ICE is a statistical test for correcting for expression heterogeneity inherent in expression dataset due to confounding from unmodeled factors. ICE directly incorporates inter-sample correlation structure as signatures of the systematic confounding effects. The full details are described in our paper Accurate discovery of expression quantitative trait loci in the presence of spurious and genuine regulatory hostposts, Genetics 180(4):1909-25, 2008.

The R implementation of ICE is available. Please refer to the Documentation link for further information. A faster C implementation will be available soon. If you have you any further question, please contact Hyun Min Kang. This is the joint work with Chun Ye and Eleazar Eskin.

This web-site is based upon work supported by the National Science Foundation under Grant No. 0513599 and 0729049. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.