Seq-ing improved gene expression estimates from microarrays using machine learning

BMC Bioinformatics

Paul K. Korir³  Paul Geeleher²  Cathal Seoighe¹ 

[1] Institute of Infectious Disease and Molecular Medicine, Anzio Road, Cape Town 7925, South Africa

[2] Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago IL-60637, USA

[3] School of Biochemistry and Cell Biology, University College Cork, Western Road, Cork, Ireland

关键词: Statistical learning;    Machine learning;    Microarray;    RNA-Seq;   

Others  :  1229466
DOI  :  10.1186/s12859-015-0712-z

 received in 2015-05-05, accepted in 2015-08-19,  发布年份 2015

【 摘 要 】

Background

Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use, demonstrated by the ever-growing numbers of samples deposited in public repositories.

Results

We propose a novel approach to microarray analysis that attains many of the advantages of RNA-Seq. This method, called Machine Learning of Transcript Expression (MaLTE), leverages samples for which both microarray and RNA-Seq data are available, using a Random Forest to learn the relationship between the fluorescence intensity of sets of microarray probes and RNA-Seq transcript expression estimates. We trained MaLTE on data from the Genotype-Tissue Expression (GTEx) project, consisting of Affymetrix gene arrays and RNA-Seq from over 700 samples across a broad range of human tissues.

Conclusion

This approach can be used to accurately estimate absolute expression levels from microarray data, at both gene and transcript level, which has not previously been possible. This methodology will facilitate re-analysis of archived microarray data and broaden the utility of the vast quantities of data still being generated.

【 授权许可】

   
2015 Korir et al.

【 图 表 】

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