MSIseq.classify: Classify Microsatellite Instability with MSIseq Classifier
In MSIseq: Assess Tumor Microsatellite Instability with a Decision Tree Classifier from Exome Somatic Mutations

Description Usage Arguments Details Value Author(s) References See Also Examples

This function gives a classification result for MSI status based on classifier with the mutation information in the mutationNum argument.

1	MSIseq.classify(mutationNum, classifier = NGSclassifier, cancerType = NULL)

`mutationNum`	A data frame output from `Compute.input.variables`, which containing 9 variables: T.sns, S.sns, T.ind, S.ind, T, S, Ratio.sns, Ratio.ind, Ratio.
`classifier`	A RWeka J48 model returned from the function `MSIseq.train`. Notice that if this argument is missing, the function will use a default build-in classifier, NGSclassifier.
`cancerType`	A data frame with two columns: Tumor_Sample_Barcode (tumor ID) and the corresponding cancer_type. Check `NGStraintype` for detail. If the classifier used in this function is trained with cancerType argument, cancerType should be provided here.

This function gives a classification of MSI status for the samples with their mutationNum information. The classification is made with the decision tree model in classifier. It also flags the samples with likely POLE deficiency based on the criteria of high T.sns (> 60/Mb) and low S.ind (< 0.18/Mb).

A data frame with three columns: Tumor_Sample_Barcode, the corresponding classified MSI_status, and a third column indicating whether the sample is likely POLE deficient.

Mini Huang

Kurt Hornik, Christian Buchta, Achim Zeileis (2009) Open-Source Machine Learning: R Meets Weka. Computational Statistics, 24(2), 225-232.

MSIseq.train, Compute.input.variables

## load sample data: test.mutationNum

data(test.mutationNum)
data(NGStestseqLen)


## classify on test data with NGSclassifier (the default classifier)

result <- MSIseq.classify(mutationNum = test.mutationNum)

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