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Last data update: 2014.03.03

R: Data-wise or PC-wise decomposition of gene set scores for a...

decompose.gs.ind

R Documentation

Data-wise or PC-wise decomposition of gene set scores for a single observation.

Description

Barplot of decomposed gene set scores, either with respect to datasets or axes.

Usage

  decompose.gs.ind(x, gs, obs, type = 3, nf = 2, plot=TRUE, col.data = NULL, 
    col.pc = NULL, legend = TRUE)

Arguments

`x`	An object of class `mgsa-class` or `moa.sup-class`
`gs`	The gene set want to exam.
`obs`	The observations want to exam.
`type`	Which type of plot. type=1 - the data-pc mode; type=2 - the pc-data mode; type=3 - both. See detail.
`nf`	The number of axes/PCs to be calculated and plotted.
`plot`	A logical indicates if a plot should be drawn
`col.data`	The bar color of datasets
`col.pc`	The bar color of PCs
`legend`	A logical if legend should be shown

Details

type=1 (the data-pc mode), the axes/PCs are represented as the narrow bars with different colors and the background wide bars behind narrow bars are gene set scores for datasets, which is calculated from the sum of all underlying individual axes/PC scores. When type=2 (the pc-data mode) the interpreation of narrow and wide bars are in the other way around. If type=3, both are shown.

This function could only be used to check the decomposition of gene set scores of a single observation. So the function is not efficent when the number of observation is large. Another function decompose.gs.group, could be used in this case, particularly when the cluster information of the observation panel is available.

Value

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

Author(s)

Chen Meng

References

TBA

Examples

  # library(mogsa)
  # loading gene expression data and supplementary data
  data(NCI60_4array_supdata)
  data(NCI60_4arrays)
  mgsa <- mogsa(x = NCI60_4arrays, sup=NCI60_4array_supdata, nf=9,
                proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)

  allgs <- colnames(NCI60_4array_supdata[[1]])
  # plot
  decompose.gs.ind(x=mgsa, gs=allgs[5], obs="BR.MDA_MB_231", type=2, nf=5)
  # or
  decompose.gs.ind(x=getmgsa(mgsa, "sup"), gs=allgs[5], obs="BR.MDA_MB_231", type=3, nf=5)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(mogsa)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/mogsa/decompose.gs.ind.Rd_%03d_medium.png", width=480, height=480)
> ### Name: decompose.gs.ind
> ### Title: Data-wise or PC-wise decomposition of gene set scores for a
> ###   single observation.
> ### Aliases: decompose.gs.ind
> 
> ### ** Examples
> 
>   # library(mogsa)
>   # loading gene expression data and supplementary data
>   data(NCI60_4array_supdata)
>   data(NCI60_4arrays)
>   mgsa <- mogsa(x = NCI60_4arrays, sup=NCI60_4array_supdata, nf=9,
+                 proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)
> 
>   allgs <- colnames(NCI60_4array_supdata[[1]])
>   # plot
>   decompose.gs.ind(x=mgsa, gs=allgs[5], obs="BR.MDA_MB_231", type=2, nf=5)
>   # or
>   decompose.gs.ind(x=getmgsa(mgsa, "sup"), gs=allgs[5], obs="BR.MDA_MB_231", type=3, nf=5)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>