RESUMEN
The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA, cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as forest of imputation trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes multiple imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. Besides visualization and classification, FITs-based imputation also improved accuracy in the detection of enhancers, calculating pathway enrichment score and prediction of chromatin-interactions. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples. The software is freely available at https://reggenlab.github.io/FITs/.