singleCellHaystack: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data

One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.

Version:	1.0.2
Imports:	methods, Matrix, splines, ggplot2, reshape2
Suggests:	knitr, rmarkdown, testthat, SummarizedExperiment, SingleCellExperiment, SeuratObject, cowplot, wrswoR, sparseMatrixStats, ComplexHeatmap, patchwork
Published:	2024-01-11
DOI:	10.32614/CRAN.package.singleCellHaystack
Author:	Alexis Vandenbon [aut, cre], Diego Diez [aut]
Maintainer:	Alexis Vandenbon <alexis.vandenbon at gmail.com>
BugReports:	https://github.com/alexisvdb/singleCellHaystack/issues
License:	MIT + file LICENSE
URL:	https://alexisvdb.github.io/singleCellHaystack/, https://github.com/alexisvdb/singleCellHaystack
NeedsCompilation:	no
Citation:	singleCellHaystack citation info
Materials:	NEWS
In views:	Omics
CRAN checks:	singleCellHaystack results

Documentation:

Reference manual:	singleCellHaystack.pdf
Vignettes:	Application on toy example

Downloads:

Package source:	singleCellHaystack_1.0.2.tar.gz
Windows binaries:	r-devel: singleCellHaystack_1.0.2.zip, r-release: singleCellHaystack_1.0.2.zip, r-oldrel: singleCellHaystack_1.0.2.zip
macOS binaries:	r-release (arm64): singleCellHaystack_1.0.2.tgz, r-oldrel (arm64): singleCellHaystack_1.0.2.tgz, r-release (x86_64): singleCellHaystack_1.0.2.tgz, r-oldrel (x86_64): singleCellHaystack_1.0.2.tgz
Old sources:	singleCellHaystack archive

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