R/seeDS.R

In maSigPro: Significant Gene Expression Profile Differences in Time Course Gene Expression Data

seeDS <- function(get, rsq=0.4, cluster.all=TRUE, plot.mDSG=FALSE, k=6, cluster.method="hclust", k.mclust=FALSE)
{
# cluster.all=TRUE, cluster with significant isoforms of all the genome.
# cluster.all=FALSE, cluster with DETs of DSGs  

Model <- get$Model
get2 <- get$get2

  data <- Model$data
  gen <- Model$gen
  design <- Model$design

  sig.iso2 <- get2$summary
  gen.sig.iso2 <- as.character(gen[rownames(data)%in%sig.iso2])
  NT2<- get$NumIso.by.gene

#---------------------------------------------------------------------------------------------------
# cluster.all = TRUE. First p.vector to all the transcripts
#---------------------------------------------------------------------------------------------------
 if(cluster.all) {
 step3 <- p.vector(data, design, family=Model$pvector2$family)
 Tfit3 <- T.fit(step3)
 get3 <- get.siggenes(Tfit3, vars="all", rsq=rsq)
 sig.iso3 <- get3$summary
 
 H <- see.genes(get3$sig.genes,item="Isoforms", k=k)
 cut <- H$cut[sig.iso2] # tomamos sólo el cut de las isoformas que nos interesan: 325
}

if(!cluster.all) {
 H <- see.genes(get2$sig.genes, item="Isoforms", k=k)
 cut <- H$cut
}

#---------------------------------------------------------------------------------------------------
# If a plot of mDSG is asked:
#---------------------------------------------------------------------------------------------------
if(plot.mDSG) {
 data.clust <- get2$sig.genes$sig.profiles
 genes.1 <- names(NT2[NT2==1])
 data.clust1 <- data.clust[gen.sig.iso2%in%genes.1,]
 H1 <- see.genes(data.clust1, edesign=design$edesign, cluster.method=cluster.method, k.mclust=k.mclust, k=k, item="Isoforms")
}

##----------------- RESULTS--------------------------------------------
  
  out <- list(Model, get2, NT2, cut, gen.sig.iso2)
  names(out)<-c("Model", "get2", "NumIso.by.gene","cut", "names.genes")
  out
}