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 (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 [aut, cre], Diego Diez [aut]
Maintainer:	Alexis Vandenbon <alexis.vandenbon at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Citation:	singleCellHaystack citation info
CRAN checks:	singleCellHaystack results

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