  In recent years, a large number of genomic and epigenomic studies have been
focusing on the integrative analysis of multiple experimental datasets measured
over a large number of observational units. The objectives of such studies
include not only inferring a hidden state of activity for each unit over
individual experiments, but also detecting highly associated clusters of units
based on their inferred states. In this paper, we develop the MBASIC (Matrix
Based Analysis for State-space Inference and Clustering) framework. MBASIC
consists of two parts: state-space mapping and state-space clustering. In
state-space mapping, it maps observations onto a finite state-space,
representing the activation states of units across conditions. In state-space
clustering, MBASIC incorporates a finite mixture model to cluster the units
based on their inferred state-space profiles across all conditions. Both the
state-space mapping and clustering can be simultaneously estimated through an
Expectation-Maximization algorithm. MBASIC flexibly adapts to a large number of
parametric distributions for the observed data, as well as the heterogeneity in
replicate experiments. In our data-driven simulation studies, MBASIC showed
significant accuracy in recovering both the underlying state-space variables
and clustering structures. We applied MBASIC to two genome research problems
using large numbers of datasets from the ENCODE project. In both studies,
MBASIC showed higher levels of raw data fidelity than analyzing these data with
a two-step approach using ENCODE results on transcription factor occupancy
data.
