title: Bayesian correlated clustering to integrate multiple datasets.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI's performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques-as well as to non-integrative approaches-demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.
authors.affiliation: Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Kirk
authors.forename: Jim E
authors.initials: JE
authors.lastname: Griffin
authors.forename: Richard S
authors.initials: RS
authors.lastname: Savage
authors.forename: Zoubin
authors.initials: Z
authors.lastname: Ghahramani
authors.forename: David L
authors.initials: DL
authors.lastname: Wild
mesh_terms: D001499:Bayes Theorem; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000235:genetics; D049490:Systems Biology