scOpen: chromatin-accessibility estimation of single-cell ATAC data

We propose scOpen, a computational method for quantifying the open chromatin status of regulatory regions from single cell ATAC-seq (scATAC-seq) experiments. scOpen is based on positive-unlabelled learning of matrices and estimates the probability that a region is open at a given cell by mitigating the sparsity of scATAC-seq matrices. We demonstrate that scOpen improves all down-stream analysis steps of scATAC-seq data as clustering, visualisation and chromatin conformation. Moreover, we show the power of scOpen and single cell-based footprinting analysis (scHINT) to dissect regulatory changes in the development of fibrosis in the kidney.