Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.08.536108v1?rss=1
Authors: Guo, J., Li, J., Huang, F., Chen, J., Shen, L.
Abstract:
The development of single-cell assay for transposase-accessible chromatin using sequencing data (scATAC-seq) has allowed the characterization of epigenetic heterogeneity at single-cell resolution. However, the sparse and noisy nature of scATAC-seq data poses unique computational challenges. To address this, we introduce scART, a novel bioinformatics tool specifically designed for scATAC-seq data analysis. scART utilizes analytical methods highly stable for processing sparse and noisy data, such as k-nearest neighbor (KNN) imputation, Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme, and the cosine similarity metric to identify underlying cellular heterogeneity in scATAC-seq data. It accurately and robustly identifies cell identities, particularly in data with low sequencing depth, and constructs the trajectory of cellular states. As a demonstration of its utility, scART successfully reconstructed the development trajectory of the embryonic mouse forebrain and uncovered the dynamics of layer-specific neurogenesis. scART is available at GitHub.
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