Classifying cells with Scasat - a tool to analyse single-cell ATAC-seq

Abstract

Results: This paper presents ScAsAT (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. For the initial sequence processing steps, the pipeline uses a number of well-known tools which it executes from a python environment for each of the fastq files. While functions for the data analysis part are mostly written in R, it is robust, flexible, interactive and easy to extend. The pipeline was applied to a single-cell ATAC-seq dataset in order to identify different cell-types from a complex cell mixture. The results from Scasat showed that open chromatin location corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population.Availability: The jupyter notebook with the complete pipeline applied to the dataset published with this paper are publicly available on the Github (https://github.com/ManchesterBioinference/Scasat).An additional notebook is also provided for analysis of a publicly available dataset. The fastq files are submitted at ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-6116.Contact: syed.murtuzabaker@manchester.ac.uk and magnus.rattray@manchester.ac.uk Supplementary information: Supplementary data are available at bioRxiv online.

Chromatin accessibility profiling methods

“…A certain degree of data aggregation across cells or features is usually required. Specialized computational tools have been developed that address the sparsity and binary nature of scATAC-seq data and facilitate more integrated analyses across groups of cells 191,[233][234][235][236][237][238][239][240][241] . However, tools designed for scATAC-seq for specific analysis tasks, such as pseudo-time and trajectory inference, remain limited.…”

“…4a). However, due to the substantial scale and sparsity of the region by cell count matrix, specialized bioinformatics tools have been developed -mostly for scATAC-seq data -to handle these assay-specific challenges 191,[233][234][235][236][237][238][239][240][241][242] . One major point in which these tools differ is the way they define genomic regions to be used as features, either as peaks from bulk or aggregated single-cell data (chromVar 239 , Cicero 238 , cisTopic 191 , scABC 241 , Scasat 233 , MAESTRO 242 ), peaks from pseudo-bulk samples 56 or fixed-size bins 56 (SnapATAC 243 ).…”