- Dependencies
- Data
- Create objects
- Predict doublets
- Data subset
Doublet prediction using {scDblFinder}
2025-03-05
Last updated: 2025-03-05
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Knit directory: muse/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0f01531 | Dave Tang | 2025-03-05 | Predicting doublets using scDblFinder |
The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq.
Dependencies
Install Bioconductor packages using
BiocManager::install().
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("scDblFinder")
BiocManager::install("TENxIO")Load libraries.
suppressPackageStartupMessages(library(scDblFinder))
suppressPackageStartupMessages(library(SingleCellExperiment))
suppressPackageStartupMessages(library(TENxIO))Data
Download data.
filtered_h5_url <- 'https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_v3/pbmc_1k_v3_filtered_feature_bc_matrix.h5'
filtered_h5 <- paste0('data/', basename(filtered_h5_url))
download_file <- function(url, outfile){
fn <- basename(url)
if(file.exists(outfile) == FALSE){
download.file(url, destfile = outfile)
} else {
message(paste0(outfile, " already exists"))
}
}
download_file(filtered_h5_url, filtered_h5)data/pbmc_1k_v3_filtered_feature_bc_matrix.h5 already existsCreate objects
Create SingleCellExperiment files.
create_sce_obj <- function(h5){
con <- TENxH5(h5)
import(con)
}
pbmc1k <- create_sce_obj(filtered_h5)Warning in rhdf5::h5read(file, name = paste0(group, ranges), index = list(1L),
: Object 'matrix/features/interval' does not exist in this HDF5 file.pbmc1kclass: SingleCellExperiment
dim: 33538 1222
metadata(1): TENxFile
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
ENSG00000268674
rowData names(3): ID Symbol Type
colnames(1222): AAACCCAAGGAGAGTA-1 AAACGCTTCAGCCCAG-1 ...
TTTGGTTGTAGAATAC-1 TTTGTTGCAATTAGGA-1
colData names(0):
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):Predict doublets
Run scDblFinder().
pbmc1k.pred <- scDblFinder(pbmc1k)Creating ~1500 artificial doublets...Dimensional reductionEvaluating kNN...Training model...iter=0, 155 cells excluded from training.iter=1, 142 cells excluded from training.iter=2, 135 cells excluded from training.Threshold found:0.29426 (2.1%) doublets calledcolData(pbmc1k.pred) |>
as.data.frame() -> pbmc1k.pred
head(pbmc1k.pred) scDblFinder.class scDblFinder.score scDblFinder.weighted
AAACCCAAGGAGAGTA-1 singlet 0.0169715248 0.40543264
AAACGCTTCAGCCCAG-1 singlet 0.0008992357 0.29487536
AAAGAACAGACGACTG-1 singlet 0.0003129665 0.21450453
AAAGAACCAATGGCAG-1 singlet 0.0005328555 0.22582183
AAAGAACGTCTGCAAT-1 singlet 0.0127416188 0.37730614
AAAGGATAGTAGACAT-1 singlet 0.0020924010 0.07760762
scDblFinder.cxds_score
AAACCCAAGGAGAGTA-1 0.006469792
AAACGCTTCAGCCCAG-1 0.049250538
AAAGAACAGACGACTG-1 0.074387442
AAAGAACCAATGGCAG-1 0.050214668
AAAGAACGTCTGCAAT-1 0.103131556
AAAGGATAGTAGACAT-1 0.075818423Plot scores per label.
boxplot(scDblFinder.score ~ scDblFinder.class, data = pbmc1k.pred, pch = 16)
Data subset
Since doublets are artifically generated using the available data, providing a different input should affect the scoring and prediction.
set.seed(1984)
n <- ceiling(ncol(pbmc1k)*0.9)
bcs <- sample(x = colnames(pbmc1k), size = n)
pbmc1k_subset <- pbmc1k[, bcs]
pbmc1k_subsetclass: SingleCellExperiment
dim: 33538 1100
metadata(1): TENxFile
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
ENSG00000268674
rowData names(3): ID Symbol Type
colnames(1100): AGGACGAAGACCTTTG-1 TTTATGCGTTGATCGT-1 ...
GTCACTCTCTTGAACG-1 TTCAGGAGTCTACAAC-1
colData names(0):
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):Predict doublets on the data subset.
pbmc1k_subset.pred <- scDblFinder(pbmc1k_subset)Creating ~1500 artificial doublets...Dimensional reductionEvaluating kNN...Training model...iter=0, 136 cells excluded from training.iter=1, 120 cells excluded from training.iter=2, 118 cells excluded from training.Threshold found:0.4633 (3%) doublets calledcolData(pbmc1k_subset.pred) |>
as.data.frame() -> pbmc1k_subset.pred
idx <- match(
row.names(pbmc1k_subset.pred),
row.names(pbmc1k.pred)
)
x <- pbmc1k.pred[idx, ]$scDblFinder.score
y <- pbmc1k_subset.pred$scDblFinder.score
plot(x, y, pch = 16, xlab = 'Full dataset', ylab = 'Subset')
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
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
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] TENxIO_1.8.2 scDblFinder_1.20.2
[3] SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0
[5] Biobase_2.66.0 GenomicRanges_1.58.0
[7] GenomeInfoDb_1.42.3 IRanges_2.40.1
[9] S4Vectors_0.44.0 BiocGenerics_0.52.0
[11] MatrixGenerics_1.18.1 matrixStats_1.4.1
[13] workflowr_1.7.1
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[13] S4Arrays_1.6.0 BiocBaseUtils_1.8.0 curl_5.2.3
[16] BiocNeighbors_2.0.1 Rhdf5lib_1.28.0 xgboost_1.7.8.1
[19] SparseArray_1.6.1 rhdf5_2.50.2 sass_0.4.9
[22] bslib_0.8.0 cachem_1.1.0 GenomicAlignments_1.42.0
[25] whisker_0.4.1 igraph_2.1.1 lifecycle_1.0.4
[28] pkgconfig_2.0.3 rsvd_1.0.5 Matrix_1.7-0
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