Background: Chromatin accessibility, measured via single-nucleus Assay for Transposase-Accessible Chromatin with sequencing (snATAC-seq), can reveal the underpinnings of transcriptional regulation across heterogeneous cell states. As the number and scale of snATAC-seq datasets increases, we need robust computational pipelines to integrate samples within a dataset and datasets across studies. These integration pipelines should correct cell-state-obfuscating technical effects while conserving underlying biological cell states, as has been shown for single-cell RNA-seq (scRNA-seq) pipelines. However, scRNA-seq integration methods have performed inconsistently on snATAC-seq datasets, potentially due to sparsity and genomic feature differences.
Results: Using single-nucleus multimodal datasets profiling ATAC and RNA simultaneously, we can measure snATAC-seq integration method performance by comparison to independently integrated snRNA-seq gold standard embeddings and annotations. Here, we benchmark 58 pipelines, incorporating 7 integration methods plus 1 embedding correction method with 5 feature sets. Using our command-line tool, we assessed 5 multimodal datasets at 3 different resolutions using 2 novel metrics to determine the best practices for multi-sample snATAC-seq integration. ATAC features outperformed Gene Activity Score (GAS) features, and embedding correction with Harmony was generally useful. SnapATAC2, PeakVI, and ArchR's iterative Latent Semantic Indexing (LSI) performed well.
Conclusions: We recommend SnapATAC2 + Harmony with pre-defined ENCODE candidate cis -regulatory element (cCRE) features as a first-pass pipeline given its metric performance, generalizability of features, and method resource-efficiency. This and other high-performing pipelines will guide future comprehensive gene regulation maps.