ArticleNAR genomics and bioinformatics2020
FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- ePerturbDB: enhancer's experimental perturbation database.Database : the journal of biological databases and curation · 2026Article
- Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.PloS one · 2022Article
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Authors and funding
6 authors.
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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
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