mogsa (version 1.6.4)

decompose.gs.ind: 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

Value

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

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.

References

TBA

See Also

See Also as decompose.gs.group

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)