Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.

ABSTRACT:

Motivation

Single-cell Chromatin ImmunoPrecipitation DNA-Sequencing (scChIP-seq) analysis is challenging due to data sparsity. High degree of sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from the ENCODE project to impute missing protein-DNA interacting regions of target histone marks or transcription factors.

Results

Imputations using machine learning models trained for each single cell, each ChIP protein target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real human data. R

SUBMITTER: Albrecht S 

PROVIDER: S-EPMC9249201 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature