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10x-180831-demuxlet.Rmd
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10x-180831-demuxlet.Rmd
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---
title: "R Notebook"
output:
html_document:
df_print: paged
toc: true
toc_float:
smooth_scroll: true
---
```{r}
library(Seurat)
library(dplyr)
library(tidyr)
library(ggplot2)
```
```{r}
data <- readRDS('output/seurat_objects/180831/10x-180831')
```
```{r}
getDemuxletForSample <- function(i, outputdir){
demuxlet <- read.table(paste('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/old/data/demuxlet/', outputdir, '/180831_10x_s', i, '.best', sep=''), header=T)
demuxlet$correct_barcode <- paste(unlist(sapply(strsplit(as.character(demuxlet$BARCODE), '-'), '[[', 1)), '-', i, sep='')
rownames(demuxlet) <- demuxlet$correct_barcode
cells <- rownames(data@meta.data)[data@meta.data$timepoint == paste('T', i, sep='')]
demuxlet_filtered <- demuxlet[demuxlet$correct_barcode %in% cells, ]
demuxlet_filtered['sample'] <- i
sng_dbl_abm <- sapply(strsplit(as.character(demuxlet_filtered$BEST), '-'), '[[', 1)
demuxlet_filtered['sng_dbl_amb'] <- sng_dbl_abm
#add counts singlets, doublets and ambiguous
demuxlet_filtered[c('SNG', 'DBL', 'AMB')] <- 0
demuxlet_filtered <- demuxlet_filtered %>% mutate(value=1) %>% spread(sng_dbl_amb, value, fill=0)
demuxlet_filtered['sng_dbl_amb'] <- sng_dbl_abm
return(demuxlet_filtered)
}
getAllDemuxletResults <- function(outdir){
demuxlet_list <- list()
for (i in 1:5){
demuxlet <- getDemuxletForSample(i, outdir)
demuxlet_list[[i]] <- demuxlet
}
demuxlet_all <- do.call(rbind, unname(demuxlet_list))
demuxlet_all$label <- as.character(demuxlet_all$BEST)
demuxlet_all$label[startsWith(demuxlet_all$label, 'DBL')] <- 'DBL'
demuxlet_all$label[startsWith(demuxlet_all$label, 'AMB')] <- 'AMB'
df_sda <- as.data.frame(aggregate(demuxlet_all[c('SNG', 'DBL', 'AMB')], by=list(sample=demuxlet_all$sample), FUN=sum))
df_snp <- as.data.frame(aggregate(demuxlet_all[c('N.SNP')], by=list(sample=demuxlet_all$sample), FUN=mean))
print(cbind(df_sda, df_snp$N.SNP))
print('Total number of SNG, DBL and AMB:')
print(table(demuxlet_all$sng_dbl_amb))
print(paste('Total average N.SNP:', mean(demuxlet_all$N.SNP)))
hist(demuxlet_all$N.SNP)
return(demuxlet_all)
}
```
#Demuxlet with QCed VCF file
```{r}
demuxlet_all_qc <- getAllDemuxletResults('190110_demuxlet_new_genotypes/demuxlet_out/demuxlet_plink_bed-updated')
```
```{r results='hide', echo=FALSE, message=FALSE}
data <- AddMetaData(data, data.frame(row.names = demuxlet_all_qc$correct_barcode, label=demuxlet_all_qc$label))
p <- plot_grid(
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('44B', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('BAT14', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('1AF', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('13a', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('DBL', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('AMB', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
labels=c('44B', 'BAT14', '1AF', '13a', 'DBL', 'AMB'), nrow=3
)
```
```{r fig1, fig.height = 15, fig.width = 12, fig.align = "center"}
p
```
#Demuxlet with QCed VCF, exons only
```{r}
demuxlet_all_qc_exons <- getAllDemuxletResults('190110_demuxlet_new_genotypes/demuxlet_out/demuxlet_plink_bed-updated.exon_only.recode')
```
```{r results='hide', echo=FALSE, message=FALSE}
