geva.quick: All-In-One Function for GEVA Intermediate Procedures
In sbcblab/geva: Gene Expression Variation Analysis (GEVA)

Description Usage Arguments Details Value Examples

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.

1	geva.quick(gobject, ...)

`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()`

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.

A GEVAResults object

## 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