singleCellHaystack-package: singleCellHaystack: Finding Needles (=differentially...

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

singleCellHaystack: Finding Needles (=differentially Expressed Genes) in Haystacks (=single Cell Data)

Description

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(s)

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

Authors:

• Diego Diez [e-mail] (ORCID)

singleCellHaystack documentation built on Dec. 28, 2022, 1:29 a.m.