Assessment of computational methods for the analysis of single-cell ATAC-seq data

By Huidong Chen, Caleb A. Lareau, Tommaso Andreani, Michael E. Vinyard, Sara P. Garcia, Kendell Clement, Miguel Andrade, Jason D. Buenrostro, Luca Pinello. Genome Biology, 2019

305 citations recorded by the source corpus.

Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1–10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10–45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. We present a benchmarking framework that is applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were compared by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed. This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC is the only method able to analyze a large dataset (> 80,000 cells).

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dc9f0e12340139dbc18638c43fafd9441c2ad40e

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Citations in the corpus

Single-cell chromatin state analysis with Signac (2021, methodology): “To test how these results generalize to other datasets, we repeated a similar analysis using a series of synthetic scATAC-seq human bone marrow cell datasets generated in a recent benchmarking study 52 , with similar results (Supplementary Fig.”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, background): “While feature conversion may seem to be a straightforward solution, the inevitable information loss 19 can be detrimental.”
Best practices for single-cell analysis across modalities (2023, background): “Libraries with zero counts for all or most of the antibody panel should be removed; however, removing cells with a low total ADT count may remove cell types that do not express a specific set of proteins or express only a few 2 .”