Bayesian correlated clustering to integrate multiple datasets.

Kirk, P and Griffin, JE and Savage, RS and Ghahramani, Z and Wild, DL (2012) Bayesian correlated clustering to integrate multiple datasets. Bioinformatics, 28. pp. 3290-3297.

Abstract

The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets.

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