geva.quick {geva} | R Documentation

## All-In-One Function for GEVA Intermediate Procedures

### Description

Given a `GEVAInput` object, applies the `geva.summarize()`, `geva.quantiles`, `geva.cluster`, and `geva.finalize` in a single call. Optional arguments are passed to the internal calls of these functions.

### Usage

    geva.quick(gobject, ...)

### Arguments

`gobject` | A `GEVAInput`, or any object that returns a `GEVAInput` upon calling `inputdata(gobject)` (e.g., `GEVASummary` or `GEVAResults`).
`...` | Optional arguments passed to `geva.summarize()`, `geva.quantiles()`, `geva.cluster()`, and `geva.finalize()`

### Details

This function performs the summarization, quantile detecetion, and clustering of an input data, then merges the results together and, if applicable, performs a factor analysis. If the `gobject` is not a `GEVAInput`, it must provide a valid `GEVAInput` object when called by `inputdata(gobject)`. Moreover, all parameters used in previous analysis will be taken into account. For instance, if `gobject` is a `GEVASummary` obtained by using `variation.method='mad'`, the internal call to `geva.summarize` in this function will use `variation.method='mad'` as well, unless if another parameter for `variation.method` is specified in the `...` arguments.

Therefore, this function can be useful not only as a shortcut to analyze `GEVAInput` but also for parameter testing when applied to a `GEVAResults` object, since the previous parameters are reused, while the specified parameters are overriden.

### Value

A `GEVAResults` object

### Examples

    ## Basic usage using a random generated input
    ginput <- geva.ideal.example()   # Generates a random input example
    gresults <- geva.quick(ginput)   # Performs the entire analysis (default parameters)

    print(head(top.genes(gresults))) # Prints the results
    plot(gresults)                        # Plots the final SV-plot

    ## Example with non-default parameters
    ginput <- geva.ideal.example()   # Generates a random input example
    gresults <- geva.quick(ginput,
                           summary.method="median",
                           variation.method="mad",
                           quantiles.method="density",
                           cluster.method="density",
                           resolution=0.32)

    print(head(top.genes(gresults))) # Prints the results
    plot(gresults)                   # Plots the final SV-plot

[Package geva version 1.0.0 Index]