A Hierarchical Framework for State Space Matrix Inference and Clustering

TL;DR

This paper introduces MBASIC, a hierarchical framework that simultaneously infers hidden activity states and clusters units based on their state profiles across multiple genomic datasets, improving analysis accuracy.

Contribution

MBASIC is a novel integrated framework combining state-space inference and clustering, adaptable to various data distributions and experimental heterogeneity.

Findings

MBASIC accurately recovers underlying states and clusters in simulations.

MBASIC outperforms two-step approaches in genome data analysis.

Application to ENCODE data demonstrates higher data fidelity.

Abstract

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.