FOM: Figure of Merit

Parameter Information


Sample Selection

The sample selection option indicates whether to cluster genes or experiments.

FOM Iteration Selection

The FOM module can be run several times to get average FOM values. This feature is useful for KMC FOM runs since each KMC run gives possibly unique results due to the random nature of KMC intialization. If this parameter is greater than one the FOM graph will reflect the mean FOM values with SD bars.

Algorithm Selection Tabs

The Figure of Merit is, in concept, a measure of fit of the expression patterns for the clusters produced by a particular algorithm. MeV's FOM implementation provides FOM results for running the KMC and CAST clustering algorithms. Each algorithm is initialized by selecting either the K-Means/K-Medians tab or the CAST tab.

KMC Parameters

Means/Medians Option

The Means or Medians option indicates whether each cluster's centroid vector should be calculated as a mean or as a median of the member expression patterns.

Maximum Number of Clusters

This positive integer value indicates the maximum number of clusters to be created. For instance, if the entered value is 10 then KMC is run 10 times to produce 1,2,3..,10 clusters. An FOM value is returned for each run.

Maximum Number of Iterations

This positive integer value is the maximum number of times that all the elements in the data set will be tested for cluster fit within a single KMC run. On each iteration each element is associated with the cluster with the closest mean (or median).

Note that a KMC run will terminate when either no elements require migration (reassignment) to new clusters or when the maximum number of iterations has been reached.

CAST Parameters

Threshold Interval

*For FOM an interval is used to perform a series of CAST runs in which the Affinity Threshold is incremented from 0.0 by the interval indicated. The default of 0.1 is often a good value since it provides 11 CAST results from 0.0 to 1.0 incremented by 0.1.

The threshold parameter is a value ranging from 0.0 to 1.0 which is used as a cluster affinity threshold. Each expression element will have an affinity for the current cluster being created based on it's relationship to the elements currently in the cluster. If that affinity is greater than the supplied threshold the gene is permitted to be a member of the cluster.