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

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

### Description

The informative geneset which has maximum relevance with target class/trait and minimum redundancy among genes are selected based on p-values obtained from Modified Bootstrap MRMR technique.

### Usage

`mbmr.pval.cutoff(x, y, m, 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/subject/time point. Each cell entry represents the expression level of a gene in a sample/subject (row names of x as gene names/gene ids). |

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

`m` |
m is a scalar representing the size of the Modified Bootstrap Sample (i.e. Out of p samples/subjects, m samples/subjects are randomly drawn with replacement, which constitutes one Modified Bootstrap Sample). |

`s` |
s is a scalar representing the number of Modified Bootstrap samples (i.e. number of times each of the m samples/subjects will be resampled from p samples/subjects). |

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

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

### Value

The function returns a list of the genes/ geneset which are highly informative to the particular trait/condition under investigation using Modified Bootstrap MRMR technique.

### Author(s)

Samarendra Das

### Examples

```
data(rice_salt)
x=as.data.frame(rice_salt[-1,])
y=as.numeric(rice_salt[1,])
m=36
s=80
Q=0.5
n=20
mbmr.pval.cutoff(x, y, m, s, Q, n)
```

*BootMRMR*version 0.1 Index]