6533b7d7fe1ef96bd1269508

RESEARCH PRODUCT

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

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Abstract 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.