SWAP.KTSP.Statistic: Function computing TSP votes (comparisons) and combine their...
In switchBox: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm

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

SWAP.KTSP.Statistics computes the votes in favor of one of the classes or the other for each TSP. This function also computes the final, combined, consensus of all TSP votes based on a specific decision rules. The default is the kTSP statistics, sum of the votes.

1	SWAP.KTSP.Statistics(inputMat, classifier, CombineFunc)

`inputMat`	is a numerical matrix containing the measurements (e.g., gene expression data) to be used to compute the individual TSP votes and their consensus. like the matrix used for training classifier (in `SWAP.KTSP.Train` function), inputMatrix rows represent the features and the columns represent the samples.
`classifier`	the classifier obtained by invoking `SWAP.KTSP.Train`.
`CombineFunc`	is the function used to combine the votes (i.e., comparisons) of individual TSPs contained in the classifier. By default, the consensus is the count of the votes taking into account the order of the features in each TSP. Using this argument alternative aggregating functions can be also passed to `SWAP.KTSP.Statistics` as described below (see “details”).

For each TSP in the KTSP classifier, SWAP.KTSP.Statistics computes the vote in favor of one of classes or the other. This function also aggregates the individual TSP votes and computes a final consensus of all TSP votes based on specific combination rules. By default, this combination is achieved by counting the comparisons (votes) of TSPs as follows: If the first feature is larger than the second one, the TSP vote is positive, else the TSP vote is negative. Different combination rules can also be specified by defining an alternative combination function and by passing it to SWAP.KTSP.Statistics using the CombineFunc argument. A combination function takes as its input a logical vector x corresponding to the sample TSP comparisons (TRUE if the first feature in the pair is larger then the second, FALSE in the opposite case). The output of the CombineFunction is a single value summarizing the votes of all individual TSPs (see examples below). Note that CombineFunction function must operate on a logical vector as input and the outcome must be real value number.

A list containing the following two components:

`statistics`	a named vector containing the aggregated summary statistics computed by `CombineFunc`. The names correspond to samples and are derived from `colnames(inputMat)`.
`comparisons`	a logical matrix containing the individual TSP votes (`TRUE` if the first pair feature is larger then the second one, `FALSE` otherwise). The columns of this matrix correspond to TSP comparisons and are named accordingly using feature names derived from `rownames(inputMat)`. The columns of this matrix correspond to the samples and are named accordingly using `colnames(inputMat)`.

Bahman Afsari bahman.afsari@gmail.com, Luigi Marchionni marchion@jhu.edu, Wikum Dinalankara wdinala1@jhmi.edu

See switchBox for the references.

SWAP.KTSP.Classify, SWAP.Filter.Wilcoxon, SWAP.CalculateSignedScore

##################################################
### Load gene expression data for the training set
data(trainingData)


### Show group variable for the TRAINING set
table(trainingGroup)


##################################################
### Train a classifier using default filtering function based on the Wilcoxon test
classifier <- SWAP.KTSP.Train(matTraining, trainingGroup,
			      FilterFunc = NULL, krange=8)

### Show the TSP in the classifier 
classifier$TSPs


##################################################
### Compute the TSP votes and combine them using various methods

### Here we will use the count of the signed TSP votes
ktspStatDefault <- SWAP.KTSP.Statistics(inputMat = matTraining,
    classifier = classifier)

### Here we will use the sum of the TSP votes
ktspStatSum <- SWAP.KTSP.Statistics(inputMat = matTraining,
    classifier = classifier, CombineFunc=sum)

### Here, for instance, we will apply a hard treshold equal to 2
ktspStatThreshold <- SWAP.KTSP.Statistics(inputMat = matTraining,
    classifier = classifier,  CombineFunc = function(x) sum(x) > 2 )

### Show components
names(ktspStatDefault)

### Show some of the votes
head(ktspStatDefault$comparisons[ , 1:2])

### Show default statistics
head(ktspStatDefault$statistics)

### Show statistics obtained using the sum
head(ktspStatSum$statistics)

### Show statistics obtained using the hard threshold
head(ktspStatThreshold)

### Make a heatmap showing the individual TSPs votes
colorForRows <- as.character(1+as.numeric(trainingGroup))
heatmap(1*ktspStatDefault$comparisons, scale="none",
    margins = c(10, 5), cexCol=0.5, cexRow=0.5,
    labRow=trainingGroup, RowSideColors=colorForRows)