# Estimate false positive and false negative error probabilities for direct protein interactions

### Description

Estimate false positive and false negative error probabilities for direct protein interactions using a protein interaction graph gold standard

### Usage

1 | ```
estimatePPIErrorRates(matList,GSPos=NULL,GSNeg=NULL)
``` |

### Arguments

`matList` |
A named list of list. The names corresponds to a particular publication. Each element of the top list is a list of two matrices: 1. The adjacency matrix of the positive interaction datapoints. 2. The adjacency matrix of the tested interaction datapoints. The dimension names of these two matrices should be identical (an error will be thrown if this is not the case) where the rows are indexed by the bait proteins and the columns are indexed by the prey. The adjacency matrices are 0,1 matrices where an 1 entry signifies either a positive interaction relationship or a positive tested relationship. A 0 entry is then well defined. |

`GSPos` |
A positive gold standard protein interaction graph given by its adjacency matrix. The dimension names of this matrix must be the same identifying system used for the dimension names for the matrices found within the matList parameter. An entry of one in this matrix indicates a positive interaction while an entry of zero is inconclusive. |

`GSNeg` |
A negative gold standard protein interaction graph given by its adjacency matrix. The dimension names of this matrix must be the same identifying system used for the dimension names for the matrices found within the matList parameter. An entry of one in this matrix indicates a negative interaction while an entry of zero is inconclusive. |

### Details

The model is described in the manuscript
*Estimating node degree in bait-prey graphs.*
by D. Scholtens et al.

### Value

A matrix.

The return value is an kx2 matrix where k is the length of matList. The first colum returns the estimated false positive error rates and the second column returns the estimated false negative error rates.

### Author(s)

T. Chiang and L. Wang

### References

Scholtens D, Chiang T, Huber W, Gentleman R. Estimating node
degree in bait-prey graphs. *Bioinformatics*.

### Examples

1 2 3 4 | ```
load(system.file("extdata", "intacty2hppMatrix.rda", package="ppiStats"))
load(system.file("extdata", "PPIpos", package="ppiStats"))
x = intacty2hppMatrix[c("EBI-389903","EBI-783101")]
estimatePPIErrorRates(x, GSPos=PPIpos)
``` |