find.similar.features: Find similar features with a given subnetwork.
In netresponse: Functional Network Analysis

Description Usage Arguments Details Value Author(s) References Examples

Given subnetwork, orders the remaining features (genes) in the input data based on similarity with the subnetwork. Allows the identification of similar features that are not directly connected in the input network.

1 2	find.similar.features(model, subnet.id, datamatrix = NULL, verbose = FALSE, information.criterion = NULL)

`model`	NetResponseModel object.
`subnet.id`	Investigated subnetwork.
`datamatrix`	Optional. Can be used to compare subnetwork similarity with new data which was not used for learning the subnetworks.
`verbose`	Logical indicating whether progress of the algorithm should be indicated on the screen.
`information.criterion`	Information criterion for model selection. By default uses the same than in the 'model' object.

The same similarity measure is used as when agglomerating the subnetworks: the features are ordered by delta (change) in the cost function, assuming that the feature would be merged in the subnetwork. The smaller the change, the more similar the feature is (change would minimize the new cost function value). Negative values of delta mean that the cost function would be improved by merging the new feature in the subnetwork, indicating features having coordinated response.

A data frame with elements feature.names (e.g. gene IDs) and delta, which indicates similarity level. See details for details. The smaller, the more similar. The data frame is ordered such that the features are listed by decreasing similarity.

Leo Lahti leo.lahti@iki.fi

See citation('netresponse') for reference details.

data(toydata)
model <- toydata$model
subnet.id <- 'Subnet-1' 
# g <- find.similar.features(model, subnet.id)
# List features that are similar to this subnetwork (delta < 0)
# (ordered by decreasing similarity)
# subset(g, delta < 0)