Analysis of C133_Neeland CITE-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 8 individuals with cystic fibrosis (CF). The samples were run on the 10X Chromium and sequenced by the Cellular Genomics Project Team at the WEHI Advanced Genomics Facility. After sequencing, expression was quantified by aligning, filtering, barcode counting, and counting the number of UMIs mapped to each gene using CellRanger v5.0.0. Version 2020-A (July 7, 2020) of the CellRanger reference files was used, which maps against the GRCh38 of the reference genome and quantifies expression using gene models from GENCODE v32/Ensembl 98.
View the CellRanger overall, read depth normalised and capture-specific web summaries: C133_1, C133_2.
View the MultiQC report.
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/C133_Neeland.CellRanger.SCE.rds"))
dim(sce)[1] 36601 13589760# Preparing HTO data -----------------------------------------------------------
is_hto <- rownames(altExp(sce, "Antibody Capture")) %in%
paste0("Human_HTO_", 1:8)
altExp(sce, "HTO") <- altExp(sce, "Antibody Capture")[is_hto, ]
altExp(sce, "ADT") <- altExp(sce, "Antibody Capture")[!is_hto, ]
altExp(sce, "Antibody Capture") <- NULL
expSum <- colSums(counts(sce))
htoSum <- colSums(counts(altExp(sce, "HTO")))
adtSum <- colSums(counts(altExp(sce, "ADT")))
dat <- data.frame(exp = expSum, hto = htoSum, adt = adtSum)3 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 identified using `emptyDrops()` from **DropletUtils**.")| x | |
|---|---|
| C133_1 | 11900 |
| C133_2 | 12928 |
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)$knee, col = "dodgerblue", lty = 2)
})[[1]]
NULL
[[2]]
NULL
| Version | Author | Date |
|---|---|---|
| f3b7b92 | Jovana Maksimovic | 2022-06-16 |
4 Remove empty droplets
Remove empty droplets.
sce <- sce[, which(empties$FDR <= 0.001)]
sceclass: SingleCellExperiment
dim: 36601 24828
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(3): ID Symbol Type
colnames(24828): 1_AAACCCACACTTCCTG-1 1_AAACCCACAGACAAAT-1 ...
2_TTTGTTGTCATTGGTG-1 2_TTTGTTGTCGATGGAG-1
colData names(3): Sample Barcode capture
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT5 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/experiment2_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/03_C133_Neeland.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 S4Vectors * 0.32.3 2021-11-21 [?] Bioconductor
P sass 0.4.0 2021-05-12 [?] CRAN (R 4.1.0)
P ScaledMatrix 1.2.0 2021-10-26 [?] Bioconductor
P scales 1.1.1 2020-05-11 [?] CRAN (R 4.0.2)
P scater * 1.22.0 2021-10-26 [?] Bioconductor
P scDblFinder * 1.8.0 2021-10-26 [?] Bioconductor
P scds * 1.10.0 2021-10-26 [?] Bioconductor
P scran * 1.22.1 2021-11-14 [?] Bioconductor
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