FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles. Issue 4 (19th November 2020)

• Record Type: Journal Article
• Authors: Sharma, Rachesh; Pandey, Neetesh; Mongia, Aanchal; Mishra, Shreya; Majumdar, Angshul; Kumar, Vibhor
• Is Part Of: NAR genomics and bioinformatics. Volume 2:Issue 4(2020)
• Publication Date: 2020-11-19
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqaa091
• Languages: English
• ISSNs: 2631-9268

Abstract

Abstract: 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/ .