Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.
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.
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, CB2 0RE, Cambridge, UK. aaron.lun@cruk.cam.ac.uk.
Aaron T L
AT
Lun
EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK.
Karsten
K
Bach
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, CB2 0RE, Cambridge, UK. marioni@ebi.ac.uk.|EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK. marioni@ebi.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, Cambridge, UK. marioni@ebi.ac.uk.
John C
JC
Marioni
D000465:Algorithms; D000818:Animals; D002138:Calibration; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D006801:Humans; D017423:Sequence Analysis, RNA / Q000379:methods* / Q000592:standards; D059629:Signal-To-Noise Ratio; D059010:Single-Cell Analysis / Q000379:methods* / Q000592:standards
Differential expression; Normalization; Single-cell RNA-seq