S2N: calculate signal to noise ratio for microRNAs(miRNAs)

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/S2N.R

Description

This function calculate the signal to noise ratio for miRNAs for the actual phenotype and also random permutations

Usage

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S2N(A, class.labels, miR.labels, nperm )

Arguments

A

Matrix of miRNAs expression values (rows are miRNAs, columns are samples)

class.labels

Phenotype of class disticntion of interest. A vector of binary labels having first the 1's and then the 0's

miR.labels

miRNA labels,Vector of probe ids or accession numbers for the rows of the expression matrix

nperm

Number of random permutations to perform

Details

The function uses matrix operations to implement the signal to noise calculation in stages and achieves fast execution speed.

Value

s2n.matrix

Matrix with random permuted or bootstraps signal to noise ratios (rows are miRNAs, columns are permutations or bootstrap subsamplings

obs.s2n.matrix

Matrix with observed signal to noise ratios (rows are miRNAs, columns are boostraps subsamplings. If fraction is set to 1.0 then all the columns have the same values

Author(s)

Junwei Han[email protected],Siyao Liu [email protected]

References

Subramanian A, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102(43):15545-15550.

See Also

MirSEA

Examples

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##Matrix of miRNAs expression values
A<-matrix(runif(200),10,20)
##class.labels("0" or "1")
a1<-rep(0,20)
a1[sample(1:20,5)]=1
a1<-sort(a1,decreasing=FALSE)
#calculate signal to noise ratio for example data
M1<-S2N(A, class.labels=a1, miR.labels=seq(1,10), nperm=100)
#show actual results for top five in the matrix 
M1$obs.s2n.matrix[1:5,1]
#show permutation results
M1$s2n.matrix[1:5,1:5]

Example output

[1]  0.38388325 -0.27053308 -0.08230109  0.27286654  0.06700507
           [,1]        [,2]       [,3]       [,4]        [,5]
[1,] -0.1105461  0.23298289 -0.3251953  0.1284537 -0.03900988
[2,] -0.3787393  0.10825862 -0.3674985  0.1753063  0.04971895
[3,]  0.1904603 -0.08379877 -0.7540392 -0.3105341  0.61564517
[4,]  0.1407321 -0.17280146 -0.2477098 -0.1471845 -0.06029557
[5,]  0.5531745 -0.19160489 -0.1255637 -0.1250186  0.55880023

MiRSEA documentation built on May 29, 2017, 2:58 p.m.