singleCellHaystack: Finding Needles (=differentially Expressed Genes) in Haystacks (=single Cell Data)

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 (2019) <doi:10.1101/557967>.

Version: 0.3.2
Imports: splines, ggplot2, reshape2
Suggests: knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment, Seurat, Rtsne, cowplot, testthat
Published: 2020-07-01
Author: Alexis Vandenbon ORCID iD [aut, cre], Diego Diez ORCID iD [aut]
Maintainer: Alexis Vandenbon <alexis.vandenbon at>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: singleCellHaystack results


Reference manual: singleCellHaystack.pdf
Vignettes: Application on toy example
Package source: singleCellHaystack_0.3.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: singleCellHaystack_0.3.2.tgz, r-oldrel: singleCellHaystack_0.3.2.tgz


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