R/LBI.R

In anapuce: Tools for Microarray Data Analysis

LBI <-
  function(infile,
           name.M = "M.norm",
           ind.array = 1:2,
           graph = TRUE,
           graphout = "FigM1M2") {
    ind <- which(names(infile) %in% paste(name.M, ind.array, sep = ""))
    cond <- as.data.frame(colSums(apply(infile[, ind], 1, is.na)))
    nbgene1 <- table(cond)[unique(cond) == 1]
    condb <- as.data.frame(colSums(apply(infile[, ind], 1, is.na)))
    nbgene2 <- table(condb)[unique(condb) == 2]
    cat("Number of genes with only one observation :", nbgene1, "\n")
    cat("Number of genes wihtout any observation :",
        max(nbgene2, 0),
        "\n")

    VarParGene2 <- rowVars(infile[, ind], na.rm = TRUE)
    VarParGene1 <-
      rowVars(data.frame(infile[, ind[1]], -infile[, ind[2]]), na.rm = TRUE)

    Var2 <- mean(VarParGene2, na.rm = TRUE)
    Var1 <- mean(VarParGene1, na.rm = TRUE)
    cat("Variance per gene (Mean Log-ratio) : ", Var1, "\n")
    cat("Variance per gene (Dye bias) : ", Var2, "\n")
    cat("LBI : ", Var1 / Var2, "\n")

    if (graph) {
      png(filename = paste(graphout, ".png", sep = ""))
      plot(
        infile[, ind[1]],
        infile[, ind[2]],
        xlim = c(-3, 3),
        ylim = c(-3, 3),
        xlab = "log2(R1/G1)",
        ylab = "log2(R2/G2)"
      )
      dev.off()
    }
    # (c) 2010 Institut National de la Recherche Agronomique

  }