R Notebook
Last updated: 2019-05-13
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Knit directory: 10x-adipocyte-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 158bede | Pytrik Folkertsma | 2019-04-17 | Build site. |
| Rmd | 7b6d197 | Pytrik Folkertsma | 2019-04-17 | added nCells tables |
| html | ac858a1 | Pytrik Folkertsma | 2019-04-01 | Build site. |
| Rmd | c22f3f2 | Pytrik Folkertsma | 2019-04-01 | demuxlet results |
| html | ea82b7d | Pytrik Folkertsma | 2019-04-01 | Build site. |
| Rmd | a8a36a6 | Pytrik Folkertsma | 2019-04-01 | demuxlet results |
| html | 07bf255 | Pytrik Folkertsma | 2019-03-28 | Build site. |
| Rmd | ca15d0e | Pytrik Folkertsma | 2019-03-28 | demuxlet notebook |
| Rmd | 03050a1 | Pytrik Folkertsma | 2019-03-28 | analysis updates |
library(Seurat)
library(dplyr)
library(tidyr)
library(ggplot2)data <- readRDS('output/seurat_objects/180831/10x-180831-notcleaned')getDemuxletForSample <- function(i, outputdir){
demuxlet <- read.table(paste('output/demuxlet/190110_demuxlet_new_genotypes/demuxlet_out/', 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
demuxlet_all_qc <- getAllDemuxletResults('demuxlet_plink_bed-updated') sample SNG DBL AMB df_snp$N.SNP
1 1 3074 359 0 89.41159
2 2 5220 311 0 78.10541
3 3 5761 289 0 90.71223
4 4 4058 432 2 51.57124
5 5 6659 675 31 40.57733
[1] "Total number of SNG, DBL and AMB:"
AMB DBL SNG
33 2066 24772
[1] "Total average N.SNP: 67.6666294518254"
p
Demuxlet with QCed VCF, exons only
demuxlet_all_qc_exons <- getAllDemuxletResults('demuxlet_plink_bed-updated.exon_only.recode') sample SNG DBL AMB df_snp$N.SNP
1 1 3056 377 0 68.64433
2 2 5153 376 2 56.22528
3 3 5721 329 0 66.24860
4 4 3957 518 17 35.26870
5 5 6460 749 156 26.79063
[1] "Total number of SNG, DBL and AMB:"
AMB DBL SNG
175 2349 24347
[1] "Total average N.SNP: 48.4977112872614"
p
Demuxlet with QCed VCF + imputed SNPs
demuxlet_all_qc_imputed <- getAllDemuxletResults('demuxlet_chr1_22_combined.qc_r2_maf.recode') sample SNG DBL AMB df_snp$N.SNP
1 1 2948 485 0 146.00524
2 2 5065 465 1 147.65811
3 3 5593 457 0 165.16992
4 4 4015 477 0 379.63268
5 5 5846 1519 0 86.84942
[1] "Total number of SNG, DBL and AMB:"
AMB DBL SNG
1 3403 23467
[1] "Total average N.SNP: 173.501804919802"
p
Demuxlet with QCed VCF + imputed SNPs, exons only
demuxlet_all_qc_exons_imputed <- getAllDemuxletResults('demuxlet_chr1_22_combined.qc_r2_maf_exon.recode') sample SNG DBL AMB df_snp$N.SNP
1 1 2890 543 0 75.05884
2 2 5211 320 0 223.39487
3 3 5474 576 0 76.20165
4 4 3714 778 0 154.23753
5 5 6139 1226 0 104.61779
[1] "Total number of SNG, DBL and AMB:"
DBL SNG
3443 23428
[1] "Total average N.SNP: 127.186892932902"
p
demuxlet_list <- list()
for (i in 1:5){
demuxlet <- getDemuxletForSample(i, 'demuxlet_chr1_22_combined.qc_r2_maf_exon.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'
rownames(demuxlet_all) <- demuxlet_all$correct_barcode
data <- AddMetaData(data, as.vector(demuxlet_all['label']))
TSNEPlot(data, group.by='label', pt.size=0.1)
data@meta.data$label[is.na(data@meta.data$label)] <- "AMB"
depot <- unlist(lapply(data@meta.data$label, function(x){
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:
data_cleaned <- SubsetData(data, cells.use=rownames(data@meta.data)[data@meta.data$depot != 'DBL'])DimPlot(data_cleaned, reduction='tsne', group.by='depot', pt.size=0.1)
type <- unlist(lapply(as.vector(data_cleaned@meta.data$depot), function(x){
if (x == 'Subq' || x == 'Visce'){
return('white')
} else {
return('brown')
}
}))
data_cleaned@meta.data['type'] <- type
DimPlot(data_cleaned, reduction='tsne', group.by='type', pt.size=0.1)
paste('Nr of cells before removing doublet and ambiguous:', length(rownames(data@meta.data)))[1] "Nr of cells before removing doublet and ambiguous: 26872"paste('Nr of cells in cleaned up data:', length(rownames(data_cleaned@meta.data)))[1] "Nr of cells in cleaned up data: 23428"Number of cells per donor and timepoint.
