singleCellHaystack: A Universal Differential Expression Prediction Tool forSingle-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: | 1.0.3 |
| Imports: | methods,Matrix, splines,ggplot2,reshape2 |
| Suggests: | knitr,rmarkdown,testthat,SummarizedExperiment,SingleCellExperiment,SeuratObject,cowplot,wrswoR,sparseMatrixStats,ComplexHeatmap,patchwork |
| Published: | 2025-12-04 |
| 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 + fileLICENSE |
| 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 |
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