Learning single-cell chromatin accessibility profiles using meta-analytic marker genes

Brief Bioinform. 2023 Jan 19;24(1):bbac541. doi: 10.1093/bib/bbac541.

Abstract

Motivation: Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate.

Results: In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.

Keywords: benchmark; cell typing; deep learning; marker genes; meta analysis; motif analysis; scATAC-seq.

Publication types

  • Meta-Analysis

MeSH terms

  • Animals
  • Chromatin* / genetics
  • Epigenesis, Genetic*
  • Mice
  • Neural Networks, Computer
  • Regulatory Sequences, Nucleic Acid

Substances

  • Chromatin