Identification of differentially expressed genes (DEGs) is a key step in single-cell transcriptomics data analysis. 'singleCellHaystack' predicts DEGs without relying on clustering of cells into arbitrary clusters. Single-cell RNA-seq (scRNA-seq) data is often processed to fewer dimensions using Principal Component Analysis (PCA) and represented in 2-dimensional plots (e.g. t-SNE or UMAP plots). 'singleCellHaystack' uses Kullback-Leibler divergence to find genes that are expressed in subsets of cells that are non-randomly positioned in a these multi-dimensional spaces or 2D representations. For the theoretical background of 'singleCellHaystack' we refer to Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3>.
Version: | 0.3.3 |
Imports: | methods, Matrix, splines, ggplot2, reshape2 |
Suggests: | knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment, Seurat, SeuratData, Rtsne, cowplot, testthat |
Published: | 2020-09-18 |
Author: | Alexis Vandenbon |
Maintainer: | Alexis Vandenbon <alexis.vandenbon at gmail.com> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Citation: | singleCellHaystack citation info |
CRAN checks: | singleCellHaystack results |
Reference manual: | singleCellHaystack.pdf |
Vignettes: |
Application on toy example |
Package source: | singleCellHaystack_0.3.3.tar.gz |
Windows binaries: | r-devel: singleCellHaystack_0.3.3.zip, r-release: singleCellHaystack_0.3.3.zip, r-oldrel: singleCellHaystack_0.3.3.zip |
macOS binaries: | r-release: singleCellHaystack_0.3.3.tgz, r-oldrel: singleCellHaystack_0.3.3.tgz |
Old sources: | singleCellHaystack archive |
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