selectContrib: Select contributing features/genes

In MineICA: Analysis of an ICA decomposition obtained on genomics data

Description

This function selects elements whose absolute scaled values exceed a given threshold.

Usage

  selectContrib(object, cutoff, level, ...)

Arguments

object	Either an IcaSet object, or a list of projection vectors, e.g the list of feature or gene projections on each component.
cutoff	The threshold according to which the elements will be selected. Must be either of length 1 and the same treshold is applied to all components, or of length equal to the number of components in order to use a specific threshold for each component.
level	The level of the selection: either "genes" to select contributing genes using SByGene(icaSet), or "features" to select contributing features using S(icaSet).
...	...

Details

Each vector is first scaled and then only elements with an absolute scaled value higher than cutoff are kept.

Value

A list of projections restricted to the elements that are higher than cutoff.

Author(s)

Anne Biton

Examples

## Not run: 
## load an example of icaSet
data(icaSetCarbayo)

##### =========
####  When arg 'object' is an IcaSet object
##### =========

## select contributing genes
selectContrib(object=icaSetCarbayo, cutoff=3, level="genes")

## select contributing features
selectContrib(object=icaSetCarbayo, cutoff=3, level="features")

##### =========
#### When arg 'object' is a list
##### =========
 c1 <- rnorm(100); names(c1) <- 100:199
 c2 <- rnorm(100); names(c2) <- 1:99
 selectContrib(object=list(c1,c2), cutoff= 0.5)

## select contributing features
contribFlist <- selectContrib(Slist(icaSetCarbayo), 3)

## select contributing genes
contribGlist <- selectContrib(SlistByGene(icaSetCarbayo), 3)



## End(Not run)

MineICA documentation built on Nov. 8, 2020, 5:35 p.m.