Abstract
Multi-study analysis adds value to microarray experiments. However, because of significant technical differences between microarray platforms, and because of differences in study design, it can be difficult to combine data. We have developed a statistical measure of reproducibility that can be applied to individual genes, measured in two different studies. This statistic, which we call the Integrative Correlation Coefficient or Correlation of Correlations, borrows strength across many genes to estimate the strength of the relationship between expression values in the two studies.
Disciplines
Bioinformatics | Computational Biology
Suggested Citation
Cope, Leslie M.; Garrett-Mayer, Liz; Gabrielson, Edward; and Parmigiani, Giovanni, "The Integrative Correlation Coefficient: a Measure of Cross-study Reproducibility for Gene Expressionea Array Data" (May 2007). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 152.
https://biostats.bepress.com/jhubiostat/paper152