Prior information-assisted integrative analysis of multiple datasets

Author:

Wang Feifei123,Liang Dongzuo24,Li Yang124ORCID,Ma Shuangge5ORCID

Affiliation:

1. Center for Applied Statistics, Renmin University of China , Beijing 100872, China

2. School of Statistics, Renmin University of China , Beijing 100872, China

3. Institute for Data Science in Health, Renmin University of China , Beijing 100872, China

4. RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China , Beijing 100872, China

5. Department of Biostatistics, Yale University , New Haven, CT 06520, United States

Abstract

Abstract Motivation Analyzing genetic data to identify markers and construct predictive models is of great interest in biomedical research. However, limited by cost and sample availability, genetic studies often suffer from the “small sample size, high dimensionality” problem. To tackle this problem, an integrative analysis that collectively analyzes multiple datasets with compatible designs is often conducted. For regularizing estimation and selecting relevant variables, penalization and other regularization techniques are routinely adopted. “Blindly” searching over a vast number of variables may not be efficient. Results We propose incorporating prior information to assist integrative analysis of multiple genetic datasets. To obtain accurate prior information, we adopt a convolutional neural network with an active learning strategy to label textual information from previous studies. Then the extracted prior information is incorporated using a group LASSO-based technique. We conducted a series of simulation studies that demonstrated the satisfactory performance of the proposed method. Finally, data on skin cutaneous melanoma are analyzed to establish practical utility. Availability and implementation Code is available at https://github.com/ldz7/PAIA. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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