Analysis of scRNA-seq Data
Droplet Selection
Jovana Maksimovic (modified from code by Peter Hickey & William Ho)
December 19, 2022
Last updated: 2022-12-19
Checks: 7 0
Knit directory:
paed-cf-cite-seq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e799f52 | Jovana Maksimovic | 2022-12-19 | wflow_publish(c("analysis/emptyDrops.Rmd", "analysis/postprocess*.Rmd", |
| html | 63f8ee8 | Jovana Maksimovic | 2022-12-15 | Build site. |
| Rmd | 916bafa | Jovana Maksimovic | 2022-12-15 | wflow_publish(c("analysis/.emptyDrops.Rmd", "analysis/postprocess_*.Rmd", |
| Rmd | f3b7b92 | Jovana Maksimovic | 2022-06-16 | Submission version |
| html | f3b7b92 | Jovana Maksimovic | 2022-06-16 | Submission version |
Bronchoalveolar lavage (BAL) samples were collected from 4 individuals: 1 control sample and 3 cystic fibrosis (CF) samples. The samples were run on teh 10X Chromium and sequenced at the Garvan-Weizmann Centre for Cellular Genomics (GWCCG). The multiplexed samples were sequenced on an Illumina NovaSeq 6000 (NovaSeq Control Software v1.3.1 / Real Time Analysis v3.3.3) ) using a NovaSeq S1 200 cycle kit (Illumina, 20012864). The cellranger count pipeline (version 6.0.2) was used for alignment, filtering, barcode counting, and UMI counting from FASTQ files. The GRCh38 reference was used for the alignment. The number of cells from the pipeline was forced to 10,000.
View the CellRanger capture-specific web summaries: A, B, C, D.
1 Load libraries
suppressPackageStartupMessages(library(BiocStyle))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(here))
suppressPackageStartupMessages(library(glue))
suppressPackageStartupMessages(library(DropletUtils))
suppressPackageStartupMessages(library(scran))
suppressPackageStartupMessages(library(scater))
suppressPackageStartupMessages(library(scuttle))
suppressPackageStartupMessages(library(scds))
suppressPackageStartupMessages(library(scDblFinder))
set.seed(42)
options(scipen=999)
options(future.globals.maxSize = 6500 * 1024^2)2 Load data
sce <- readRDS(here("data/SCEs/04_CF_BAL_Pilot.CellRanger_v6.SCE.rds"))
dim(sce)[1] 33538 88535843 Identify empty droplets
We use the emptyDrops() function from the DropletUtils package to test whether the expression profile for each cell barcode is significantly different from the ambient RNA pool (Lun et al. 2018). A significant deviation indicates that the barcode corresponds to a cell-containing droplet. Cells are called at a false discovery rate (FDR) of 0.1%.
sce$capture <- factor(sce$Sample)
capture_names <- levels(sce$capture)
capture_names <- setNames(capture_names, capture_names)
empties <- do.call(rbind, lapply(capture_names, function(cn) {
message(cn)
empties <- readRDS(
here("data", "emptyDrops", paste0(cn, ".emptyDrops.rds")))
empties$capture <- cn
empties
}))
tapply(
empties$FDR,
empties$capture,
function(x) sum(x <= 0.001, na.rm = TRUE)) %>%
knitr::kable(
caption = "Number of non-empty droplets")| x | |
|---|---|
| A | 4980 |
| B | 6093 |
| C | 11197 |
| D | 12313 |
3.1 Examine results
par(mfrow = c(2, 2))
lapply(levels(sce$capture), function(s) {
sce <- sce[, sce$capture == s]
bcrank <- barcodeRanks(counts(sce))
# Only showing unique points for plotting speed.
uniq <- !duplicated(bcrank$rank)
plot(
x = bcrank$rank[uniq],
y = bcrank$total[uniq],
log = "xy",
xlab = "Rank",
ylab = "Total UMI count",
main = s,
cex.lab = 1.2,
xlim = c(1, 500000),
ylim = c(1, 200000))
abline(h = metadata(bcrank)$inflection, col = "darkgreen", lty = 2)
abline(h = metadata(bcrank)$knee, col = "dodgerblue", lty = 2)
})
| Version | Author | Date |
|---|---|---|
| f3b7b92 | Jovana Maksimovic | 2022-06-16 |
[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL4 Remove empty droplets
Remove empty droplets.
sce <- sce[, which(empties$FDR <= 0.001)]
sceclass: SingleCellExperiment
dim: 33538 34583
metadata(1): Samples
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
ENSG00000268674
rowData names(3): ID Symbol Type
colnames(34583): 1_AAACCCAAGCTAGTTC-1 1_AAACCCACAAGATTGA-1 ...
