pilot3: Comparison of cell calling algorithms
C.B. Azodi
2022-03-02
Last updated: 2022-03-02
Checks: 5 2
Knit directory: BAUH_2020_MND-single-cell/analysis/
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Unstaged changes:
Modified: analysis/2022-01-07_pilot3_Cell-Calling-Comparison.Rmd
Modified: analysis/2022-02-28_pilot3_Cell-demultiplexing-Lenti.Rmd
Modified: analysis/2022-03-01_pilot3_cellbender.Rmd
Modified: analysis/2022-03-01_pilot3_dropkick.Rmd
Modified: analysis/index.Rmd
Modified: code/get_barcodes_to_use.R
Modified: config/config_pilot3.0_MN.yml
Modified: config/config_pilot3.0_iPSC.yml
Modified: workflow/Snakefile
Modified: workflow/rules_cellcalling.smk
Modified: workflow/rules_demultiplexing.smk
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 799d504 | cazodi | 2022-02-04 | updates since server crash |
| Rmd | 88c5ba9 | cazodi | 2022-01-25 | add picard |
| Rmd | 8e5ef43 | cazodi | 2022-01-12 | at2 analysis update |
| Rmd | f75c7ba | cazodi | 2022-01-11 | updated pipeline for pilot3 |
suppressPackageStartupMessages({
library(ggplot2)
library(ggvenn)
library(ComplexHeatmap)
library(Seurat)
})
rerun <- TRUE
wd <- "/mnt/beegfs/mccarthy/backed_up/general/cazodi/Projects/BAUH_2020_MND-single-cell/output/pilot3.0_MN/"load_cells_from_list <- function(path){
for (i in seq(1, 4)){
tmp <- scan(paste0(wd, gsub("Capture1", paste0("Capture", i), path)), what="character")
tmp <- paste0(tmp, "-c", i)
if (i == 1){
all <- tmp
} else{ all <- c(all, tmp)}
}
return(all)
}
load_cells_from_dQC <- function(path, umi=1000){
for (i in seq(1, 4)){
df <- read.table(paste0(wd, gsub("Capture1", paste0("Capture", i), path)), sep=" ")
df <- df[df$umi >= umi, ]
tmp <- row.names(df)
tmp <- paste0(tmp, "-c", i)
if (i == 1){
all <- tmp
} else{ all <- c(all, tmp)}
}
return(all)
}
load_cells_with_turboGFP <- function(path, min=1, rerun=FALSE) {
for (i in seq(1, 4)){
message("Processing Capture", i)
rawpath <- paste0(wd, gsub("Capture1", paste0("Capture", i), path))
if(rerun) {
rawCounts <- Seurat::Read10X(rawpath)
gfpCounts <- as.data.frame(list(GFP = rawCounts["turboGFP", ]))
rm(rawCounts)
saveRDS(gfpCounts, paste0(rawpath, "TurboGFP-counts.rds"))
} else{
gfpCounts <- readRDS(paste0(rawpath, "TurboGFP-counts.rds"))
}
tmp <- row.names(gfpCounts)[which(gfpCounts$GFP >= min)]
tmp <- paste0(tmp, "-c", i)
if (i == 1){
all <- tmp
} else{ all <- c(all, tmp)}
}
return(all)
}Compare cells called by different methods
Only looking at Sample 1 GEX: SI-TT-D6: Unfixed (HBSS) cells with TurboGFT tag (target 20k cells) results as the fixed cells were poor quality.
cellranger <- load_cells_from_list("01_cellcalling-cellRanger/Capture1-GEX/outs/filtered_feature_bc_matrix/barcodes.tsv.gz")
dropkick <- load_cells_from_list("01_cellcalling-dropkick/Capture1-GEX/raw_feature_bc_matrix_dropkick_barcodes.txt")
cellbender <- load_cells_from_list("01_cellcalling-cellbender/Capture1-GEX/matrix_cell_barcodes.csv")
dropletqc <- load_cells_from_dQC("01_cellcalling-dropletQC/Capture1-GEX/dropletQC_cell_barcodes.tsv")
turbogfp <- load_cells_with_turboGFP("01_cellcalling-cellRanger/Capture1-GEX/outs/raw_feature_bc_matrix/", rerun=FALSE)Processing Capture1Processing Capture2Processing Capture3Processing Capture4Overlap in barcodes called as cells:
cellList <- list(CellRanger = cellranger,
dropkick = dropkick,
cellbender = cellbender,
DropletQC = dropletqc)
ggvenn(cellList, stroke_size = 0.5, set_name_size = 4, text_size = 3,
fill_color = c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF")) 
Venn diagram of overlap in barcodes called as cells by different methods.
