Statistical analysis of big data on pharmacogenomics

Jianqing Fan, Han Liu

Research output: Contribution to journalReview articlepeer-review

30 Scopus citations

Abstract

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed.

Original languageEnglish (US)
Pages (from-to)987-1000
Number of pages14
JournalAdvanced Drug Delivery Reviews
Volume65
Issue number7
DOIs
StatePublished - Jun 30 2013

All Science Journal Classification (ASJC) codes

  • Pharmaceutical Science

Keywords

  • Approximate factor model
  • Big data
  • Graphical model
  • High dimensional statistics
  • Marginal screening
  • Multiple testing
  • Robust statistics
  • Variable selection

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