  The linking genotype to phenotype is the fundamental aim of modern genetics.
We focus on study of links between gene expression data and phenotype data
through integrative analysis. We propose three approaches.
  1) The inherent complexity of phenotypes makes high-throughput phenotype
profiling a very difficult and laborious process. We propose a method of
automated multi-dimensional profiling which uses gene expression similarity.
Large-scale analysis show that our method can provide robust profiling that
reveals different phenotypic aspects of samples. This profiling technique is
also capable of interpolation and extrapolation beyond the phenotype
information given in training data. It can be used in many applications,
including facilitating experimental design and detecting confounding factors.
  2) Phenotype association analysis problems are complicated by small sample
size and high dimensionality. Consequently, phenotype-associated gene subsets
obtained from training data are very sensitive to selection of training
samples, and the constructed sample phenotype classifiers tend to have poor
generalization properties. To eliminate these obstacles, we propose a novel
approach that generates sequences of increasingly discriminative gene cluster
combinations. Our experiments on both simulated and real datasets show robust
and accurate classification performance.
  3) Many complex phenotypes, such as cancer, are the product of not only gene
expression, but also gene interaction. We propose an integrative approach to
find gene network modules that activate under different phenotype conditions.
Using our method, we discovered cancer subtype-specific network modules, as
well as the ways in which these modules coordinate. In particular, we detected
a breast-cancer specific tumor suppressor network module with a hub gene,
PDGFRL, which may play an important role in this module.
