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) .


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install.packages("singleCellHaystack")

0.3.3 by Alexis Vandenbon, a month ago


Browse source code at https://github.com/cran/singleCellHaystack


Authors: Alexis Vandenbon [aut, cre] , Diego Diez [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports methods, Matrix, splines, ggplot2, reshape2

Suggests knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment, Seurat, SeuratData, Rtsne, cowplot, testthat


See at CRAN