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

Description Usage Arguments Value Author(s) Examples

View source: R/RGSEAsd.R

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.

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

`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.

[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.

Chengcheng Ma

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)
}