decompose.gs.ind: Data-wise or PC-wise decomposition of gene set scores for a...
In mogsa: Multiple omics data integrative clustering and gene set analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2	decompose.gs.ind(x, gs, obs, type = 3, nf = 2, plot=TRUE, col.data = NULL, col.pc = NULL, legend = TRUE)

`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

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.

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

Chen Meng

TBA

See Also as decompose.gs.group

  # 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)