bmrmr.pval.cutoff {BootMRMR} | R Documentation |

## Selection of informative geneset based on statistical significance value using Bootstrap-MRMR technique

### Description

The informative geneset which has maximum relevance with target class/trait and minimum redundancy among genes based on statistical significance values computed from the Bootstrap-MRMR technique.

### Usage

`bmrmr.pval.cutoff(x, y, s, Q, n)`

### Arguments

`x` |
x is a N by p data frame of gene expression values where rows represent genes and columns represent samples or subjects or time point. Each cell entry represents the expression level of a gene in a sample/subject (row names of x as gene names or gene ids). |

`y` |
y is a p by 1 numeric vector with entries 1 or -1 representing sample labels, where, 1/-1 represents the sample label of subjects/ samples for stress/control condition(for two class problems). |

`s` |
s is a scalar representing the number of bootstrap generated, s must be sufficiently large (i.e. number of times bootstrap samples are generated. |

`Q` |
Q is a scalar representing the quartile value of the rankscores of genes (lies within 1/N to 1), usually the second quartile, i.e. 0.5 or third quartile i.e. 0.75 may be taken. |

`n` |
n is a scalar representing the size of the informative geneset to be obtained. |

### Value

The function returns a list of the genes/informative geneset which are highly relevant to the particular trait/condition under investigation and minimal redundant among themselves.

### Author(s)

Samarendra Das

### Examples

```
data(rice_salt)
x=as.data.frame(rice_salt[-1,])
y=as.numeric(rice_salt[1,])
s=80
Q=0.5
n=20
bmrmr.pval.cutoff(x, y, s, Q, n)
```

*BootMRMR*version 0.1 Index]