Chromatin-accessibility estimation from single-cell ATAC data with scOpen

We propose a computational method for quantifying the open chromatin status of regulatory regions from single cell ATAC-seq experiments. scOpen, which is based on positive-unlabeled learning of matrices, is able to estimate the probability that a region is open at a given cell mitigating the sparsity of scATAC-seq matrices. We demonstrate that scOpen improves all down-stream analysis steps of scATAC-seq data as clustering, visualization, detection of transcription factors and chromatin conformation in several scATAC-seq data. We used scATAC product from 10x Genomics to profile UUO mouse kidney at Day 0, 2 and 10. We generated Runx1 overexpressing RNA-seq data from human kidney fibroblasts.