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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