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 (bioRxiv, 2022) <doi:10.1101/2022.11.13.516355>.
| Version: |
1.0.0 |
| Imports: |
methods, Matrix, splines, ggplot2, reshape2 |
| Suggests: |
knitr, rmarkdown, testthat, SummarizedExperiment, SingleCellExperiment, SeuratObject, cowplot, wrswoR, sparseMatrixStats, ComplexHeatmap, patchwork |
| Published: |
2022-12-20 |
| 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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