[-i,]), 2, log2(km_avg[i,]), '-')),
stringsAsFactors=F)
} ) )
subclu_specific_genes_pos_score <- unique((gene_scores %>% group_by(subclu) %>% arrange(pos_score) %>% top_n(n, -pos_score) %>% ungroup())$gene)
subclu_specific_gene_data <- km_avg[,match(subclu_specific_genes_pos_score, use_genes)]
colnames(subclu_specific_gene_data) <- subclu_specific_genes_pos_score
rownames(subclu_specific_gene_data)<-clus_id
sub_nor<-scale(subclu_specific_gene_data)
list(sub_nor=sub_nor,gene_scores=gene_scores)
}
# ------------------------------------------------
# save figures to file with specified format
# ------------------------------------------------
.save_figure <- function(ggp,dir,id,marker,width,height) {
# create file name
x <- id
x <- gsub('+','',x,fixed = TRUE); x <- gsub('-','',x,fixed = TRUE)
x <- gsub(' ','_',x,fixed = TRUE); x <- gsub('/','_',x,fixed = TRUE)
filepath <- file.path(dir,paste(x,'_',marker,'.png',sep=""))
ggsave(file=filepath,ggp,width=width,height=height)
cat('Saved figure to: ',filepath,'\n')
}
# ------------------------------------------------
# plot gene expression based on gene markers
# ------------------------------------------------
.plot_gene_expression <- function(markers, genes, mat, tsne_df, style, title, dir=NULL) {
for (marker in markers) {
idx<-match(marker,genes)
goi<-.normalize_by_gene(mat[,idx])
p <- ggplot(tsne_df,aes(X1,X2,col=goi))+geom_point(size=0)+ggtitle(paste(title,'(',marker,')')) +
theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
midpnt <- 0.5
if (style == 'type1') {
min_lim <- min(goi); max_lim <- max(goi)
if (marker == 'CD8A') {midpnt <- 0; min_lim <- -1}
p <- p + scale_colour_gradient2(low="royalblue3",high="red",mid="white",midpoint=midpnt,limits=c(min_lim,max_lim),
guide=guide_colorbar(barwidth = 0.8, barheight = 5))
} else if (style == 'type2') {
if (marker == 'FCER1A') {midpnt <- 1}
if (marker == 'S100A8') {midpnt <- 0.3}
p <- p + scale_colour_gradient2(low="olivedrab1",high="red",mid="white",midpoint=midpnt,
guide=guide_colorbar(barwidth = 0.8, barheight = 5))
}
if (is.null(dir)) {
print(p)
} else {
.save_figure(p,dir,title,marker,width=4.0,height=3.5)
}
}
}
# ---------------------------------------------------------
# plot cluster heatmap and save cluster specific genes
# ---------------------------------------------------------
.plot_heatmap_and_write_genes <- function(n_clust,tsne_df,mat,genes,dir) {
clu<-c(1:n_clust)
km_centers <- do.call(cbind, lapply(1:n_clust,function(i) .train_multinomial(mat[which(tsne_df$k == clu[i]),])))
km_avg<-t(km_centers)
sub_nor<-.get_cluster_specific_genes(km_avg,genes,clu,10)
write.table(sub_nor$gene_scores,file = dir,sep="\t",quote=F,row.names = F)
pheatmap(sub_nor$sub_nor,cluster_rows = T,cluster_cols = T,fontsize_col=8,color=grey.colors(8))
}
# --------------------------------------------------
# get variable genes from normalized UMI counts
# --------------------------------------------------
# m: matrix normalized by UMI counts
.get_variable_gene<-function(m) {
df<-data.frame(mean=colMeans(m),cv=apply(m,2,sd)/colMeans(m),var=apply(m,2,var))
df$dispersion<-with(df,var/mean)
df$mean_bin<-with(df,cut(mean,breaks=c(-Inf,quantile(mean,seq(0.1,1,0.05)),Inf)))
var_by_bin<-ddply(df,"mean_bin",function(x) {
data.frame(bin_median=median(x$dispersion),
bin_mad=mad(x$dispersion))
})
df$bin_disp_median<-var_by_bin$bin_median[match(df$mean_bin,var_by_bin$mean_bin)]
df$bin_disp_mad<-var_by_bin$bin_mad[match(df$mean_bin,var_by_bin$mean_bin)]
df$dispersion_norm<-with(df,abs(dispersion-bin_disp_median)/bin_disp_mad)
df
}
# -----------------------------------------------------
# compute top n principle components with propack
# -----------------------------------------------------
.do_propack <- function(x,n) {
use_genes <- which(colSums(x) > 1)
m <- x[,use_genes]
bc_tot <- rowSums(m)
median_tot <- median(bc_tot)
m <- sweep(m, 1, median_tot/bc_tot, '*')
m <- log(1+m)
m <- sweep(m, 2, colMeans(m), '-')
m <- sweep(m, 2, apply(m, 2, sd), '/')
ppk<-propack.svd(as.matrix(m),neig=n)
