fgga {fgga} | R Documentation

## Factor Graph GO Annotation model

### Description

A hierarchical graph-based machine learning model for the consistent GO annotation of protein coding genes.

### Usage

    fgga(graphGO, tableGOs, dxCharacterized, dxTestCharacterized,
        kFold, kernelSVM, tmax, epsilon)

### Arguments

`graphGO` | A graphNEL graph with ‘m’ GO node labels.
`tableGOs` | A binary matrix with ‘n’ proteins (rows) by ‘m’ GO node labels (columns).
`dxCharacterized` | A data frame with ‘n’ proteins (rows) by ‘f’ features (columns).
`dxTestCharacterized` | A data frame with ‘k’ proteins (rows) by ‘f’ features (columns).
`kFold` | An integer for the number of folds.
`kernelSVM` | The kernel used to calculate the variance (default: radial).
`tmax` | An integer indicating the maximum number of iterations (default: 200).
`epsilon` | An integer that represents the convergence criteria (default: 0.001).

### Details

The FGGA model is built in two main steps. In the first step, a core Factor Graph (FG) modeling hidden GO-term predictions and relationships is created. In the second step, the FG is enriched with nodes modeling observable GO-term predictions issued by binary SVM classifiers. In addition, probabilistic constraints modeling learning gaps between hidden and observable GO-term predictions are introduced. These gaps are assumed to be independent among GO-terms, locally additive with respect to observed predictions, and zero-mean Gaussian. FGGA predictions are issued by the native iterative message passing algorithm of factor graphs.

### Value

A named matrix with ‘k’ protein coding genes (rows) by ‘m’ GO node labels (columns) where each element indicates a probabilistic prediction value.

### Author(s)

Flavio E. Spetale and Elizabeth Tapia <spetale@cifasis-conicet.gov.ar>

### References

Spetale F.E., Tapia E., Krsticevic F., Roda F. and Bulacio P. “A Factor Graph Approach to Automated GO Annotation”. PLoS ONE 11(1): e0146986, 2016.

Spetale Flavio E., Arce D., Krsticevic F., Bulacio P. and Tapia E. “Consistent prediction of GO protein localization”. Scientific Report 7787(8), 2018

### See Also

`fgga2bipartite`, `sumProduct`, `svmGO`

### Examples

    data(CfData)

    mygraphGO <- as(CfData[["graphCfGO"]], "graphNEL")

    dxCfTestCharacterized <- CfData[["dxCf"]][CfData[["indexGO"]]$indexTest[1:2], ]

    myTableGO <- CfData[["tableCfGO"]][
                        CfData[["indexGO"]]$indexTrain[1:300], ]

    dataTrain <- CfData[["dxCf"]][
                        CfData[["indexGO"]]$indexTrain[1:300], ]

    fggaResults <- fgga(graphGO = mygraphGO,
                    tableGOs = myTableGO, dxCharacterized = dataTrain,
                    dxTestCharacterized = dxCfTestCharacterized, kFold = 2,
                    tmax = 50, epsilon = 0.05)

[Package fgga version 1.2.0 Index]