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Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.

Published version
Peer-reviewed

Repository DOI


Change log

Authors

Lun, Aaron TL 
Marioni, John C 

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.

Description

Keywords

Differential expression, Normalization, Single-cell RNA-seq, Algorithms, Animals, Calibration, Gene Expression Profiling, Humans, Sequence Analysis, RNA, Signal-To-Noise Ratio, Single-Cell Analysis

Journal Title

Genome Biol

Conference Name

Journal ISSN

1474-7596
1474-760X

Volume Title

17

Publisher

Springer Science and Business Media LLC
Sponsorship
Cancer Research UK (22231)
All authors were supported by core funding from Cancer Research UK (code: SW73).