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FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles.
Sharma, Rachesh; Pandey, Neetesh; Mongia, Aanchal; Mishra, Shreya; Majumdar, Angshul; Kumar, Vibhor.
Afiliação
  • Sharma R; Department of Electronic and Communication Engineering, Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase-III, New Delhi 110020, India.
  • Pandey N; Department of Computational Biology, Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase-III, New Delhi 110020, India.
  • Mongia A; Department of Electronic and Communication Engineering, Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase-III, New Delhi 110020, India.
  • Mishra S; Department of Computational Biology, Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase-III, New Delhi 110020, India.
  • Majumdar A; Department of Electronic and Communication Engineering, Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase-III, New Delhi 110020, India.
  • Kumar V; Department of Computational Biology, Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase-III, New Delhi 110020, India.
NAR Genom Bioinform ; 2(4): lqaa091, 2020 Dec.
Article em En | MEDLINE | ID: mdl-33575635
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/.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: NAR Genom Bioinform Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Índia

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: NAR Genom Bioinform Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Índia