EpiCHAOS is a metric for calculating cell-to-cell heterogeneity in single-cell epigenomics data. EpiCHAOS can be applied to any single cell epigenomics dataset (e.g. scATAC-seq, scChIP-seq, scNMT-seq, scTAM-seq) in binarised matrix form.
EpiCHAOS scores are assigned per cluster (or otherwise defined group of single cells, e.g. cell type, treatment condition). Scores will range from 0-1 where higher scores indicate higher cell-to-cell heterogeneity.
For details please refer to the epiCHAOS preprint: https://www.biorxiv.org/content/10.1101/2024.04.24.590899v1. "analysis-scripts" contains R scripts used for all analyses related to this publication.
Calculation of epiCHAOS scores requires (i) a single cell epigenomics dataset in binarised matrix form, e.g. a peaks-by-cells or tiles-by-cells matrix in the case of scATAC-seq data, (ii) cell annotation matching column names to clusters, cell types or other groups on which heterogenetiy scores should be computed.
R functions for computing epiCHAOS scores can be found in "epiCHAOS-functions/compute_epiCHAOS". The main function here is "epiCHAOS()" which takes a counts matrix and cell annotation metadata as input and returns a dataframe of epiCHAOS scores for each group/cluster.
- Single-cell matrix is binarised if not already
- Pairwise chance-corrected Jaccard indices are calculated between all cells in each group/cluster
- The mean of all pairwise indices is computed per cluster as a raw heterogeneity score
- Raw heterogeneity scores are adjusted for differences in total counts between groups by fitting a linear regression model and taking the residuals of the model as an adjusted score
- Scores are normalised to a range of 0-1 and subtracted from one so that a higher score indicates higher cell-to-cell heterogeneity
