Abstract
One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes) into groups of highly correlated genes that have the same effect on the outcome (recovery). We propose a random effects model where the genes within each group (cluster) equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.
Highlights
Cluster analysis has been used in diverse fields to assign characteristics of an observation into groups so that those in the same group are more similar to each other than they are to those in other groups
We have proposed Bayesian methodology for the informative clustering of genes
Our model accounts for correlation between genes in the same cluster and jointly relates the gene
Summary
Cluster analysis has been used in diverse fields to assign characteristics of an observation (or observations) into groups (clusters) so that those in the same group are more similar to each other than they are to those in other groups. Cluster analysis is described as unsupervised learning because there is not an outcome that informs the algorithm. It is juxtaposed to supervised learning that includes techniques such as discriminant analysis that are designed to use the characteristics of an observation in order to predict an outcome that is associated with it. A great deal of new interest in machine learning comes from its application to genomics and proteomics, exciting new avenues of biological research. PLOS ONE | DOI:10.1371/journal.pone.0141874 November 12, 2015
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