Journal
GENOME BIOLOGY
Volume 17, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-016-0947-7
Keywords
Single-cell RNA-seq; Normalization; Differential expression
Funding
- Cancer Research UK [A17197]
- EMBL
- Cancer Research UK [22231] Funding Source: researchfish
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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.
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