order.responses: order.responses
In netresponse: Functional Network Analysis

Description Usage Arguments Value Note Author(s) References Examples

Orders the responses by association strength (enrichment score) to a given sample set. For instance, if the samples correspond to a particular experimental factor, this function can be used to prioritize the responses according to their association strength to this factor.

order.responses(
  models,
  sample,
  method = "hypergeometric",
  min.size = 2,
  max.size = Inf,
  min.responses = 2,
  subnet.ids = NULL,
  verbose = FALSE,
  data = NULL
)

`models`	List of models. Each model should have a sample-cluster assignment matrix qofz.
`sample`	Measure enrichment of this sample (set) across the observed responses.
`method`	'hypergeometric' measures enrichment of factor levels in this response; 'precision' measures response purity for each factor level; 'dependency' measures logarithm of the joint density between response and factor level vs. their marginal densities: log(P(r,s)/(P(r)P(s)))
`min.size, max.size, min.responses`	Optional parameters to filter the results based on subnet size and number of responses.
`subnet.ids`	Specify subnets for which the responses shall be ordered. By default, use all subnets.
`verbose`	Follow progress by intermediate messages.
`data`	data (samples x features; or a vector in univariate case)

A data frame with elements 'ordered.responses' which gives a data frame of responses ordered by enrichment score for the investigated sample. The subnetwork, response id and enrichment score are shown. The method field indicates the enrichment calculation method. The sample field lists the samples et for which the enrichments were calculated. The info field lists additional information on enrichment statistics.

Tools for analyzing end results of the model.

Leo Lahti leo.lahti@iki.fi

See citation('netresponse') for citation details.

res <- order.responses(models = NULL, sample = NULL)
# - for given sample/s (factor level), 
#     order responses (across all subnets) by association strength 
#     (enrichment score); overrepresentation
# order.responses(model, sample, method  = 'hypergeometric')