n_cells <- as.data.frame(as.matrix(ftable(data_cleaned@meta.data$timepoint, data_cleaned@meta.data$depot)))
n_cells[nrow(n_cells), ] <- colSums(n_cells)
rownames(n_cells)[length(rownames(n_cells))] <- 'total'
n_cells[,ncol(n_cells)+1] <- rowSums(n_cells)
names(n_cells)[length(names(n_cells))] <- 'total'
n_cells Peri Subq Supra Visce total
T1 692 750 608 840 2890
T2 1273 1336 1351 1251 5211
T3 1259 1605 1118 1492 5474
T4 1236 762 857 859 3714
T5 1139 1816 1640 1544 6139
total 5599 6269 5574 5986 23428#write.table(n_cells, '../tables/tables_paper/supplementary_tables/10x-180831-ncells.tsv', sep='\t', quote=F, col.names=NA)df <- merge(demuxlet_all_qc_exons_imputed, data@meta.data, by.x='correct_barcode', by.y='row.names')
n_cells_timepoint_dbl_sng <- as.data.frame(as.matrix(ftable(df$timepoint, df$sng_dbl_amb)))
ncells_timepoint_snps <- aggregate(df$N.SNP, by=list(timepoint=df$timepoint), FUN=mean)
n_cells_combined <- merge(n_cells_timepoint_dbl_sng, ncells_timepoint_snps, by.x='row.names', by.y='timepoint')
rownames(n_cells_combined) <- n_cells_combined$Row.names
n_cells_combined['Row.names'] <- NULL
names(n_cells_combined)[length(names(n_cells_combined))] <- 'avg N.SNP'
#n_cells_combined[nrow(n_cells_combined)+1, ] <- colSums(n_cells_combined)
#rownames(n_cells_combined)[length(rownames(n_cells_combined))] <- 'total'
#n_cells_combined[,ncol(n_cells_combined)+1] <- rowSums(n_cells_combined)
#names(n_cells_combined)[length(names(n_cells_combined))] <- 'total'#write.table(n_cells_combined, '../tables/tables_paper/supplementary_tables/10x-180831-ncells_demuxlet.tsv', sep='\t', quote=F, col.names=NA)#data@meta.data['depot'] <- substr(data@meta.data$sample_name, 1, nchar(data@meta.data$sample_name)-2)
#saveRDS(data, '../../10x-adipocyte-analysis/output/10x-180831')Summary of results
demuxlet_all_qc['vcf'] <- 'qc'
demuxlet_all_qc_exons['vcf'] <- 'qc_exons'
demuxlet_all_qc_imputed['vcf'] <- 'qc_imputed'
demuxlet_all_qc_exons_imputed['vcf'] <- 'qc_exons_imputed'
demuxlet_all <- rbind(demuxlet_all_qc, demuxlet_all_qc_exons, demuxlet_all_qc_exons_imputed, demuxlet_all_qc_imputed)
test <- aggregate(demuxlet_all['sng_dbl_amb'], by=list(demuxlet_all$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
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) VCF SNG DBL AMB N.SNP
1 qc 24772 2066 33 67.66663
2 qc_exons 24347 2349 175 48.49771
3 qc_exons_imputed 23428 3443 0 127.18689
4 qc_imputed 23467 3403 1 173.50180df_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)) 
| Version | Author | Date |
|---|---|---|
| 158bede | Pytrik Folkertsma | 2019-04-17 |
Number of singlets, doublets and ambiguous per VCF file.
