Promoting Similarity of Sparsity Structures in Integrative Analysis With Penalization

Yuan Huang; Qingzhao Zhang; Sanguo Zhang; Jian Huang; Shuangge Ma

doi:10.1080/01621459.2016.1139497

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Promoting Similarity of Sparsity Structures in Integrative Analysis With Penalization

Journal article

Open access

Promoting Similarity of Sparsity Structures in Integrative Analysis With Penalization

Yuan Huang, Qingzhao Zhang, Sanguo Zhang, Jian Huang and Shuangge Ma

Journal of the American Statistical Association, Vol.112(517), pp.342-350

01/02/2017

DOI: 10.1080/01621459.2016.1139497

PMCID: PMC6086364

PMID: 30100648

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https://figshare.com/articles/dataset/Promoting_Similarity_of_Sparsity_Structures_in_Integrative_Analysis_with_Penalization/2069553View

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Abstract

For data with high-dimensional covariates but small sample sizes, the analysis of single datasets often generates unsatisfactory results. The integrative analysis of multiple independent datasets provides an effective way of pooling information and outperforms single-dataset and several alternative multi-datasets methods. Under many scenarios, multiple datasets are expected to share common important covariates, that is, the corresponding models have similarity in their sparsity structures. However, the existing methods do not have a mechanism to promote the similarity in sparsity structures in integrative analysis. In this study, we consider penalized variable selection and estimation in integrative analysis. We develop an L 0 -penalty-based method, which explicitly promotes the similarity in sparsity structures. Computationally it is realized using a coordinate descent algorithm. Theoretically it has the selection and estimation consistency properties. Under a wide spectrum of simulation scenarios, it has identification and estimation performance comparable to or better than the alternatives. In the analysis of three lung cancer datasets with gene expression measurements, it identifies genes with sound biological implications and satisfactory prediction performance. Supplementary materials for this article are available online.

Cancer genomic data

Integrative analysis

penalization

Sparsity structure

Variable selection

Details

Title: Subtitle: Promoting Similarity of Sparsity Structures in Integrative Analysis With Penalization
Creators: Yuan Huang - Yale University
Qingzhao Zhang - Yale University
Sanguo Zhang - Chinese Academy of Sciences
Jian Huang - University of Iowa
Shuangge Ma - Yale University
Resource Type: Journal article
Publication Details: Journal of the American Statistical Association, Vol.112(517), pp.342-350
DOI: 10.1080/01621459.2016.1139497
PMID: 30100648
PMCID: PMC6086364
NLM abbreviation: J Am Stat Assoc
ISSN: 0162-1459
eISSN: 1537-274X
Publisher: Taylor & Francis
Language: English
Date published: 01/02/2017
Academic Unit: Statistics and Actuarial Science; Biostatistics
Record Identifier: 9984257609502771

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