R/supporting_maps.r

In hloefflerwirth/oposSOM: Comprehensive analysis of transcriptome data

pipeline.supportingMaps <- function(env)
{
  dirname <- "Supporting Maps&Profiles"
  dir.create(dirname, showWarnings=FALSE)

  filename <- file.path(dirname, "Supporting Maps.pdf")
  util.info("Writing:", filename)

  pdf(filename, 21/2.54, 21/2.54, useDingbats=FALSE)

  ### Population Map ###
  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))
  m <- log10(env$som.result$node.summary[,"n.features"])
  m[which(is.infinite(m))] <- NA

  image(matrix(m, env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom),
        axes=FALSE, col=env$color.palette.heatmaps(1000), main="Population Map", cex.main=2.5)

  mtext("log ( # genes in metagene )", side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 0.3, 1))
  image(matrix(1:100, 1, 100), col = env$color.palette.heatmaps(1000), axes=FALSE)

  axis(2, c(1, max(env$som.result$node.summary[,"n.features"])),
       at=c(0, 1), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)

  box()

  ### Variance Map ###
  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))

  image(matrix(log10(apply(env$metadata, 1, var)), env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom),
        axes=FALSE, col=env$color.palette.heatmaps(1000), main="Metagene Variance Map", cex.main=2.5)

  mtext("log ( metagene variance )", side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 0.3, 1))
  image(matrix(1:100, 1, 100), col=env$color.palette.heatmaps(1000), axes=FALSE)

  axis(2, c(round(min(apply(env$metadata, 1, var), na.rm=TRUE),2),
            round(max(apply(env$metadata, 1, var),na.rm=TRUE),2)),
       at=c(0, 1), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)

  box()

  ### Significance Map ###
  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))
  suppressWarnings({ p <- -apply(env$p.m, 1, mean) })

  image(matrix(p, env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom),
        axes=FALSE, main="Metagene Significance Map", cex.main=2.5, zlim=c(-1,0),
        col=colorRampPalette(c("blue4", "blue4", "blue3", "blue3", "blue2",
                               "blue2", "blue1", "lightblue", "darkgreen",
                               "#008B00", "green3", "green", "yellow", "gold",
                               "orange", "red", "darkred"))(1000))

  mtext(expression(paste("<",p[k],">")), side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 1.15, 0.15))

  image(matrix(1:10, 1, 10), axes=FALSE,
        col=rev(colorRampPalette(c("darkgreen", "#008B00", "green3", "green",
                                   "yellow", "gold", "orange", "red", "darkred"))(1000)))

  axis(2, c("0.0", "0.05", "0.1", "0.2", "0.3", "0.4", "0.5"),
       at=c(-0.05, 0.06, 0.17, 0.39, 0.61, 0.83, 1.05), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)

  box()

  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))
  suppressWarnings({ p <- apply(env$p.m, 1, min) })

  image(matrix(-log(p), env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom),
        axes=FALSE, main="Metagene Significance Map", cex.main=2.5,
        col=env$color.palette.heatmaps(1000))

  mtext(expression(paste(min[m],"( log ",p["k,m"]," )")), side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 0.3, 1))
  image(matrix(1:100, 1, 100), col=rev(env$color.palette.heatmaps(1000)), axes=FALSE)
  axis(2, at=c(0,1), c(round(min(log(p),na.rm=TRUE)), 0), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)
  box()


  ### Entropy Map ###
  q25 <- quantile(env$metadata, 0.25)
  q75 <- quantile(env$metadata, 0.75)

  p.metadata <- apply(env$metadata, 1, function(x)
  {
    hist(x, breaks=c(min(x), q25, q75, max(x)), plot=FALSE)$counts / ncol(env$indata)
  })

  H <- apply(p.metadata, 2, function(p)
  {
    p <- p[p != 0]
    -sum(p * log2(p))
  })

