miRNAss (version 1.5)

adjacencyMatrixKNN: MiRNAss: Genome-wide pre-miRNA discovery from few labeled examples

Description

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

Usage

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

Arguments

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).

Value

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

Examples

# NOT RUN {
# 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")
}
# }