Authors:
ATL Lun, K Bach, JC Marioni
Journal name: 
Genome Biol
Citation info: 
17:75
Abstract: 
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.
DOI: 
http://doi.org/10.1186/s13059-016-0947-7
Research group: 
Marioni Group
E-pub date: 
27 Apr 2016
Users with this publication listed: 
Aaron Lun
Carlos Caldas
Evis Sala
Ferdia Gallagher
James Brenton
John Marioni
Jonghee Yoon
Luca Peruzzotti-Jametti
Mary McLean
Richard Gilbertson