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Fast analysis of scATAC-seq data using a predefined set of genomic regions version 2; peer review: 2 approved
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Fast analysis of scATAC-seq data using a predefined set of genomic regions version 2; peer review: 2 approved
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Journal Article
Fast analysis of scATAC-seq data using a predefined set of genomic regions version 2; peer review: 2 approved
2020
Overview
Background: Analysis of scATAC-seq data has been recently scaled to thousands of cells. While processing of other types of single cell data was boosted by the implementation of alignment-free techniques, pipelines available to process scATAC-seq data still require large computational resources. We propose here an approach based on pseudoalignment, which reduces the execution times and hardware needs at little cost for precision.
Methods: Public data for 10k PBMC were downloaded from 10x Genomics web site. Reads were aligned to various references derived from DNase I Hypersensitive Sites (DHS) using
kallisto and quantified with
bustools. We compared our results with the ones publicly available derived by
cellranger-atac. We subsequently tested our approach on scATAC-seq data for K562 cell line.
Results: We found that
kallisto does not introduce biases in quantification of known peaks; cells groups identified are consistent with the ones identified from standard method. We also found that cell identification is robust when analysis is performed using DHS-derived reference in place of
de novo identification of ATAC peaks. Lastly, we found that our approach is suitable for reliable quantification of gene activity based on scATAC-seq signal, thus allows for efficient labelling of cell groups based on marker genes.
Conclusions: Analysis of scATAC-seq data by means of
kallisto produces results in line with standard pipelines while being considerably faster; using a set of known DHS sites as reference does not affect the ability to characterize the cell populations.
Publisher
Faculty of 1000 Ltd,F1000 Research Limited,F1000 Research Ltd
