plotGainingH: Gaining plots for using h-statistic instead original omic...
In deco: Decomposing Heterogeneous Cohorts using Omic Data Profiling

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

This function returns three plots for a particular feature(s), showing the correlation between the h-statistic calculated by DECO and the original omic data.

1 2	plotGainingH(deco, data, ids, print.annot = FALSE, orig.classes = TRUE)

`deco`	a "deco" R object generated by 'decoNSCA' R function.
`data`	a matrix of normalized omic data with 'f' features (rows) by 'n' samples (columns).
`ids`	character vector indicating what original IDs from input data should be highlighted and plot in 'Profile' section. If all IDs found have to be plot, input all corresponding IDs here.
`print.annot`	logical indicating if annotation (gene SYMBOL) should be printed instead original IDs. This option needs previous annotation within 'decoRDA' function or late annotation binding information to @featureTable slot of 'deco' R object.
`orig.classes`	logical indicating if original sample categories compared (orig.classes = TRUE) or DECO subclasses (orig.classes = FALSE) will be represented.

Diagnostic plot associating omic profile and h-statistic profile of any significant feature found by DECO.

Three plots are returned: Boxplot: distribution of omic data and h-statistic values per DECO subclass. Top-left: parametric correlation between omic data and h-statistic per sample. Top-right: non-parametric correlation (ranking) between omic data and h-statistic per sample.

Francisco Jose Campos Laborie. <fjcamlab@gmail.com>

decoRDA, decoNSCA

#### Further information in DECO vignette(s).
## Run after decoRDA and decoNSCA steps on 'ALCLdata'
data(ALCLdata)
ALCL

## Feature to represent
id <- featureTable(deco.results.ma)[1, "ID"]

#### Comparing DECO subclasses against source of samples.
plotGainingH(deco.results.ma, data = assay(ALCL), ids = id, 
             print.annot = FALSE, orig.classes = FALSE)