R/cancer_hallmarks.r

Defines functions pipeline.cancerHallmarks

pipeline.cancerHallmarks <- function(env)
{
  hallmark.sets.names <- list(
    'Angiogenesis'=c(
      "positive regulation of angiogenesis",
      "blood vessel development",
      "sprouting angiogenesis"
    ),
    'Contr. Genomic Instability'=c(
      "DNA repair",
      "response to DNA damage stimulus",
      "response to UV",
      "response to ionizing radiation"
    ),
    'Glucose Energetics'=c(
      "glycolysis",
      "positive regulation of glycolysis"
    ),
    'Inflammation'=c(
      "inflammatory response",
      "positive regulation of inflammatory response"
    ),
    'Invasion and Metastasis'=c(
      "ALONSO_METASTASIS_EMT_UP",
      "CROMER_METASTASIS_UP",
      "ODONNELL_METASTASIS_UP",
      "JAEGER_METASTASIS_UP",
      "VICENT_METASTASIS_UP",
      "BIDUS_METASTASIS_UP",
      "RAMASWAMY_METASTASIS_UP",
      "ALONSO_METASTASIS_UP",
      "negative regulation of cell adhesion",
      "negative regulation of cell-cell adhesion",
      "negative regulation of cell-matrix adhesion",
      "cell motility"
    ),
    'Proliferation'=c(
      "positive regulation of cell proliferation",
      "positive regulation of cell growth",
      "cell cycle",
      "DNA replication"
    ),
    'Replicative Immortality'=c(
      "telomere maintenance",
      "chromosome, telomeric region",
      "REACTOME_EXTENSION_OF_TELOMERES",
      "DAIRKEE_TERT_TARGETS_UP",
      "KANG_IMMORTALIZED_BY_TERT_UP"
    ),
    'Resisting Death'=c(
      "negative regulation of apoptotic process",
      "negative regulation of autophagy"
    )
  )


  hallmark.sets.genes <- lapply(hallmark.sets.names, function(x)
  {
    unique(unlist(sapply(env$gs.def.list[x], function(y) { y$Genes })))
  })

  hallmark.sets.ids <- lapply(hallmark.sets.genes, function(x)
  {
    unique( env$gene.info$ensembl.mapping[ which(env$gene.info$ensembl.mapping$ensembl_gene_id%in%x), 1] )
  })

  hallmark.sets.names <- hallmark.sets.names[which(sapply(hallmark.sets.ids, length) > 0)]
  hallmark.sets.genes <- hallmark.sets.genes[which(sapply(hallmark.sets.ids, length) > 0)]
  hallmark.sets.ids <- hallmark.sets.ids[which(sapply(hallmark.sets.ids, length) > 0)]

  ### GSZ profiles
  
  if(length(hallmark.sets.ids)>1)
  {  
    hallmark.sets.list <- lapply(hallmark.sets.genes, function(x) list(Genes=x,Type=""))
  
    mean.ex.all <- colMeans( env$indata.ensID.m )
    sd.ex.all <- apply( env$indata.ensID.m, 2, sd )
    
    hallmark.GSZ.matrix <- t( sapply( hallmark.sets.list, Sample.GSZ, env$indata.ensID.m, mean.ex.all, sd.ex.all ) )

    hallmark.spot.enrichment <- unlist(sapply( env[[paste("spot.list.",env$preferences$standard.spot.modules,sep="")]]$spots, function(x)
    {
      spot.ens.ids <- unique(env$gene.info$ensembl.mapping$ensembl_gene_id[ which(env$gene.info$ensembl.mapping[,1]%in%x$genes) ])
      return(GeneSet.Fisher(spot.ens.ids, unique(env$gene.info$ensembl.mapping$ensembl_gene_id), hallmark.sets.list, sort=FALSE))
    }))
  
  
    ### Output
    filename <- file.path("Geneset Analysis", "0verview Cancer Hallmarks.pdf")
    util.info("Writing:", filename)
    pdf(filename, 21/2.54, 29.7/2.54, useDingbats=FALSE)
  
    layout(matrix(c(1:8), 4, byrow=TRUE), widths=c(3, 1))
  
    for (i in 1:nrow(hallmark.GSZ.matrix))
    {
      hallmark.sets.group.profiles <-
        tapply(hallmark.GSZ.matrix[i,], env$group.labels, c)[unique(env$group.labels)]
  
      par(mar=c(5,4,4,2))
  
      boxplot(hallmark.sets.group.profiles, col=env$groupwise.group.colors, las=2, ylab="GSZ",
              main=names(hallmark.sets.names)[i], ylim=range(hallmark.GSZ.matrix))
  
      abline(h=0, lty=2)
  
      n.map <- matrix(0,env$preferences$dim.1stLvlSom,env$preferences$dim.1stLvlSom)
      gs.nodes <- env$som.result$feature.BMU[hallmark.sets.ids[[i]]]
      n.map[as.numeric(names(table(gs.nodes)))] <- table(gs.nodes)
      n.map[which(n.map==0)] <- NA
      n.map <- matrix(n.map, env$preferences$dim.1stLvlSom)
  
      par(mar=c(5,1,4,1))
  
      lim <- c(1,env$preferences$dim.1stLvlSom) + env$preferences$dim.1stLvlSom*0.01*c(-1,1)
      colr <- env$color.palette.heatmaps(1000)[(na.omit(as.vector(n.map)) - min(n.map,na.rm=TRUE)) /
                            max(1, (max(n.map,na.rm=TRUE) - min(n.map,na.rm=TRUE))) *
                            999 + 1]
  
      plot(which(!is.na(n.map), arr.ind=TRUE), xlim=lim, ylim=lim, pch=16,
           axes=FALSE, xlab="",ylab="", xaxs="i", yaxs="i", col=colr,
           cex=0.5 + na.omit(as.vector(n.map)) / max(n.map,na.rm=TRUE) * 2.8)
  
      title(sub=paste("# features =", length(hallmark.sets.ids[[i]]), ", max =",
                      max(n.map,na.rm=TRUE)),line=0)
  
      box()
    }
  
    if (length(hallmark.spot.enrichment) > 1)
    {
      par(mfrow=c(1,1))
  
      hallmark.spot.enrichment.bin <- hallmark.spot.enrichment
      hallmark.spot.enrichment.bin[which(hallmark.spot.enrichment >= 0)] <- 1
      hallmark.spot.enrichment.bin[which(hallmark.spot.enrichment < 0.1)] <- 0.75
      hallmark.spot.enrichment.bin[which(hallmark.spot.enrichment < 0.01)] <- 0.5
      hallmark.spot.enrichment.bin[which(hallmark.spot.enrichment < 0.001)] <- 0.25
      hallmark.spot.enrichment.bin[which(hallmark.spot.enrichment < 0.00001)] <- 0.0
  
      heatmap(hallmark.spot.enrichment.bin,
              col=c("black","red3","orange","yellow","gray92"),
              Colv=NA, Rowv=NA, scale="n", mar=c(10,10),
              main="Hallmark spot enrichment", margins=c(5,13))
  
      par(new=TRUE, mar=c(5,0,0,3))
      frame()
  
      legend("bottomright", c("p < 0.1","p < 0.01","p < 0.001","p < 0.00001"),
             pch=15, col=rev(c("black","red3","orange","yellow")))
    }
    
  dev.off()

  }

}
hloefflerwirth/oposSOM documentation built on Sept. 12, 2022, 5:07 p.m.