RankMerging: Merging the ranker lists with the same labels of the...
In GeneExpressionSignature: Gene Expression Signature based Similarity Metric

Description Usage Arguments Details See Also Examples

View source: R/RankMerging.R

Merging the assay data according to phenotypic data of the input ExpressionSet. Each group of the ranked lists with the same phenotypic data is aggregated into a single list, return it as an ExpressionSet object.

1	RankMerging(exprSet, MergingDistance = c("Spearman", "Kendall"), weighted = TRUE)

`exprSet`	an ExpressionSet object, each column of assay data represents a ranked list obtained by preprocessing the corresponding gene expression profile, and phenotypic data represents the short description (characteristics of gene expression profile, such as the drug type, the disease state) about the assay data.
`MergingDistance`	distance to be used which "measures" the similarity of ordered lists, the default is "Spearman"
`weighted`	there are tow rank merging approaches for two cases: if weighted=FALSE, all ranked list with the same biological state are treated equally important, a simple but useful method average ranking technique is selected; otherwise, weighted=TRUE, each individual ranked lists has its own ranked weights, this takes the iterative rank-aggregating algorithm, default is TRUE.

The krubor function is used in the aggregating procedure. And the following methods are used in the implementation: a measure of the distance between two ranked lists (Spearman's Footrule), a method to merge two or more ranked lists the (Borda Merging Method), and a algorithm to obtain a single ranked list from a set of them in a hierarchical way (the Kruskal Algorithm). If choose Kendall as distance, the effectiveness of this function is certainly limited by the size of the merging problem.

SignatureDistance

#load the sample expressionSet
data(exampleSet)

## Merging each group of the ranked lists in the exampleSet with the same phenotypic data into a single PRL
MergingSet=RankMerging(exampleSet,"Spearman",weighted=TRUE)