This function calculates a single consensus from given individual data.
simpleConsensusCalculation(
individualData,
consensusOptions,
verbose = 1,
indent = 0)Individual data from which the consensus is to be calculated. It can be either a list or a
multiData structure in which each element is a numeric vector or array.
A list of class ConsensusOptions that contains options for the consensus calculation. A suitable list
can be obtained by calling function newConsensusOptions.
Integer level of verbosity of diagnostic messages. Zero means silent, higher values make the output progressively more and more verbose.
Indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.
A numeric vector or array of the same dimensions as each element of individualData
Consensus is defined as the element-wise (also known as "parallel") quantile of of the individual data at
probability given by the consensusQuantile element of consensusOptions.
Consensus network analysis was originally described in Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54 http://www.biomedcentral.com/1752-0509/1/54
consensusCalculation for consensus calculation that can work with BlockwiseData
and can calibrate data before calculating consensus.