Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq - Cui, Zhe; Cui, Ya; Gao, Yan; Jiang, Tao; Zang, Tianyi; Wang, Yadong (Frontiers in Genetics, 2021)
Single-cell Assay Transposase Accessible Chromatin sequencing (scATAC-seq) has been wildly used in profiling genome-wide chromatin accessibility in thousands of individual cells. However, comparing with single-cell RNA-seq, the peaks of scATAC-seq are much sparser due to the lower copy numbers (diploid in humans) and the inherent missing signals, which makes it more challenging to classify cell type based on specific expressed gene or other canonical markers. Here, we present svmATAC, a support vector machine (SVM)-based method for accurately identifying cell types in scATAC-seq datasets by enhancing peaks signal strength and imputing signals through patterns of co-accessibility. We applied svmATAC to several scATAC-seq data from human immune cells, human hematopoietic system cells and peripheral blood mononuclear cells. The benchmark results showed that svmATAC is free of literature-based marker and robust across datasets in different libraries and platforms. svmATAC and its manual are freely available online at https://github.com/mrcuizhe/svmATAC.