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: |
0.3.4 |
| Imports: |
methods, Matrix, splines, ggplot2, reshape2 |
| Suggests: |
knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment, SeuratObject, Rtsne, cowplot, testthat, wrswoR |
| Published: |
2021-03-28 |
| 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 |
| Materials: |
NEWS |
| CRAN checks: |
singleCellHaystack results |
Documentation:
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