MultiVariAnalysis: Function for Multivariant Analysis

In HTDA: HTDA (High Throughput Data Analysis)

Usage

MultiVariAnalysis(object, method = "clustering", ...)

Arguments

object	preprocessing data from gcms, lcms and rnaseq
method	clustering methods
...

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (object, method = "clustering", ...) 
{
    if (method == "clustering") {
        clust.genes <- amap::hcluster(x = object@qData, method = "pearson", 
            link = "average")
        clust.arrays <- amap::hcluster(x = t(object@qData), method = "pearson", 
            link = "average")
        heatcol <- colorRampPalette(c("Green", "Red"))(32)
        heatmap(x = object@qData, Rowv = as.dendrogram(clust.genes), 
            Colv = as.dendrogram(clust.arrays), col = heatcol)
    }
    if (method == "pca") {
        p <- amap::acp(object@qData)
        plot(p)
        return(p)
    }
    if (method == "kmeans") {
        cl <- amap::Kmeans(object@qData, 2)
        plot(object@qData, col = cl$cluster)
        points(cl$centers, col = 1:2, pch = 8, cex = 2)
        return(cl)
    }
  }

HTDA documentation built on May 2, 2019, 4:53 p.m.