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 Bioljournal_title
Genome biologyauthors
Hicks SC,Irizarry RAdoi
10.1186/s13059-015-0679-0subject
Has Abstractpub_date
2015-06-04 00:00:00pages
117eissn
1474-7596issn
1474-760Xpii
10.1186/s13059-015-0679-0journal_volume
16pub_type
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