rankGenes: Rank genes by the gene expression intensities
In singscore: Rank-based single-sample gene set scoring method

Description Usage Arguments Value See Also Examples

The rankGenes function is a generic function that can deal with mutilple types of inputs. Given a matrix of gene expression that has samples in columns, genes in rows, and values being gene expression intensity,rankGenes ranks gene expression intensities in each sample.

It can also work with S4 objects that have gene expression matrix as a component (i.e ExpressionSet, DGEList,SummarizedExperiment). It calls the rank function in the base package which ranks the gene expression matrix by its absolute expression level. If the input is S4 object of DGEList, ExpressionSet, or SummarizedExperiment, it will extract the gene expression matrix from the object and rank the genes. The default 'tiesMethod' is set to 'min'.

rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'matrix'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'data.frame'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'DGEList'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'ExpressionSet'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'SummarizedExperiment'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

`expreMatrix`	matrix, data.frame, ExpressionSet, DGEList or SummarizedExperiment storing gene expression measurements
`tiesMethod`	character, indicating what method to use when dealing with ties
`stableGenes`	character, containing a list of stable genes to be used to rank genes using expression of stable genes. This is required when using the stable genes dependent version of singscore (see details in `simpleScore`). Stable genes for solid cancers (carcinomas) and blood transcriptomes can be obtained using the `getStableGenes` function

The ranked gene expression matrix that has samples in columns and genes in rows. Unit normalised ranks are returned if data is ranked using stable genes

getStableGenes, simpleScore, rank, "ExpressionSet", "SummarizedExperiment", "DGEList"

rankGenes(toy_expr_se) # toy_expr_se is a gene expression dataset

# ExpressionSet object
emat <- SummarizedExperiment::assay(toy_expr_se)
e <- Biobase::ExpressionSet(assayData = as.matrix(emat))
rankGenes(e)

#scoring using the stable version of singscore
rankGenes(e, stableGenes = c('2', '20', '25'))

## Not run: 
#for real cancer or blood datasets, use getStableGenes()
rankGenes(cancer_expr, stableGenes = getStableGenes(5))
rankGenes(blood_expr, stableGenes = getStableGenes(5, type = 'blood'))

## End(Not run)