# script to run maSigPro on one series patient data


# create edesign object
edesign = cbind( Time=rep(1:4,5),
               	Replicates= c(1:20),
              	P1= c(rep(1,4),rep(0,16)),
		P2=c(rep(0,4),rep(1,4),rep(0,12)),
		P3=c(rep(0,8),rep(1,4),rep(0,8)),
		P4=c(rep(0,12),rep(1,4),rep(0,4)),
                P5=c(rep(0,16),rep(1,4))
                )
data # make you your data is a matrix with labelled columns (Arrays) and rows (genes)

rownames(edesign) <- colnames(data) # you need to match rownames in edesign and colnames of your dataset (arrays)

library(maSigPro)

# make and modify design object 
design2 <- make.design.matrix(edesign, degree=2) # quadratic model
# remove patient vs time interactions
design2$dis <- design2$dis[,-c(6:9,11:14)] 
design2$groups.vector <- design2$groups.vector[-c(6:9,11:14)]

design3 <- make.design.matrix(edesign, degree=3) # cubic model
# remove patient vs time interactions
design3$dis <- design3$dis[,-c(6:9,11:14,16:19)] 
design2$groups.vector <- design2$groups.vector[-c(6:9,11:14,16:19)]

# Usage maSigPro
==============

fit <- p.vector(data,edesign2, Q=0.05)  # or edesign3
tstep <- T.fit(fit)
sigs <- get.siggnes(tstep, vars="groups", significant.intercept="none", rsq=0.6)  # try different rsq values
sigs$summary # lists significant genes

see <- see.genes(sigs$sig.genes [[1]]) # graphical display
see$cut # gives the cluster assignment for genes