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))
| Version | Author | Date |
|---|---|---|
| 158bede | Pytrik Folkertsma | 2019-04-17 |
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))
| Version | Author | Date |
|---|---|---|
| 158bede | Pytrik Folkertsma | 2019-04-17 |
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))
| Version | Author | Date |
|---|---|---|
| 158bede | Pytrik Folkertsma | 2019-04-17 |
Percentage doublets
paste('QCed:',sum(demuxlet_all_qc$DBL) / length(demuxlet_all_qc$DBL))[1] "QCed: 0.0768858620817982"paste('QCed, exons only:', sum(demuxlet_all_qc_exons$DBL) / length(demuxlet_all_qc_exons$DBL))[1] "QCed, exons only: 0.0874176621636709"paste('QCed + imputed:', sum(demuxlet_all_qc_imputed$DBL) / length(demuxlet_all_qc_imputed$DBL))[1] "QCed + imputed: 0.126642104871423"paste('QCed + imputed, exons only', sum(demuxlet_all_qc_exons_imputed$DBL) / length(demuxlet_all_qc_exons_imputed$DBL))[1] "QCed + imputed, exons only 0.128130698522571"
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyr_0.8.3 dplyr_0.8.0.1 Seurat_2.3.4 Matrix_1.2-17 cowplot_0.9.4
[6] ggplot2_3.1.0
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 class_7.3-15
[4] modeltools_0.2-22 ggridges_0.5.1 mclust_5.4.3
[7] rprojroot_1.3-2 htmlTable_1.13.1 base64enc_0.1-3
[10] fs_1.2.7 rstudioapi_0.10 proxy_0.4-23
[13] npsurv_0.4-0 flexmix_2.3-15 bit64_0.9-7
[16] mvtnorm_1.0-10 codetools_0.2-16 splines_3.5.3
[19] R.methodsS3_1.7.1 lsei_1.2-0 robustbase_0.93-4
[22] knitr_1.22 jsonlite_1.6 Formula_1.2-3
[25] workflowr_1.2.0 ica_1.0-2 cluster_2.0.7-1
[28] kernlab_0.9-27 png_0.1-7 R.oo_1.22.0
[31] compiler_3.5.3 httr_1.4.0 backports_1.1.3
[34] assertthat_0.2.1 lazyeval_0.2.2 lars_1.2
[37] acepack_1.4.1 htmltools_0.3.6 tools_3.5.3
[40] igraph_1.2.4 gtable_0.3.0 glue_1.3.1
[43] reshape2_1.4.3 RANN_2.6.1 Rcpp_1.0.1
[46] trimcluster_0.1-2.1 gdata_2.18.0 ape_5.3
[49] nlme_3.1-137 iterators_1.0.10 fpc_2.1-11.1
[52] gbRd_0.4-11 lmtest_0.9-36 xfun_0.5
[55] stringr_1.4.0 irlba_2.3.3 gtools_3.8.1
[58] DEoptimR_1.0-8 MASS_7.3-51.1 zoo_1.8-5
[61] scales_1.0.0 doSNOW_1.0.16 parallel_3.5.3
[64] RColorBrewer_1.1-2 yaml_2.2.0 reticulate_1.11.1
[67] pbapply_1.4-0 gridExtra_2.3 rpart_4.1-13
[70] segmented_0.5-3.0 latticeExtra_0.6-28 stringi_1.4.3
[73] foreach_1.4.4 checkmate_1.9.1 caTools_1.17.1.2
[76] bibtex_0.4.2 Rdpack_0.10-1 SDMTools_1.1-221
[79] rlang_0.3.2 pkgconfig_2.0.2 dtw_1.20-1
[82] prabclus_2.2-7 bitops_1.0-6 evaluate_0.13
[85] lattice_0.20-38 ROCR_1.0-7 purrr_0.3.2
[88] labeling_0.3 htmlwidgets_1.3 bit_1.1-14
[91] tidyselect_0.2.5 plyr_1.8.4 magrittr_1.5
[94] R6_2.4.0 snow_0.4-3 gplots_3.0.1.1
[97] Hmisc_4.2-0 pillar_1.3.1 whisker_0.3-2
[100] foreign_0.8-71 withr_2.1.2 fitdistrplus_1.0-14
[103] mixtools_1.1.0 survival_2.43-3 nnet_7.3-12
[106] tsne_0.1-3 tibble_2.1.1 crayon_1.3.4
[109] hdf5r_1.1.1 KernSmooth_2.23-15 rmarkdown_1.12
[112] grid_3.5.3 data.table_1.12.0 git2r_0.25.2
[115] metap_1.1 digest_0.6.18 diptest_0.75-7
[118] R.utils_2.8.0 stats4_3.5.3 munsell_0.5.0