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>.
|Author||Alexis Vandenbon [aut, cre] (<https://orcid.org/0000-0003-2180-5732>), Diego Diez [aut] (<https://orcid.org/0000-0002-2325-4893>)|
|Maintainer||Alexis Vandenbon <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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