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IMIX: A multivariate mixture model approach to integrative analysis of multiple types of omics data

View ORCID ProfileZiqiao Wang, View ORCID ProfilePeng Wei
doi: https://doi.org/10.1101/2020.06.23.167312
Ziqiao Wang
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
2The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
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Peng Wei
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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  • For correspondence: pwei2@mdanderson.org
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Abstract

Motivation Integrative genomic analysis is a powerful tool to study the biological mechanisms underlying a complex disease or trait across multiplatform high-dimensional data, such as DNA methylation, copy number variation (CNV), and gene expression. It is common to perform large-scale genome-wide association analysis of an outcome for each data type separately and combine the results ad hoc, leading to loss of statistical power and uncontrolled overall false discovery rate (FDR).

Results We propose a multivariate mixture model framework (IMIX) that integrates multiple types of genomic data and allows examining and relaxing the commonly adopted conditional independence assumption. We investigate across-data-type FDR control in IMIX, and show the gain in lower misclassification rates at controlled over-all FDR compared with established individual data type analysis strategies, such as Benjamini-Hochberg FDR control, the q-value, and the local FDR control by extensive simulations. IMIX features statistically-principled model selection, FDR control, and computational efficiency. Applications to the Cancer Genome Atlas (TCGA) data provide novel multi-omic insights into the luminal/basal subtyping of bladder cancer and the prognosis of pancreatic cancer.

Availability and implementation We have implemented our method in R package “IMIX” with instructions and examples available at https://github.com/ziqiaow/IMIX.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/ziqiaow/IMIX

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 24, 2020.
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IMIX: A multivariate mixture model approach to integrative analysis of multiple types of omics data
Ziqiao Wang, Peng Wei
bioRxiv 2020.06.23.167312; doi: https://doi.org/10.1101/2020.06.23.167312
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IMIX: A multivariate mixture model approach to integrative analysis of multiple types of omics data
Ziqiao Wang, Peng Wei
bioRxiv 2020.06.23.167312; doi: https://doi.org/10.1101/2020.06.23.167312

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