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bioRxiv · 10.1101/2020.08.12.243121

FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles

Sharma, R.·Pandey, N.·Mongia, A.·Mishra, S.·Majumdar, A.·KUMAR, V.
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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 a forest of 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. 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. First made online as thesis work at https://repository.iiitd.edu.in/xmlui/handle/123456789/807

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Sharma, R., Pandey, N., Mongia, A., Mishra, S., Majumdar, A., KUMAR, V.. 2020-08-12. FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles. https://doi.org/10.1101/2020.08.12.243121

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