Up: Component summary Function

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

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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