RGSEAsd: Random Gene Set Enrichment Analysis features selected based...

Description Usage Arguments Value Author(s) Examples

View source: R/RGSEAsd.R

Description

This is the function for classification with features from top and bottom deviating from the mean value of the whole transcriptome for a certain standard deviations of the subtset features.

Usage

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RGSEAsd(query, reference, queryclasses, refclasses, random = 5000, sd = 2, iteration = 100)

Arguments

query

A matrix, The query data. This is the data which the research wants to know the class.

reference

A matrix. The reference data. Based of the reference data, the research infer the class of query data.

queryclasses

A character vector. It contains the classes of query data. If you don't know the classes of query data, just give it a character vector equal to the number of query data.

refclasses

A character vector. It contains the classes of reference data. You must know it.

random

A numeric variable. The number of features in the subset randomly sampled from the whole features each time.

sd

number of standard deviations the features selected from the subset deviate from the mean value of the subset.

iteration

A numeric varialbe. The times of random sampling.

Value

[1] The times of each sample in the reference dataset is the most similar to the query data. [2] The frequencey of features selected from the top and bottom of the subsets from the query data, if the query data is correcly classified.

Author(s)

Chengcheng Ma

Examples

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if(interactive()) {
    data(cmap)
    test <- RGSEAsd(cmap[,1],cmap[,2:6], queryclasses=colnames(cmap)[1], 
      refclasses=colnames(cmap)[2:6], random=5000, sd=2, iteration=100)
}

Bioconductor-mirror/RGSEA documentation built on May 31, 2017, 10:43 p.m.