Cargando…
FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles
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-chrom...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676476/ https://www.ncbi.nlm.nih.gov/pubmed/33575635 http://dx.doi.org/10.1093/nargab/lqaa091 |
_version_ | 1783611779907059712 |
---|---|
author | Sharma, Rachesh Pandey, Neetesh Mongia, Aanchal Mishra, Shreya Majumdar, Angshul Kumar, Vibhor |
author_facet | Sharma, Rachesh Pandey, Neetesh Mongia, Aanchal Mishra, Shreya Majumdar, Angshul Kumar, Vibhor |
author_sort | Sharma, Rachesh |
collection | PubMed |
description | 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/. |
format | Online Article Text |
id | pubmed-7676476 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-76764762021-02-10 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 NAR Genom Bioinform Standard Article 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/. Oxford University Press 2020-11-19 /pmc/articles/PMC7676476/ /pubmed/33575635 http://dx.doi.org/10.1093/nargab/lqaa091 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Standard Article Sharma, Rachesh Pandey, Neetesh Mongia, Aanchal Mishra, Shreya Majumdar, Angshul Kumar, Vibhor FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles |
title | FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles |
title_full | FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles |
title_fullStr | FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles |
title_full_unstemmed | FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles |
title_short | FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles |
title_sort | fits: forest of imputation trees for recovering true signals in single-cell open chromatin profiles |
topic | Standard Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676476/ https://www.ncbi.nlm.nih.gov/pubmed/33575635 http://dx.doi.org/10.1093/nargab/lqaa091 |
work_keys_str_mv | AT sharmarachesh fitsforestofimputationtreesforrecoveringtruesignalsinsinglecellopenchromatinprofiles AT pandeyneetesh fitsforestofimputationtreesforrecoveringtruesignalsinsinglecellopenchromatinprofiles AT mongiaaanchal fitsforestofimputationtreesforrecoveringtruesignalsinsinglecellopenchromatinprofiles AT mishrashreya fitsforestofimputationtreesforrecoveringtruesignalsinsinglecellopenchromatinprofiles AT majumdarangshul fitsforestofimputationtreesforrecoveringtruesignalsinsinglecellopenchromatinprofiles AT kumarvibhor fitsforestofimputationtreesforrecoveringtruesignalsinsinglecellopenchromatinprofiles |