Kitty Murphy, Alexi Nott, and Sarah Marzi
Cell type identity is a major driver of epigenetic variation, making biological interpretation of bulk tissue epigenomes difficult. Here we present CHAS (cell type-specific histone acetylation score), an R package for inferring cell type-specific signatures in bulk brain H3K27ac profiles. CHAS annotates peaks identified in bulk brain studies of H3K27ac to cell type-specific signals in four major brain cell types, and based on signal intensities generates cell type-specific histone acetylation scores to act as a proxy for cell type proportion. CHAS was successfully validated in pseudo-bulk samples of known cell type proportions and applied to three brain disorder epigenome-wide association studies conducted on bulk brain tissue.
If you use CHAS, please cite our preprint: Murphy, Nott & Marzi. CHAS, a deconvolution tool, infers cell type-specific signatures in bulk brain histone acetylation studies of brain disorders. bioRxiv, 2021.
See the CHAS vignette website for up-to-date instructions on usage.
CHAS annotates peaks identified in bulk tissue studies of H3K27ac to their cell type-specific signals by overlapping the bulk peaks with cell sorted H3K27ac peaks and identifying which of the bulk peaks are specific to a given cell type. For a bulk peak to be defined as cell type-specific two criteria must be met: (i) the bulk peak is annotated only to a single cell type; (ii) the bulk peak overlaps a predefined percentage of that cell type’s peak.
Using a counts per million matrix and the cell type-specific bulk H3K27ac peaks identified in step 1 of the workflow, CHAS generates scores by averaging the normalised signal intensity of a sample across all peaks specific to a given cell type, thereby deriving a proxy of the proportion of that cell type in the given bulk sample. As a constraint from peak-normalisation, the maximum signal intensity for any given peak and sample is 1 and the resulting score will lie between 0 and 1 for a given sample and cell type.
- If you use CHAS, please cite our preprint: Murphy, Nott & Marzi. CHAS, a deconvolution tool, infers cell type-specific signatures in bulk brain histone acetylation studies of brain disorders. bioRxiv, 2021.
- If you use the cell sorted H3K27ac data associated with this package then please cite the following paper: Nott, et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science, 2019.
- If you use the entorhinal cortex peaks and/or counts available within this package then please cite the following paper: Marzi, et al. A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex. Nature Neuroscience, 2018.
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("neurogenomics/CHAS")
You can then load the package and data package:
library(CHAS)
This project is licensed under the terms of the GNU General Public License v3.0.