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

作者：Huidong Chen, Caleb A. Lareau, Tommaso Andreani, Michael E. Vinyard, Sara P. Garcia, Kendell Clement, Miguel Angel Andrade-Navarro, Jason Daniel Buenrostro, Luca Pinello · 发表于：Genome biology · 年份：2019 · DOI：10.1186/s13059-019-1854-5 · 被引用次数：413 · 研究领域：Single-cell and spatial transcriptomics、Genomic variations and chromosomal abnormalities、Genomics and Chromatin Dynamics

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the met...