pca<-t(ppk$d*t(ppk$u))
list(ppk=ppk,pca=pca, m=m,use_genes=use_genes)
}
# ---------------------------------------------
# normalize the gene barcode matrix by umi
# ---------------------------------------------
.normalize_by_umi <-function(x) {
cs <- colSums(x)
x_use_genes <- which(cs >= 1)
x_filt<-x[,x_use_genes]
rs<-rowSums(x_filt)
rs_med<-median(rs)
x_norm<-x_filt/(rs/rs_med)
list(m=x_norm,use_genes=x_use_genes)
}
# --------------------------------------
# down-sample a gene-barcode matrix
# --------------------------------------
.downsample_gene_bc_mtx <- function(json, orig_matrix_data, mol_info, tgt_rpc, target_type='raw_reads', transcriptome='hg19') {
if (!(target_type %in% c('raw_reads', 'conf_mapped_reads'))) {
stop(sprintf('Unsupported target_type: %s', target_type))
}
if (length(orig_matrix_data) > 2) {
warning('Multiple transcriptomes not yet implemented')
}
cat("Filtering mol info\n")
mol_info <- mol_info[reads > 0]
cat("Sorting mol info\n")
setkey(mol_info, barcode, gene, umi)
cat("Aggregating mol info\n")
bc_gene_umi <- mol_info[, j=list(reads=sum(reads)), by=c('barcode', 'gene', 'umi')]
n_cells <- json[[sprintf("%s_filtered_bcs", transcriptome)]]
tot_reads <- json$total_reads
candidate_reads <- sum(bc_gene_umi$reads)
candidate_read_frac <- candidate_reads / json$total_reads
orig_mat_barcodes <- sub('-.*$', '', orig_matrix_data[[transcriptome]]$barcodes)
subsampled_mats <- lapply(tgt_rpc, function(tgt_rpc_i) {
cat('.')
if (target_type == 'raw_reads') {
tgt_candidate_reads <- tgt_rpc_i * n_cells * candidate_read_frac
candidate_rpc <- tgt_candidate_reads / n_cells
raw_reads_per_cell <- tgt_rpc_i
} else if (target_type == 'conf_mapped_reads') {
tgt_candidate_reads <- tgt_rpc_i * n_cells
candidate_rpc <- tgt_candidate_reads / n_cells
raw_reads_per_cell <- candidate_rpc / candidate_read_frac
}
if (tgt_candidate_reads > candidate_reads) {
return(NA)
}
subsample_rate <- tgt_candidate_reads / candidate_reads
cat("Subsampling\n")
bc_gene_umi_subsampled <- bc_gene_umi %>% mutate(reads=rbinom(length(reads), reads, subsample_rate))
cat("Sorting\n")
setkey(bc_gene_umi_subsampled, barcode, gene)
cat("Re-aggregating\n")
bc_gene_counts <- bc_gene_umi_subsampled[barcode %in% orig_mat_barcodes, j=list(count=sum(reads > 0)), by=c('barcode', 'gene')]
cat("Building matrix\n")
with(bc_gene_counts, sparseMatrix(i = match(barcode, orig_mat_barcodes),
j = 1 + gene, x = count, dims=dim(orig_matrix_data[[transcriptome]]$mat)))
} )
return(subsampled_mats)
}
# ------------------------------------------
# reassign purified ids, due to overlap
# ------------------------------------------
.reassign_pbmc_11<-function(z) {
unlist(lapply(1:nrow(z),function(i) {
best<-which.max(z[i,])
x<-z[i,]
nextbest<-which.max(x[x!=max(x)])
# if best is CD4+ T helper, and the next best is cd4+/cd25+, or cd4+/cd45ro+ or cd4+/cd45ra+/cd25-, use the next best assignment
if (best==9 & (nextbest==3 || nextbest==4 || nextbest==6)) {
best=nextbest
}
# if best is CD8+, and the next best is CD8+/CD45RA+, use next best assignment
if (best==7 & nextbest==5) {
best=5
}
best
}))
}
# ----------------------------------------
# set color 68k colors for aesthetics
# ----------------------------------------
.set_pbmc_color_11<-function() {
myColors <- c( "dodgerblue2",
"green4",
"#6A3D9A", # purple
"grey",
"tan4",
"yellow",
"#FF7F00", # orange
"black",
"#FB9A99", # pink
"orchid",
"red")
id<-c("CD19+ B","CD14+ Monocyte","Dendritic","CD56+ NK","CD34+","CD4+/CD25 T Reg","CD4+/CD45RA+/CD25- Naive T","CD4+/CD45RO+ Memory","CD4+ T Helper2","CD8+/CD45RA+ Naive Cytotoxic","CD8+ Cytotoxic T")
names(myColors)<-id
scale_colour_manual(name = "cls",values = myColors)
}
c25 <- c("dodgerblue2","#E31A1C", # red
"green4",
"#6A3D9A", # purple
"#FF7F00", # orange
"black","gold1",
"skyblue2",
"#FB9A99", # lt pink
"palegreen2",
"#CAB2D6", # lt purple
"#FDBF6F", # lt orange
"gray70", "khaki2",
"maroon","orchid1","deeppink1","blue1","steelblue4",
"darkturquoise","green1","yellow4","yellow3",
"darkorange4","brown")