data <- AddMetaData(data, data.frame(row.names = demuxlet_all_qc_exons$correct_barcode, label=demuxlet_all_qc_exons$label))
p <- plot_grid(
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('44B', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('BAT14', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('1AF', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('13a', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('DBL', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('AMB', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
labels=c('44B', 'BAT14', '1AF', '13a', 'DBL', 'AMB'), nrow=3
)
```
```{r fig2, fig.height = 15, fig.width = 12, fig.align = "center"}
p
```
#Demuxlet with QCed VCF + imputed SNPs
```{r}
demuxlet_all_qc_imputed <- getAllDemuxletResults('190110_demuxlet_new_genotypes/demuxlet_out/demuxlet_chr1_22_combined.qc_r2_maf.recode')
```
```{r results='hide', echo=FALSE, message=FALSE}
data <- AddMetaData(data, data.frame(row.names = demuxlet_all_qc_imputed$correct_barcode, label=demuxlet_all_qc_imputed$label))
p <- plot_grid(
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('44B', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('BAT14', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('1AF', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('13a', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('DBL', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('AMB', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
labels=c('44B', 'BAT14', '1AF', '13a', 'DBL', 'AMB'), nrow=3
)
```
```{r fig3, fig.height = 15, fig.width = 12, fig.align = "center"}
p
```
#Demuxlet with QCed VCF + imputed SNPs, exons only
```{r}
demuxlet_all_qc_exons_imputed <- getAllDemuxletResults('190110_demuxlet_new_genotypes/demuxlet_out/demuxlet_chr1_22_combined.qc_r2_maf_exon.recode')
```
```{r results='hide', echo=FALSE, message=FALSE}
data <- AddMetaData(data, data.frame(row.names = demuxlet_all_qc_exons_imputed$correct_barcode, label=demuxlet_all_qc_exons_imputed$label))
p <- plot_grid(
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('44B', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('BAT14', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('1AF', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
DimPlot(data, cells.highlight = rownames(data@meta.data)[grep('13a', data@meta.data$label)], reduction.use='tsne', cols.use='gray', cols.highlight = 'blue'),
labels=c('44B', 'BAT14', '1AF', '13a'), nrow=3
)
```
```{r fig4, fig.height = 15, fig.width = 12, fig.align = "center"}
p
```
```{r results='hide'}
# demuxlet_list <- list()
# for (i in 1:5){
# demuxlet <- getDemuxletForSample(i, '190110_demuxlet_new_genotypes/demuxlet_out/demuxlet_plink_bed-updated.exon_only.recode')
# demuxlet_list[[i]] <- demuxlet
# }
# demuxlet_all <- do.call(rbind, unname(demuxlet_list))
#
# demuxlet_all$label <- as.character(demuxlet_all$BEST)
# demuxlet_all$label[startsWith(demuxlet_all$label, 'DBL')] <- 'DBL'
# demuxlet_all$label[startsWith(demuxlet_all$label, 'AMB')] <- 'AMB'
#
# data <- AddMetaData(data, as.vector(demuxlet_all['label']))
# TSNEPlot(data, group.by='label', pt.size=0.1)
#
# data@meta.data$label[is.na(data@meta.data$label)] <- "AMB"
#
#which(is.na(data@meta.data$label))
#data@meta.data$label[23431] <- 'DBL'
depot <- unlist(lapply(data@meta.data$label, function(x){
if (is.na(x)){
return(NA)
}
if (x == 'SNG-13a_13a'){
return('Subq')
} else if (x == 'SNG-1AF_1AF'){
return('Peri')
} else if (x == 'SNG-44B_44B'){
