Abstract
The integrative correlation coefficient was developed to facilitate the validation of expression microarray results in public datasets, by identifying genes that are reproducibly measured across studies and even across microarray platforms. In the current study, we develop a number of interesting and important mathematical and statistical properties of the integrative correlation coefficient, including a unique permutation-based null distribution with the unusual property that the variance does not shrink as the sample size increases, discussing how these findings impact its use and interpretation, and what they have to say about any method for identifying reproducible genes in a meta-analysis.
Original language | English (US) |
---|---|
Pages (from-to) | 270-280 |
Number of pages | 11 |
Journal | Journal of Multivariate Analysis |
Volume | 123 |
DOIs | |
State | Published - Jan 2014 |
Keywords
- 62H20
- 62P10
- Bioinformatics
- Correlation
- Cross-study validation
- Gene expression
- Reproducibility
- Statistics
ASJC Scopus subject areas
- Statistics and Probability
- Numerical Analysis
- Statistics, Probability and Uncertainty