Comprehensive analysis of single cell ATAC-seq data with SnapATAC

By Rongxin Fang, Sebastian Preissl, Y. Li, Xiaomeng Hou, J. Lucero, Xinxin Wang, Amir Motamedi, A. Shiau, Xinzhu Zhou, Fangming Xie and 6 more. Nature Communications, 2021

345 citations recorded by the source corpus.

Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators. Single cell analysis of transposase-accessible chromatin is deepening our understanding on the origins of cellular diversity, yet methods are limited by data sparsity. Here, the authors introduce SnapATAC, a pipeline to resolve cellular heterogeneity and reveal candidate regulatory elements across different cell populations.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7f677c0247b99fa94741491775ab04fdbb9a7b02

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Citations in the corpus

Single-cell chromatin state analysis with Signac (2021, methodology): “This was defined as the fraction of k neighbors in the reduced-dimension space (LSI, cisTopic, SCALE or SnapATAC) for each cell i that belonged to the same annotated cell type as cell i , where cell types were predicted as described above using the gene expression assay.”
Best practices for single-cell analysis across modalities (2023, methodology): “Although advancements in experimental assays allow for the paired measurements of many modality combinations 246 , different modalities are still commonly measured independently, resulting in unpaired data 247 .”