return('Visce')
} else if (x == 'SNG-BAT14_BAT14'){
return('Supra')
} else {
return('DBL')
}
}))
data@meta.data['depot'] <- depot
DimPlot(data, reduction='tsne', group.by='depot', pt.size=0.1)
```
Filter out doublets:
```{r}
#data <- SubsetData(data, cells.use=rownames(data@meta.data)[data@meta.data$label != 'DBL'])
```
```{r}
DimPlot(data, reduction='tsne', group.by='depot', pt.size=0.1)
#TSNEPlot(data, group.by='sample_name', pt.size=0.1)
```
```{r}
type <- unlist(lapply(as.vector(data@meta.data$depot), function(x){
if (x == 'Subq' || x == 'Visce'){
return('white')
} else {
return('brown')
}
}))
data@meta.data['type'] <- type
DimPlot(data, reduction='tsne', group.by='type', pt.size=0.1)
```
Add depot labels
```{r}
#data@meta.data['depot'] <- substr(data@meta.data$sample_name, 1, nchar(data@meta.data$sample_name)-2)
```
```{r}
#saveRDS(data, '../../10x-adipocyte-analysis/output/10x-180831')
```
#Summary of results
```{r}
demuxlet_all_qc['vcf'] <- 'qc'
demuxlet_all_qc_exons['vcf'] <- 'qc_exons'
demuxlet_all_qc_imputed['vcf'] <- 'qc_imputed'
demuxlet_all_qc_exons_imputed['vcf'] <- 'qc_exons_imputed'
demuxlet_all <- rbind(demuxlet_all_qc, demuxlet_all_qc_exons, demuxlet_all_qc_exons_imputed, demuxlet_all_qc_imputed)
test <- aggregate(demuxlet_all['sng_dbl_amb'], by=list(demuxlet_all$vcf, demuxlet_all$sng_dbl_amb), FUN=length)
df_aggregated <- aggregate(demuxlet_all[c('SNG', 'DBL', 'AMB')], by=list(VCF=demuxlet_all$vcf, timepoint=demuxlet_all$sample), FUN=sum)
df_sng_dbl_amb_vcf <- aggregate(demuxlet_all[c('SNG', 'DBL', 'AMB')], by=list(VCF=demuxlet_all$vcf), FUN=sum)
```
Number of SNPs, SNG, DBL, AMB
```{r}
df_snps_vcf <- aggregate(demuxlet_all['N.SNP'], by=list(VCF=demuxlet_all$vcf), FUN=mean)
df_sng_dbl_amb_vcf['N.SNP'] <- df_snps_vcf$N.SNP
as.data.frame(df_sng_dbl_amb_vcf)
```
```{r}
df_snps_vcf_timepoint <- aggregate(demuxlet_all['N.SNP'], by=list(VCF=demuxlet_all$vcf, timepoint=demuxlet_all$sample), FUN=mean)
ggplot(df_snps_vcf_timepoint, aes(x=VCF, y=N.SNP, fill=factor(timepoint))) +
geom_bar(stat='identity', position='dodge') +
labs(title='Number of SNPs', fill='timepoint', x='VCF file', y='') +
theme_gray() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
Number of singlets, doublets and ambiguous per VCF file.
```{r}
ggplot(df_aggregated, aes(x=VCF, y=SNG, fill=factor(timepoint))) +
geom_bar(stat='identity', position='dodge') +
labs(title='Number of singlets', fill='timepoint', x='VCF file', y='') +
theme_gray() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```{r}
ggplot(df_aggregated, aes(x=VCF, y=DBL, fill=factor(timepoint))) +
geom_bar(stat='identity', position='dodge') +
labs(title='Number of doublets', fill='timepoint', x='VCF file', y='') +
theme_gray() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```{r}
ggplot(df_aggregated, aes(x=VCF, y=AMB, fill=factor(timepoint))) +
geom_bar(stat='identity', position='dodge') +
labs(title='Number of ambiguous', fill='timepoint', x='VCF file', y='') +
theme_gray() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
Percentage doublets
```{r}
print('QCed')
sum(demuxlet_all_qc$DBL) / length(demuxlet_all_qc$DBL)
print('QCed, exons only')
sum(demuxlet_all_qc_exons$DBL) / length(demuxlet_all_qc_exons$DBL)
print('QCed + imputed')
sum(demuxlet_all_qc_imputed$DBL) / length(demuxlet_all_qc_imputed$DBL)
print('QCed + imputed, exons only')
sum(demuxlet_all_qc_exons_imputed$DBL) / length(demuxlet_all_qc_exons_imputed$DBL)
```
```{r}
#
```