## Analyze the current MAExplorer array data using PCA creating 
# PDF and .out files.
# Authors: P. Lemkin, S. Sundaram
# $Date: 6-14-2003$ $Revision: 1.1$
#

# The input file contains the format as genes(rows) x experiments(columns).
oclData <- oclTbl$hData
condNames <- oclTbl$condNames
allCondNames <- oclTbl$allCondNames
sampleNames <- oclTbl$sampleNames
nConds <- oclTbl$nConds
nGenes <- oclTbl$nGenes
isVertStacked <- oclTbl$isVertStacked
paste("The Ordered Condition List has ",nGenes," genes")
paste("The Ordered Condition List has ",nConds," conditions")
paste("The Ordered Condition List has ",length(sampleNames)," samples")

# -- [1] Compute the PCA both for Arrays and for Genes
print("Computing the PCA of the current MAExplorer data by 'arrays'")
maePCAarrays <- mae.simplePCAanalysis(data=oclData,plotFile="simplePCAarrays.pdf",
                                      mode="arrays", scale.data=TRUE,
                                      uniqGroups=allCondNames,group=condNames)

# -- [1.1] Write out the data into files 
cat("Number of Eigen Vectors=",maePCAarrays$nEigenVecs,"\n")
summary(maePCAarrays)
summary(maePCAarrays$pca)
mae.writeEigenData(maePCAarrays$pca, eigenValFile="EigenValuesArrays.out", 
                   eigenVecsFile="EigenVectorsArrays.out",
                   points3Dfile="Points3DArrays.out")

# --- [1.2] Now do 3D plot of the Array data and of Genes data PC1, PC2, PC3
p3Da <- read.table("Points3DArrays.out", header=TRUE)
colnames(p3Da) <- c("PC1","PC2","PC3")

# Do 3D plots of loadings of samples
pdf("points3Darray.pdf", encoding="ISOLatin1",width=6,height=6) 
mae.colorBased3dPlotting(allCondNames[-1],condNames,p3Da,
                         xindex=1,yindex=2,zindex=3,
                         xlabel="PC1", ylabel="PC2", zlabel="PC3",
                         mainTitle="3D plot of PC1 vs PC2 vs PC3 of Arrays")
### [TODO] Add legend
dev.off()


# -- [2] Compute the PCA both for Arrays and for Genes
print("Computing the PCA of the current MAExplorer data by 'genes'")
maePCAgenes <- mae.simplePCAanalysis(data=oclData,plotFile="simplePCAgenes.pdf",
                                     mode="genes", scale.data=TRUE,
                                     uniqGroups=allCondNames,group=condNames)
# -- [2.1] Write out the data into files   
cat("Number of Eigen Vectors=",maePCAgenes $nEigenVecs,"\n")
summary(maePCAgenes)
summary(maePCAgenes$pca)
mae.writeEigenData(maePCAgenes$pca, eigenValFile="EigenValuesGenes.out", 
                   eigenVecsFile="EigenVectorsGenes.out",
                   points3Dfile="Points3DGenes.out")