SWAP.CalculateSignedScore {switchBox} | R Documentation |
SWAP.CalculateSignedScore
calculates the pair-wise scores
between features pairs. The user may pass a filtering function
to reduce the number of starting features, or provide a restricted
set of pairs to limit the reported scores to this list.
SWAP.CalculateSignedScore(inputMat, phenoGroup, FilterFunc = SWAP.Filter.Wilcoxon, RestrictedPairs, handleTies = FALSE, verbose = FALSE, ...)
inputMat |
is a numerical matrix containing the
measurements (e.g., gene expression data)
to be used to build the K-TSP classifier.
The columns represent samples and the
rows represent the features (e.g., genes).
The number of columns must agree
with the length of |
phenoGroup |
is a factor containing the training phenotypes with two levels. |
FilterFunc |
is a filtering function to reduce the
starting number of features to be used to identify the
Top Scoring Pairs (TSPs). The default filter is based on
the Wilcoxon rank-sum test
and alternative filtering functions can be passed too
(see |
RestrictedPairs |
is a character matrix with two columns
containing the feature pairs to be considered for score calculations.
Each row should contain a pair of feature names matching the
|
handleTies |
is a logical value indicating whether tie handling should be enabled or not. FALSE by default. |
verbose |
is a logical value indicating whether status messages will be printed or not throughout the function. FALSE by default. |
... |
Additional argument passed to the filtering
function |
The output is a list containing the following items:
labels |
the levels (phenotypes) in |
score |
a matrix or a vector containing the pair-wise scores.
Basically, |
Note that the P
, Q
, and score
list elements are matrices when scores are computed
for all possible feature pairs, while they are vectors
when scores are computed for restricted pairs
defined by RestrictedPairs
.
Bahman Afsari bahman.afsari@gmail.com, Luigi Marchionni marchion@jhu.edu, Wikum Dinalankara wdinala1@jhmi.edu
See switchBox for the references.
See SWAP.KTSP.Train
,
SWAP.Filter.Wilcoxon
,
and SWAP.KTSP.Statistics
.
### Load gene expression data for the training set data(trainingData) ### Show group variable for the TRAINING set table(trainingGroup) ### Compute the scores using all features (a matrix will be returned) scores <- SWAP.CalculateSignedScore(matTraining, trainingGroup, FilterFunc=NULL, ) ### Show scores class(scores) dim(scores$score) ### Get the scores for a couple of features diag(scores$score[ 1:3 , 5:7 ]) ### Compute the scores using the default filtering function for 20 features scores <- SWAP.CalculateSignedScore(matTraining, trainingGroup, featureNo=20) ### Show scores dim(scores$score) ### Creating some random pairs set.seed(123) somePairs <- matrix(sample(rownames(matTraining), 25, replace=FALSE), ncol=2) ### Compute the scores for restricted pairs (a vector will be returned) scores <- SWAP.CalculateSignedScore(matTraining, trainingGroup, FilterFunc = NULL, RestrictedPairs = somePairs ) ### Show scores class(scores$score) length(scores$score)