Multiple functional linear model for association analysis of RNA-seq with imaging

Abstract

Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions (2DFPCA) for representing imaging data and develop a multiple functional linear model (MFLM) in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA-seq data of ovarian cancer and kidney renal clear cell carcinoma (KIRC) studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.

Graphical abstract

Keywords

imaging / RNA-seq / imaging genomics / functional principal component analysis / functional linear model

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ACKNOWLEDGEMENTS

The project described was supported by grants 1R01AR057120–01, 1R01HL106034-01 and 1R01 MH101054 from the National Institutes of Health, NHLBI and NIMH, respectively. The authors wish to acknowledge the contributions of the research institutions, study investigators, field staff and study participants in creating the TCGA datasets for biomedical research.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Junhai Jiang, Nan Lin, Shicheng Guo, Jinyun Chen and Momiao Xiong declare that they have no conflict of interest.

Junhai Jiang, Nan Lin, Shicheng Guo, Jinyun Chen and Momiao Xiong conformed to the Helsinki Declaration of 1875, as revised in 2000(5) concerning Human and Animal Rights, and that they followed out policy concerning Informed Consent as shown on Springer.com.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg