BioMed Central, Genome Biology, 1(17), 2016
DOI: 10.1186/s13059-016-0947-7
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This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by BioMed Central. ; Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses. ; All authors were supported by core funding from Cancer Research UK (code: SW73).