Integrative analysis of multiple types of genomic data using an accelerated failure time frailty model

Shirong Deng, Jie Chen () and Huidong Shi
Additional contact information
Shirong Deng: Wuhan University
Jie Chen: Augusta University
Huidong Shi: Augusta University

Computational Statistics, 2021, vol. 36, issue 2, No 31, 1499-1532

Abstract: Abstract As the high throughput technologies rapidly develop, multiple types of genomic data become available within and across different studies. It has become a challenging task in modern statistical research to use all types of genomic data to infer some disease-prone genetic information. In this work, we propose an integrative analysis of multiple and different types of genomic data, clinical covariates and survival data under a framework of an accelerated failure time with frailty model. The proposed integrative approach aims to answer some aspects of the complex problem in genomic data analysis by finding relevant genomic features and inferring patients’ survival time using identified features. The proposed integrative approach is developed using a weighted least-squares with a sparse group LASSO penalty as the objective function to simultaneously estimate and select the relevant features. Extensive simulation studies are conducted to assess the performance of the proposed method with two types of genomic data, DNA methylation data and copy number variation data, on 600 genes and three clinical covariates. The simulation results show promises of the proposed method. The proposed method is applied to the analysis of the Cancer Genome Atlas data on Glioblastoma, a lethal brain cancer, and biologically interpretable results are obtained.

Keywords: Genomic data; High-dimensional data; Integrative analysis; Sparse group lasso; Accelerated failure time frailty model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-020-01060-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:36:y:2021:i:2:d:10.1007_s00180-020-01060-5

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-020-01060-5

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().