1 research outputs found
Mixture Envelope Model for Heterogeneous Genomics Data Analysis
Envelope model also known as multivariate regression model was proposed to
solve the multiple response regression problems. It measures the linear
association between predictors and multiple responses by using the minimal
reducing subspace of the covariance matrix that accommodates the mean function.
However, in many real applications, data may consist many unknown confounding
factors or they just come from different resources. Thus, there might be some
heterogeneous dependency across the whole population and divide them into
different groups. For example, there exists several subtypes across the
population with breast cancer with different gene interaction mechanisms for
each subtype group. In this setting, constructing a single model using all
observations ignores the difference between groups while estimating multiple
models for each group is infeasible due to the unknown group classification. To
deal with this problem, we proposed a mixture envelope model which construct a
groupwise model for heterogeneous data and simultaneously classify them into
different groups by an Imputation-Conditional Consistency (ICC) algorithm.
Simulation results shows that our proposed method outperforms on both
classification and prediction than some existing methods. Finally, we apply our
proposed method into breast cancer analysis to identify patients with
inflammatory breast cancer subtype and evaluate the associations between
micro-RNAs and message RNAs gene expression