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ClusterAnnotator

Produces a hierarchical clustering of the samples and assigns the provided annotations to the brances of the clustering tree. The clustering is based on the provided data matrix.

Version	1.0
Bundle	microarray
Categories	Clustering
Authors	Marko Laakso (Marko.Laakso@Helsinki.FI)
Issue tracker	View/Report issues
Source files	component.xml function.scala
Usage	Example with default values ClusterAnnotator( data = Matrix, annotations = AnnotationTable, annots = <string>, maxP = 0.05, minMI = 0.2, showLabel = true, useNA = false )

Inputs

Name	Type	Mandatory	Description
data	Matrix	Mandatory	Clustering data
annotations	AnnotationTable	Mandatory	Sample annotations

Outputs

Name	Type	Description
originalPlot	Latex	Original clustering report
annotPlot	Latex	Annotated clustering report
splits	SetList	Details about the annotation assignments

Parameters

Name	Type	Default	Description
annots	string	(no default)	A comma separated list of annotation name=column pairs representing visible labels and the corresponding annotations columns selected for the annotations.
maxP	float	0.05	P-value threshold to be used.
minMI	float	0.2	The lower limit of the mutual information to be used.
showLabel	boolean	true	Include sample labels to the output visualization.
useNA	boolean	false	If true, the missing data (=NA values) is used also to calculate the MI and missing data can thus be enriched in the branches of the tree. If false, the random process to select a node uniformly random, which is used to define the MI, is modified in the way that a node with missing data cannot be selected.

Test cases

Test case	Parameters▼	IN data	IN annotations	OUT originalPlot	OUT annotPlot	OUT splits
case1	properties	data	annotations	(missing)	(missing)	splits
	annots = Tyyppi=type,class
case2	properties	data	annotations	(missing)	(missing)	splits
	annots = Tyyppi=type,class, maxP = 1, minMI = 0.1

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