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Updated: Jun 11, 2026

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

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Published on: November 13, 2017

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Xi Wang^1,2, Qiwei Lian^1,2, Haoyu Dong¹

¹Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China.

Genomics, Proteomics & Bioinformatics

|July 25, 2024

Summary

Gene set scoring (GSS) tools designed for RNA sequencing data perform comparably on single-cell ATAC sequencing (scATAC-seq) data. Dropout imputation significantly improves GSS performance across most tools for scATAC-seq analysis.

Keywords:

Benchmark Gene set scoring Pathway analysis Single-cell ATAC-seq Single-cell RNA-seq

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Gene set scoring (GSS) is crucial for analyzing gene expression data (RNA sequencing) to understand cellular heterogeneity.
Single-cell ATAC sequencing (scATAC-seq) offers insights into gene regulation, but dedicated GSS tools are scarce.
The applicability of existing RNA sequencing GSS tools to scATAC-seq data requires thorough investigation.

Purpose of the Study:

To benchmark the performance of various GSS tools on scATAC-seq data.
To assess the suitability of RNA sequencing GSS tools for scATAC-seq analysis.
To provide guidelines for selecting appropriate GSS methods and preprocessing techniques for scATAC-seq data.

Main Methods:

Systematic benchmarking of ten GSS tools (four for bulk RNA-seq, five for scRNA-seq, one for scATAC-seq).
Evaluation using matched scATAC-seq and scRNA-seq datasets, and up to ten independent scATAC-seq datasets.
Analysis of the impact of gene activity conversion, dropout imputation, and gene set collections on GSS results.

Main Results:

GSS tools showed comparable performance on scATAC-seq and scRNA-seq data, indicating their potential applicability.
Dropout imputation significantly enhanced the performance of most GSS tools for scATAC-seq data.
The influence of gene activity conversion and gene set choice varied depending on the specific GSS tool and dataset.

Conclusions:

Existing GSS tools, particularly those for RNA sequencing, can be effectively applied to scATAC-seq data.
Dropout imputation is a critical preprocessing step for improving GSS performance in scATAC-seq analysis.
The study provides practical recommendations for optimizing GSS in scATAC-seq studies based on tool and dataset characteristics.