4_TTTGTTGTCTAGTACG-1 4_TTTGTTGTCTCGAACA-1
colData names(3): Sample Barcode capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):5 Call within-sample doublets
Use scds and scDblFinder to try to identify within-sample doublets. Doublets are called on each capture separately.
out <- here("data/SCEs/experiment1_doublets.rds")
if(!file.exists(out)){
sceLst <- sapply(levels(sce$capture), function(cap){
## Annotate doublets using scds three step process as run in Demuxafy
sce1 <- bcds(sce[, sce$capture == cap],
retRes = TRUE, estNdbl = TRUE)
sce1 <- cxds(sce1, retRes = TRUE, estNdbl = TRUE)
sce1 <- cxds_bcds_hybrid(sce1, estNdbl = TRUE)
## Annotate doublets using scDblFInder with rate estimate from Demuxafy
sce1 <- scDblFinder(sce1, dbr = ncol(sce1)/1000*0.008)
sce1
})
lapply(sceLst, function(s){
colData(s) %>%
data.frame %>%
rownames_to_column(var = "cell")
}) %>%
bind_rows() %>%
saveRDS(file = out)
} 6 Save data
Save the object.
out <- here("data/SCEs/04_CF_BAL_Pilot.emptyDrops.SCE.rds")
if (!file.exists(out)) saveRDS(sce, file = out)7 Session info
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.0 (2021-05-18)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2022-12-19
pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
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P scuttle * 1.4.0 2021-10-26 [?] Bioconductor
P sessioninfo 1.2.2 2021-12-06 [?] CRAN (R 4.1.0)
P SingleCellExperiment * 1.16.0 2021-10-26 [?] Bioconductor
P sparseMatrixStats 1.6.0 2021-10-26 [?] Bioconductor
P statmod 1.4.36 2021-05-10 [?] CRAN (R 4.1.0)
P stringi 1.7.6 2021-11-29 [?] CRAN (R 4.1.0)
P stringr * 1.4.0 2019-02-10 [?] CRAN (R 4.0.2)
P SummarizedExperiment * 1.24.0 2021-10-26 [?] Bioconductor
P tibble * 3.1.6 2021-11-07 [?] CRAN (R 4.1.0)
P tidyr * 1.1.4 2021-09-27 [?] CRAN (R 4.1.0)
P tidyselect 1.1.1 2021-04-30 [?] CRAN (R 4.1.0)
P tidyverse * 1.3.1 2021-04-15 [?] CRAN (R 4.1.0)
P tzdb 0.2.0 2021-10-27 [?] CRAN (R 4.1.0)
P utf8 1.2.2 2021-07-24 [?] CRAN (R 4.1.0)
P vctrs 0.3.8 2021-04-29 [?] CRAN (R 4.0.2)
P vipor 0.4.5 2017-03-22 [?] CRAN (R 4.1.0)
P viridis 0.6.2 2021-10-13 [?] CRAN (R 4.1.0)
P viridisLite 0.4.0 2021-04-13 [?] CRAN (R 4.0.2)
P whisker 0.4 2019-08-28 [?] CRAN (R 4.0.2)
P withr 2.4.3 2021-11-30 [?] CRAN (R 4.1.0)
P workflowr * 1.7.0 2021-12-21 [?] CRAN (R 4.1.0)
P xfun 0.29 2021-12-14 [?] CRAN (R 4.1.0)
P xgboost 1.5.0.2 2021-11-21 [?] CRAN (R 4.1.0)
P xml2 1.3.3 2021-11-30 [?] CRAN (R 4.1.0)
P XVector 0.34.0 2021-10-26 [?] Bioconductor
P yaml 2.2.1 2020-02-01 [?] CRAN (R 4.0.2)
P zlibbioc 1.40.0 2021-10-26 [?] Bioconductor
[1] /oshlack_lab/jovana.maksimovic/projects/MCRI/melanie.neeland/paed-cf-cite-seq/renv/library/R-4.1/x86_64-pc-linux-gnu
[2] /config/binaries/R/4.1.0/lib64/R/library
P ── Loaded and on-disk path mismatch.
──────────────────────────────────────────────────────────────────────────────8 References
Lun, A., S. Riesenfeld, T. Andrews, T. P. Dao, T. Gomes, participants in the 1st Human Cell Atlas Jamboree, and J. Marioni. 2018. “Distinguishing Cells from Empty Droplets in Droplet-Based Single-Cell RNA Sequencing Data.” bioRxiv. https://doi.org/10.1101/234872.
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /config/binaries/R/4.1.0/lib64/R/lib/libRblas.so
LAPACK: /config/binaries/R/4.1.0/lib64/R/lib/libRlapack.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] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] scDblFinder_1.8.0 scds_1.10.0
[3] scater_1.22.0 scran_1.22.1
[5] scuttle_1.4.0 DropletUtils_1.14.1
[7] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[9] Biobase_2.54.0 GenomicRanges_1.46.1
[11] GenomeInfoDb_1.30.1 IRanges_2.28.0
[13] S4Vectors_0.32.3 BiocGenerics_0.40.0
[15] MatrixGenerics_1.6.0 matrixStats_0.61.0
[17] glue_1.6.0 here_1.0.1
[19] forcats_0.5.1 stringr_1.4.0
[21] dplyr_1.0.7 purrr_0.3.4
[23] readr_2.1.1 tidyr_1.1.4
[25] tibble_3.1.6 ggplot2_3.3.5
[27] tidyverse_1.3.1 BiocStyle_2.22.0
[29] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.4.1
[3] plyr_1.8.6 igraph_1.2.11
[5] BiocParallel_1.28.3 digest_0.6.29
[7] htmltools_0.5.2 viridis_0.6.2
[9] fansi_1.0.0 magrittr_2.0.1
[11] ScaledMatrix_1.2.0 cluster_2.1.2
[13] tzdb_0.2.0 limma_3.50.0
[15] modelr_0.1.8 R.utils_2.11.0
[17] colorspace_2.0-2 rvest_1.0.2
[19] ggrepel_0.9.1 haven_2.4.3
[21] xfun_0.29 callr_3.7.0
[23] crayon_1.4.2 RCurl_1.98-1.6
[25] jsonlite_1.7.2 gtable_0.3.0
[27] zlibbioc_1.40.0 XVector_0.34.0
[29] DelayedArray_0.20.0 BiocSingular_1.10.0
[31] Rhdf5lib_1.16.0 HDF5Array_1.22.1
[33] scales_1.1.1 DBI_1.1.2
[35] edgeR_3.36.0 Rcpp_1.0.7
[37] viridisLite_0.4.0 dqrng_0.3.0
[39] rsvd_1.0.5 metapod_1.2.0
[41] httr_1.4.2 ellipsis_0.3.2
[43] pkgconfig_2.0.3 R.methodsS3_1.8.1
[45] sass_0.4.0 dbplyr_2.1.1
[47] locfit_1.5-9.4 utf8_1.2.2
[49] tidyselect_1.1.1 rlang_0.4.12
[51] later_1.3.0 munsell_0.5.0
[53] cellranger_1.1.0 tools_4.1.0
[55] xgboost_1.5.0.2 cli_3.1.0
[57] generics_0.1.1 broom_0.7.11
[59] evaluate_0.14 fastmap_1.1.0
[61] yaml_2.2.1 processx_3.5.2
[63] knitr_1.37 fs_1.5.2
[65] sparseMatrixStats_1.6.0 whisker_0.4
[67] R.oo_1.24.0 xml2_1.3.3
[69] compiler_4.1.0 rstudioapi_0.13
[71] beeswarm_0.4.0 reprex_2.0.1
[73] statmod_1.4.36 bslib_0.3.1
[75] stringi_1.7.6 highr_0.9
[77] ps_1.6.0 lattice_0.20-45
[79] bluster_1.4.0 Matrix_1.4-0
[81] vctrs_0.3.8 pillar_1.6.4
[83] lifecycle_1.0.1 rhdf5filters_1.6.0
[85] BiocManager_1.30.16 jquerylib_0.1.4
[87] BiocNeighbors_1.12.0 data.table_1.14.2
[89] bitops_1.0-7 irlba_2.3.5
[91] httpuv_1.6.5 R6_2.5.1
[93] bookdown_0.24 promises_1.2.0.1
[95] renv_0.15.0-14 gridExtra_2.3
[97] vipor_0.4.5 sessioninfo_1.2.2
[99] MASS_7.3-53.1 assertthat_0.2.1
[101] rhdf5_2.38.0 rprojroot_2.0.2
[103] withr_2.4.3 GenomeInfoDbData_1.2.7
[105] parallel_4.1.0 hms_1.1.1
[107] grid_4.1.0 beachmat_2.10.0
[109] rmarkdown_2.11 DelayedMatrixStats_1.16.0
[111] git2r_0.29.0 getPass_0.2-2
[113] pROC_1.18.0 lubridate_1.8.0
[115] ggbeeswarm_0.6.0