Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression

Lei Ding; Daniel J. McDonald

doi:10.1093/bioinformatics/btx265

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arXiv · 1907.05927

Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression

Lei Ding·Daniel J. McDonald

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Abstract

Motivation: The discovery of relationships between gene expression measurements and phenotypic responses is hampered by both computational and statistical impediments. Conventional statistical methods are less than ideal because they either fail to select relevant genes, predict poorly, ignore the unknown interaction structure between genes, or are computationally intractable. Thus, the creation of new methods which can handle many expression measurements on relatively small numbers of patients while also uncovering gene-gene relationships and predicting well is desirable. Results: We develop a new technique for using the marginal relationship between gene expression measurements and patient survival outcomes to identify a small subset of genes which appear highly relevant for predicting survival, produce a low-dimensional embedding based on this small subset, and amplify this embedding with information from the remaining genes. We motivate our methodology by using gene expression measurements to predict survival time for patients with diffuse large B-cell lymphoma, illustrate the behavior of our methodology on carefully constructed synthetic examples, and test it on a number of other gene expression datasets. Our technique is computationally tractable, generally outperforms other methods, is extensible to other phenotypes, and also identifies different genes (relative to existing methods) for possible future study. Key words: regression; principal components; matrix sketching; preconditioning Availability: All of the code and data are available at https://github.com/dajmcdon/aimer/.

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Lei Ding, Daniel J. McDonald. 2019-07-12. Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression. https://doi.org/10.1093/bioinformatics%2Fbtx265

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