Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements.

ABSTRACT:

Background

Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.

Results

We present a generative model-based Bayesian hierarchical clustering algorithm for microarray time series that employs Gaussian process regression to capture the structure of the data. By using a mixture model likelihood, our method permits a small proportion of the data to be modelled as outli

SUBMITTER: Cooke EJ 

PROVIDER: S-EPMC3228548 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature