singleCellHaystack-package: singleCellHaystack: A Universal Differential Expression...

In alexisvdb/singleCellHaystack: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data

singleCellHaystack: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data

Description

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41467-020-17900-3")} and our update Vandenbon and Diez (Scientific Reports, 2023) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41598-023-38965-2")}.

Author(s)

Maintainer: Alexis Vandenbon [e-mail] (ORCID)

Authors:

• Diego Diez [e-mail] (ORCID)

See Also

Useful links:

• https://alexisvdb.github.io/singleCellHaystack/

• https://github.com/alexisvdb/singleCellHaystack

• Report bugs at https://github.com/alexisvdb/singleCellHaystack/issues

alexisvdb/singleCellHaystack documentation built on Jan. 17, 2024, 10:45 a.m.