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

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

1 | ```
S2N(A, class.labels, miR.labels, nperm )
``` |

`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 |

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

`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 |

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

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.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
##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]
``` |

```
[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.

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