Description Usage Arguments Value Author(s) References See Also Examples
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.
1 2 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ### 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)
|
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