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

WARNING - this function is generally not expected to be used, but is intended as an internal function. It is included for backwards compatibility with the pplr package, but may be deprecated and then hidden in future. Users should generally use `pumaDE`

instead.

This function calculates the probability of positive log-ratio (PPLR) between any two specified conditions in the input data, mean and standard deviation of gene expression level for each condition.

1 |

`e` |
a data frame containing the mean and standard deviation of gene expression levels for each condition. |

`control` |
an integer denoting the control condition. |

`experiment` |
an integer denoting the experiment condition. |

`sorted` |
Boolean. Should PPLR values be sorted by value? If FALSE, PPLR values are returned in same order as supplied. |

The input of 'e' should be a data frame comprising of 2*n components, where n is the number of conditions. The first 1,2,...,n components include the mean of gene expression values for conditions 1,2,...,n, and the n+1, n+2,...,2*n components contain the standard deviation of expression levels for condition 1,2,...,n.

The return is a data frame. The description of the components are below.

`index ` |
The original row number of genes. |

`cM ` |
The mean expression levels under control condition. |

`sM` |
The mean expression levels under experiment condition. |

`cStd` |
The standard deviation of gene expression levels under control condition. |

`sStd` |
The standard deviation of gene expression levels under experiment condition. |

`LRM` |
The mean log-ratio between control and experiment genes. |

`LRStd` |
The standard deviation of log-ratio between control and experiment genes. |

`stat` |
A statistic value which is -mean/(sqrt(2)*standard deviation). |

`PPLR` |
Probability of positive log-ratio. |

Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray

Liu,X., Milo,M., Lawrence,N.D. and Rattray,M. (2006) Probe-level variances improve accuracy in detecting differential gene expression, Bioinformatics, 22(17):2107-13.

Related methods `pumaDE`

, `bcomb`

and `hcomb`

1 2 3 4 | ```
data(exampleE)
data(exampleStd)
r<-bcomb(exampleE,exampleStd,replicates=c(1,1,1,2,2,2),method="map")
p<-pplr(r,1,2)
``` |

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