Single-cell ChIP-seq imputation with SIMPA by leveraging bulk ENCODE data

Steffen Albrecht, Tommaso Andreani, Miguel A. Andrade-Navarro, Jean-Fred Fontaine

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Feb 10, 2020
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Received Date: 21st January 20

Single-cell ChIP-seq analysis is challenging due to data sparsity. We present SIMPA (https://github.com/salbrec/SIMPA), a single-cell ChIP-seq data imputation method leveraging predictive information within bulk ENCODE data to impute missing protein-DNA interacting regions of target histone marks or transcription factors. Machine learning models trained for each single cell, each target, and each genomic region enable drastic improvement in cell types clustering and genes identification.

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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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