df <- as.data.frame(list(method=c("CellRanger", "dropkick", "DropletQC", "cellbender"),
nCells=c(length(cellranger), length(dropkick),
length(dropletqc), length(cellbender)),
nGFP=c(sum(cellranger %in% turbogfp),
sum(dropkick %in% turbogfp),
sum(dropletqc %in% turbogfp),
sum(cellbender %in% turbogfp))))
df$percent_with_GFP <- round(df$nGFP / df$nCells *100, 2)
df %>% arrange(-percent_with_GFP) method nCells nGFP percent_with_GFP
1 DropletQC 25961 1564 6.02
2 dropkick 12546 733 5.84
3 cellbender 45501 2118 4.65
4 CellRanger 47010 2092 4.45Upset plots
m1 <- make_comb_mat(cellList)
UpSet(m1, set_order = c("CellRanger", "cellbender", "dropkick", "DropletQC"),
comb_col = c("#B0DBF1", "#253DA1", "#1D2570", "#000137")[comb_degree(m1)])
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.5 (Ootpa)
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] SeuratObject_4.0.4 Seurat_4.0.6 ComplexHeatmap_2.10.0
[4] ggvenn_0.1.9 dplyr_1.0.7 ggplot2_3.3.5
loaded via a namespace (and not attached):
[1] circlize_0.4.13 workflowr_1.6.2 plyr_1.8.6
[4] igraph_1.2.11 lazyeval_0.2.2 splines_4.1.1
[7] listenv_0.8.0 scattermore_0.7 digest_0.6.29
[10] foreach_1.5.1 htmltools_0.5.2 magick_2.7.3
[13] fansi_1.0.0 magrittr_2.0.1 tensor_1.5
[16] cluster_2.1.2 doParallel_1.0.16 ROCR_1.0-11
[19] globals_0.14.0 matrixStats_0.61.0 spatstat.sparse_2.1-0
[22] colorspace_2.0-2 ggrepel_0.9.1 xfun_0.28
[25] crayon_1.4.2 jsonlite_1.7.2 spatstat.data_2.1-2
[28] survival_3.2-13 zoo_1.8-9 iterators_1.0.13
[31] glue_1.6.0 polyclip_1.10-0 gtable_0.3.0
[34] leiden_0.3.9 GetoptLong_1.0.5 future.apply_1.8.1
[37] shape_1.4.6 BiocGenerics_0.40.0 abind_1.4-5
[40] scales_1.1.1 DBI_1.1.1 miniUI_0.1.1.1
[43] Rcpp_1.0.7 viridisLite_0.4.0 xtable_1.8-4
[46] clue_0.3-60 reticulate_1.22 spatstat.core_2.3-2
[49] stats4_4.1.1 htmlwidgets_1.5.4 httr_1.4.2
[52] RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2
[55] pkgconfig_2.0.3 farver_2.1.0 sass_0.4.0
[58] uwot_0.1.11 deldir_1.0-6 utf8_1.2.2
[61] tidyselect_1.1.1 labeling_0.4.2 rlang_0.4.12
[64] reshape2_1.4.4 later_1.3.0 munsell_0.5.0
[67] tools_4.1.1 generics_0.1.1 ggridges_0.5.3
[70] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0
[73] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[76] fs_1.5.2 fitdistrplus_1.1-6 purrr_0.3.4
[79] RANN_2.6.1 pbapply_1.5-0 future_1.23.0
[82] nlme_3.1-153 whisker_0.4 mime_0.12
[85] compiler_4.1.1 plotly_4.10.0 png_0.1-7
[88] spatstat.utils_2.3-0 tibble_3.1.6 bslib_0.3.1
[91] stringi_1.7.6 highr_0.9 lattice_0.20-45
[94] Matrix_1.4-0 vctrs_0.3.8 pillar_1.6.4
[97] lifecycle_1.0.1 spatstat.geom_2.3-1 lmtest_0.9-39
[100] jquerylib_0.1.4 GlobalOptions_0.1.2 RcppAnnoy_0.0.19
[103] data.table_1.14.2 cowplot_1.1.1 irlba_2.3.5
[106] httpuv_1.6.5 patchwork_1.1.1 R6_2.5.1
[109] promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3
[112] IRanges_2.28.0 parallelly_1.30.0 codetools_0.2-18
[115] MASS_7.3-54 assertthat_0.2.1 rprojroot_2.0.2
[118] rjson_0.2.20 withr_2.4.3 sctransform_0.3.2
[121] S4Vectors_0.32.3 mgcv_1.8-38 parallel_4.1.1
[124] rpart_4.1-15 tidyr_1.1.4 rmarkdown_2.11
[127] Rtsne_0.15 git2r_0.29.0 shiny_1.7.1