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 language | English (US) |
|---|---|
| Pages (from-to) | 987-1000 |
| Number of pages | 14 |
| Journal | Advanced Drug Delivery Reviews |
| Volume | 65 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jun 30 2013 |
Funding
We thank Rongling Wu for his helpful comments and discussions. Jianqing Fan is supported by NSF Grant DMS-1206464 and NIH Grants R01GM100474 and R01-GM072611 . Han Liu is supported by NSF Grant III-1116730 and a NIH sub-award from Johns Hopkins University.
Keywords
- Approximate factor model
- Big data
- Graphical model
- High dimensional statistics
- Marginal screening
- Multiple testing
- Robust statistics
- Variable selection
ASJC Scopus subject areas
- Pharmaceutical Science