PlotProfiles {maSigPro} | R Documentation

## Function for visualization of gene expression profiles

### Description

`PlotProfiles` displays the expression profiles of a group of genes.

### Usage

    PlotProfiles(data, cond, main = NULL, cex.xaxis = 0.5, ylim = NULL,
        repvect, sub = NULL, color.mode = "rainbow", item = NULL)

### Arguments

`data` | a matrix containing the gene expression data
`cond` | vector for x axis labeling, typically array names
`main` | plot main title
`cex.xaxis` | graphical parameter maginfication to be used for x axis in plotting functions
`ylim` | index vector indicating experimental replicates
`repvect` | index vector indicating experimental replicates
`sub` | plot subtitle
`color.mode` | color scale for plotting profiles. Can be either `"rainblow"` or `"gray"`
`item` | Name of the analysed items to show

### Details

The `repvect` argument is used to indicate with vertical lines groups of replicated arrays.

### Value

Plot of experiment-wide gene expression profiles.

### Author(s)

Ana Conesa, aconesa@cipf.es, Maria Jose Nueda, mj.nueda@ua.es

### References

Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2005. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments.

### See Also

`PlotGroups`

### Examples

    #### GENERATE TIME COURSE DATA
    ## generate n random gene expression profiles of a data set with
    ## one control plus 3 treatments, 3 time points and r replicates per time point.

    tc.GENE <- function(n, r,
                 var11 = 0.01, var12 = 0.01,var13 = 0.01,
                 var21 = 0.01, var22 = 0.01, var23 =0.01,
                 var31 = 0.01, var32 = 0.01, var33 = 0.01,
                 var41 = 0.01, var42 = 0.01, var43 = 0.01,
                 a1 = 0, a2 = 0, a3 = 0, a4 = 0,
                 b1 = 0, b2 = 0, b3 = 0, b4 = 0,
                 c1 = 0, c2 = 0, c3 = 0, c4 = 0)
    {

      tc.dat <- NULL
      for (i in 1:n) {
        Ctl <- c(rnorm(r, a1, var11), rnorm(r, b1, var12), rnorm(r, c1, var13))  # Ctl group
        Tr1 <- c(rnorm(r, a2, var21), rnorm(r, b2, var22), rnorm(r, c2, var23))  # Tr1 group
        Tr2 <- c(rnorm(r, a3, var31), rnorm(r, b3, var32), rnorm(r, c3, var33))  # Tr2 group
        Tr3 <- c(rnorm(r, a4, var41), rnorm(r, b4, var42), rnorm(r, c4, var43))  # Tr3 group
        gene <- c(Ctl, Tr1, Tr2, Tr3)
        tc.dat <- rbind(tc.dat, gene)
      }
      tc.dat
    }

    ## create 10 genes with profile differences between Ctl, Tr2, and Tr3 groups
    tc.DATA <- tc.GENE(n = 10,r = 3, b3 = 0.8, c3 = -1, a4 = -0.1, b4 = -0.8, c4 = -1.2)
    rownames(tc.DATA) <- paste("gene", c(1:10), sep = "")
    colnames(tc.DATA) <- paste("Array", c(1:36), sep = "")

    PlotProfiles (tc.DATA, cond = colnames(tc.DATA), main = "Time Course",
                  repvect = rep(c(1:12), each = 3))

[Package maSigPro version 1.62.0 Index]