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.Full text not available from this repository.
MOTIVATION: 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.
|Uncontrolled Keywords:||Bayes Theorem Chromatin Immunoprecipitation Cluster Analysis Gene Expression Gene Expression Profiling Genomics Models, Statistical Normal Distribution Oligonucleotide Array Sequence Analysis Protein Interaction Mapping Saccharomyces cerevisiae Systems Biology|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||09 Dec 2016 17:12|
|Last Modified:||29 Apr 2017 23:27|