scFastDE
Fast Donor-Weighted Pseudo-Bulk Differential Expression for scRNA-seq
Bioconductor version: 3.24 · Package version: 0.99.3
scFastDE provides fast, donor-weighted pseudo-bulk differential expression analysis for multi-donor single-cell RNA-seq experiments. Unlike existing tools that loop over genes serially, scFastDE uses vectorised sparse matrix operations across all genes simultaneously, achieving 10-50x speed gains on large datasets. Donors are weighted by the square root of their cell count, giving principled influence to well-represented donors without discarding donors with few cells. Paired experimental designs (same donors in multiple conditions) are automatically detected; pseudo-bulk is then aggregated per donor-condition pair and a blocking model accounts for inter-donor variation. A sparse pseudo-bulk guard automatically handles cell types where some donors fall below a minimum cell threshold. All functions operate natively on SingleCellExperiment objects.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("scFastDE") Details
| Maintainer | Subhadip Jana <subhadipjana1409@gmail.com> |
| Author | Subhadip Jana [aut, cre] (ORCID: <https://orcid.org/0009-0003-7860-2853>) |
| License | MIT + file LICENSE |
| URL | https://github.com/SubhadipJana1409/scFastDE |
| Bug Reports | https://github.com/SubhadipJana1409/scFastDE/issues |
| Downloads rank | 45 |
| Source branch | devel |
| biocViews | ATACSeq, CellBasedAssays, DifferentialExpression, GeneExpression, Sequencing, SingleCell, Software, StatisticalMethod, Transcriptomics, WorkflowStep |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | scFastDE_0.99.3.tar.gz |
| Windows binary (x86_64) | scFastDE_0.99.3.zip |
| macOS binary (arm64) | scFastDE_0.99.3.tgz |
| macOS binary (x86_64) | scFastDE_0.99.3.tgz |
Dependencies
Depends: R (>= 4.5.0)
Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, BiocParallel, Matrix, limma, methods, stats, ggplot2, rlang, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), scuttle, withr