Work

Statistical Methods for Integrating Multiple Types of High-Throughput Data

Year: 2009

Type: book-chapter

Abstract: Large-scale sequencing, copy number, mRNA, and protein data have given great promise to the biomedical research, while posing great challenges to data management and data analysis. Integrating different types of high-throughput data from diverse sources can increase the statistical power of data analysis and provide deeper biological understanding. This chapter uses two biomedical research examples to illustrate why there is an urgent need to develop reliable and robust methods for integrating the heterogeneous data. We then introduce and review some recently developed statistical methods for integrative analysis for both statistical inference and classification purposes. Finally, we present some useful public access databases and program code to facilitate the integrative analysis in practice.

Source: Methods in molecular biology

DOI: 10.1007/978-1-60761-580-4_19

Authors: Yang Xie, Chul Ahn

Institutions: Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: University of Michigan, National Institute on Drug Abuse, National Cancer Institute, National Center for Research Resources

Awards: G1991560713, G247409761, G3934381981, North and Central Texas Clinical and Translational Science Initiative (UL1), G995644284