DBNorm: normalizing high-density oligonucleotide microarray data based on distributions

BMC Bioinformatics

Software

Daniel Catchpoole¹  David Skillicorn²  Qinxue Meng³  Paul J. Kennedy³ 

[1] Children’s Cancer Research Unit, The Children’s Hospital at Westmead, 180 Hawkesbury Rd, 2145, Westmead, NSW, Australia;School of Computing, Queen’s University at Kingston, 99 University Ave, K7L3N6, Kingston, ON, Canada;School of Software, Faculty of Engineering and Information Technology and the Centre for Artificial Intelligence, University of Technology Sydney (UTS), PO Box 123, 15 Broadway, 2007, Ultimo, NSW, Australia;

关键词: Normalization;    Distribution;    Gene expression data;    R;   

DOI  :  10.1186/s12859-017-1912-5

 received in 2017-05-16, accepted in 2017-11-01,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundData from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable.ResultsThis paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required.ConclusionsThe performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

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