adjacencyMatrixKNN: MiRNAss: Genome-wide pre-miRNA discovery from few labeled...
In cyones/miRNAss: Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples

Description Usage Arguments Value Examples

View source: R/adjacencyMatrixKNN.R

This funtions builds the adjacency matrix (the graph) given a data frame of numerical features.

1 2	adjacencyMatrixKNN(sequenceFeatures, sequenceLabels = rep(0, nrow(sequenceFeatures)), nNearestNeighbor = 10, threadNumber = NA)

`sequenceFeatures`	Data frame with features extracted from stem-loop sequences.
`sequenceLabels`	Vector of labels of the stem-loop sequences. It must have -1 for negative examples, 1 for known miRNAs and zero for the unknown sequences (the ones that would be classificated).
`nNearestNeighbor`	Number of nearest neighbors in the KNN graph. The default value is 10.
`threadNumber`	Number of threads used for the calculations. If it is NA leave OpenMP decide the number (may vary across different platforms).

Returns the eigen descomposition as a list with two elements: The eigen vectors matrix 'U' and the eigen values vector 'D'.

# First construct the label vector with the CLASS column
y = as.numeric(celegans$CLASS)*2 - 1

# Remove some labels to make a test
y[sample(which(y>0),200)] = 0
y[sample(which(y<0),700)] = 0

# Take all the features but remove the label column
x = subset(celegans, select = -CLASS)

A = adjacencyMatrixKNN(x, y, 10, 8)

for (nev in seq(50,200, 50)) {
    # the data frame of features 'x' should not be pass as parameter
    p = miRNAss(sequenceLabels = y, AdjMatrix = A,
                nEigenVectors = nev)
    # Calculate some performance measures
    SE = mean(p[ celegans$CLASS & y==0] > 0)
    SP = mean(p[!celegans$CLASS & y==0] < 0)
    cat("N: ", nev, "\n  SE: ", SE, "\n  SP:   ", SP, "\n")
}