eigenDecomposition: 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/eigenDecomposition.R

This funtions calculate the eigenvectors and eigen values of the Laplacian of the graph. As this proccess is quite time comsumin, this functions allows to obtain this decomposition once and the be able to run miRNAss several times in shorter times.

1	eigenDecomposition(AdjMatrix, nEigenVectors)

`AdjMatrix`	Adjacency sparse matrix of the graph.
`nEigenVectors`	Number of eigen vectors.

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)
E = eigenDecomposition(AdjMatrix = A, nEigenVectors = 100)
for (mp in c(0.1,1,10)) {
    p = miRNAss(sequenceLabels = y, AdjMatrix = A,
                eigenVectors = E, missPenalization = mp)
    # Calculate some performance measures
    SE = mean(p[ celegans$CLASS & y==0] > 0)
    SP = mean(p[!celegans$CLASS & y==0] < 0)
    cat("mP: ", mp, "\n  SE: ", SE, "\n  SP:   ", SP, "\n")
}