Abstract
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.
Highlights
Analysis of scATAC-seq data has been recently scaled to thousands of cells
Single cell ATAC-seq data Single cell ATAC-seq data for PBMC were downloaded from the 10x Genomics public datasets and include sequences for 10k PBMC from a healthy donor
Limitations of kallisto-based analysis At time of writing, kallisto does not natively support scATACseq analysis, though it can be applied to any scRNA-seq technology which supports cellular barcodes (CB) and unique molecular identifiers (UMI)
Summary
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. Reads were aligned to various references derived from DNase I Hypersensitive Sites (DHS) using kallisto and quantified with bustools. 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 found that cell identification is robust when analysis is performed using DHSderived reference in place of de novo identification of ATAC peaks. 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
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