quantro: a data-driven approach to guide the choice of an appropriate normalization method.

Abstract:

:Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html .

journal_name

Genome Biol

journal_title

Genome biology

authors

Hicks SC,Irizarry RA

doi

10.1186/s13059-015-0679-0

subject

Has Abstract

pub_date

2015-06-04 00:00:00

pages

117

eissn

1474-7596

issn

1474-760X

pii

10.1186/s13059-015-0679-0

journal_volume

16

pub_type

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