decompose.gs.group {mogsa} | R Documentation

## Data-wise or PC-wise decomposition of gene set scores for all observations.

### Description

Data-wise or PC-wise decomposition of gene set scores (GSS) across all observations. The predefined group/cluster information should be given so that the mean decomposed GSSs for each group are returned and plotted.

### Usage

      decompose.gs.group(x, gs, group, decomp = "data", nf = 2, x.legend = "bottomleft",
        y.legend = NULL, plot = TRUE, main = NULL, ...)

### Arguments

`x` | An object of class `mgsa-class` or `moa.sup-class`
`gs` | The gene set want to exam.
`group` | An vector or factor to indicate the group of observations, such as clusters. See examples.
`decomp` | A charater string either "data" or "pc" to indicate how the gene set scores should be decomposed (with respect to data or PC.
`nf` | The number of axes/PCs to be calculated and plotted.
`x.legend` | Used to control the position of legends.
`y.legend` | Used to control the position of legends.
`plot` | A logical indicates if a plot should be drawn.
`main` | The main title of plot.
`...` | Other arguments passed to `barplot`.

### Details

This function could be used when the number of observation is large and there are cluster/group information is available. In this case, the means of decomposed gene set scores over each group is calculated. The vertical bar on the end of each bar indicates the 95% confident interval of the means.

### Value

Return nothing or a matrix depends on how argument `plot` is set.

### Author(s)

Chen Meng

### References

TBA

### See Also

See Also `decompose.gs.ind`

### Examples

      # library(mogsa)
      # loading gene expression data and supplementary data
      data(NCI60_4array_supdata)
      data(NCI60_4arrays)

      # using a list of data.frame as input
      mgsa <- mogsa(x = NCI60_4arrays, sup=NCI60_4array_supdata, nf=9,
                    proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)

      colcode <- as.factor(sapply(strsplit(colnames(NCI60_4arrays$agilent), split="\\."), "[", 1))
      decompose.gs.group(x = mgsa, gs = 2, group = colcode, decomp = "data", plot = TRUE)
      decompose.gs.group(x = mgsa, gs = 2, group = colcode, decomp = "pc", nf = 3, plot = TRUE)

[Package mogsa version 1.20.0 Index]