  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))

  image(matrix(H, env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom),
        axes=FALSE, col=env$color.palette.heatmaps(1000), main="Standard Metagene Entropy Map", cex.main=2.5)

  mtext(expression(h[k]), side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 0.3, 1))
  image(matrix(1:100, 1, 100), col=env$color.palette.heatmaps(1000), axes=FALSE)

  axis(2, c(round(min(H,na.rm=TRUE),2),round(max(H,na.rm=TRUE),2)),
       at=c(0, 1), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)

  box()

  ### Covariance Map ###
  errors <- c()

  for (i in 1:env$preferences$dim.1stLvlSom^2)
  {
    genes <- names(which(env$som.result$feature.BMU == i))
    mean.cor <- 0

    for (ii in genes)
    {
      suppressWarnings({
        mean.cor <- mean.cor + cor(env$metadata[i,], as.numeric(env$indata[ii,]))
      })
    }

    errors[i] <- mean.cor / length(genes)
  }

  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))

  image(matrix(errors, env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom), axes=FALSE,
        col=env$color.palette.heatmaps(1000), main="Gene-Metagene Covariance Map", cex.main=2.5)

  mtext("correlation genes - metagene", side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 0.3, 1))
  image(matrix(1:100, 1, 100), col=env$color.palette.heatmaps(1000), axes=FALSE)

  axis(2, c(round(min(errors,na.rm=TRUE),2),round(max(errors,na.rm=TRUE),2)),
       at=c(0, 1), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)

  box()

  ### Deviation Map ###
  errors <- rep(NA, env$preferences$dim.1stLvlSom^2)

  for (i in 1:env$preferences$dim.1stLvlSom^2)
  {
    genes <- names(which(env$som.result$feature.BMU == i))

    e <- apply(env$indata[genes, ,drop=FALSE], 1, function(x)
    {
      1 / (ncol(env$indata)-1) * sum((x - env$metadata[i,])^2)
    })

    errors[i] <- sqrt(mean(e))
  }

  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))

  image(matrix(errors, env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom),
        axes=FALSE, col=env$color.palette.heatmaps(1000), main="Deviation Map", cex.main=2.5)

  mtext("deviation genes - metagene", side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 0.3, 1))
  image(matrix(1:100, 1, 100), col=env$color.palette.heatmaps(1000), axes=FALSE)

  axis(2, c(round(min(errors,na.rm=TRUE),2), round(max(errors,na.rm=TRUE),2)),
       at=c(0,1), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)

  box()

  ### Distance Map ###
  uh <- rep(NA, env$preferences$dim.1stLvlSom^2)

  for (i in 1:env$preferences$dim.1stLvlSom^2)
  {
    pos.x <- env$som.result$node.summary[i,"x"]
    pos.y <- env$som.result$node.summary[i,"y"]

    uh[i] <- mean(sapply(get.neighbors(pos.x, pos.y, env$preferences$dim.1stLvlSom), function(x)
    {
      x <- x - 1
      ij <- (x[1] + 1) + x[2] * env$preferences$dim.1stLvlSom
      sqrt(sum((env$metadata[ij,] - env$metadata[i,])^2))
    }))
  }

  par(mfrow = c(1, 1))
  par(mar=c(5, 6, 4, 5))

  image(matrix(log10((uh)), env$preferences$dim.1stLvlSom, env$preferences$dim.1stLvlSom),
        axes=FALSE, col=colorRampPalette(c("blue2","white","red2"))(1000),
        main="Distance Map", cex.main=2.5)

  mtext("deviation of adjacent metagenes", side=1, line=1, cex=1.4)
  box()

  par(new=TRUE, mar=c(1, 0, 0, 0))
  layout(matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0), 3, 3), c(1, 0.05, 0.02), c(0.14, 0.3, 1))
  image(matrix(1:100, 1, 100), col = colorRampPalette(c("blue2","white","red2"))(1000), axes=FALSE)

  axis(2, c(round(min(uh,na.rm=TRUE),2), round(max(uh,na.rm=TRUE),2)),
       at=c(0,1), las=2, tick=FALSE, pos=-0.5, cex.axis=1.4)

  box()


  dev.off()
}