Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility

Kawaguchi, Risa Karakida, Tang, Ziqi, Fischer, Stephan, Tripathy, Rohit, Koo, Peter, Gillis, Jesse (April 2021) Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility. bioRxiv. (Unpublished)

DOI: 10.1101/2021.04.01.438068


Background Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) measures genome-wide chromatin accessibility for the discovery of cell-type specific regulatory networks. ScATAC-seq combined with single-cell RNA sequencing (scRNA-seq) offers important avenues for ongoing research, such as novel cell-type specific activation of enhancer and transcription factor binding sites as well as chromatin changes specific to cell states. On the other hand, scATAC-seq data is known to be challenging to interpret due to its high number of zeros as well as the heterogeneity derived from different protocols. Because of the stochastic lack of marker gene activities, cell type identification by scATAC-seq remains difficult even at a cluster level. Results In this study, we exploit reference knowledge obtained from external scATAC-seq or scRNA-seq datasets to define existing cell types and uncover the genomic regions which drive cell-type specific gene regulation. To investigate the robustness of existing cell-typing methods, we collected 7 scATAC-seq datasets targeting mouse brain for a meta-analytic comparison of neuronal cell-type annotation, including a reference atlas generated by the BRAIN Initiative Cell Census Network (BICCN). By comparing the area under the receiver operating characteristics curves (AUROCs) for the three major cell types (inhibitory, excitatory, and non-neuronal cells), cell-typing performance by single markers is found to be highly variable even for known marker genes due to study-specific biases. How-ever, the signal aggregation of a large and redundant marker gene set, optimized via multiple scRNA-seq data, achieves the highest cell-typing performances among 5 existing marker gene sets, from the individual cell to cluster level. That gene set also shows a high consistency with the cluster-specific genes from inhibitory subtypes in two well-annotated datasets, suggesting applicability to rare cell types. Next, we demonstrate a comprehensive assessment of scATAC-seq cell typing using exhaustive combinations of the marker gene sets with supervised learning methods including machine learning classifiers and joint clustering methods. Our results show that the combinations using robust marker gene sets systematically ranked at the top, not only with model based prediction using a large reference data but also with a simple summation of expression strengths across markers. To demonstrate the utility of this robust cell typing approach, we trained a deep neural network to predict chromatin accessibility in each subtype using only DNA sequence. Through model interpretation methods, we identify key motifs enriched about robust gene sets for each neuronal subtype. Conclusions Through the meta-analytic evaluation of scATACseq cell-typing methods, we develop a novel method set to exploit the BICCN reference atlas. Our study strongly supports the value of robust marker gene selection as a feature selection tool and cross-dataset comparison between scATAC-seq datasets to improve alignment of scATAC-seq to known biology. With this novel, high quality epigenetic data, genomic analysis of regulatory regions can reveal sequence motifs that drive cell type-specific regulatory programs.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > Chromatin dynamics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > epigenetics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > epigenetics
bioinformatics > computational biology > algorithms > machine learning
CSHL Authors:
Communities: CSHL labs > Gillis Lab
CSHL labs > Koo Lab
School of Biological Sciences > Publications
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 3 April 2021
Date Deposited: 04 May 2021 18:02
Last Modified: 29 Feb 2024 18:14
URI: https://repository.cshl.edu/id/eprint/39955

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving