  High-throughput microarray and sequencing technology have been used to
identify disease subtypes that could not be observed otherwise by using
clinical variables alone. The classical unsupervised clustering strategy
concerns primarily the identification of subpopulations that have similar
patterns in gene features. However, as the features corresponding to irrelevant
confounders (e.g. gender or age) may dominate the clustering process, the
resulting clusters may or may not capture clinically meaningful disease
subtypes. This gives rise to a fundamental problem: can we find a subtyping
procedure guided by a pre-specified disease outcome? Existing methods, such as
supervised clustering, apply a two-stage approach and depend on an arbitrary
number of selected features associated with outcome. In this paper, we propose
a unified latent generative model to perform outcome-guided disease subtyping
constructed from omics data, which improves the resulting subtypes concerning
the disease of interest. Feature selection is embedded in a regularization
regression. A modified EM algorithm is applied for numerical computation and
parameter estimation. The proposed method performs feature selection, latent
subtype characterization and outcome prediction simultaneously. To account for
possible outliers or violation of mixture Gaussian assumption, we incorporate
robust estimation using adaptive Huber or median-truncated loss function.
Extensive simulations and an application to complex lung diseases with
transcriptomic and clinical data demonstrate the ability of the proposed method
to identify clinically relevant disease subtypes and signature genes suitable
to explore toward precision medicine.
