varianceGO {fgga} | R Documentation |
varianceGO estimates the variance of gaussian distributions modeling the additive learning noise that corrupts ideal GO-term predictions.
varianceGO(tableGOs, dxCharacterized, kFold, graphGO, rootNode, kernelSVM = "radial")
tableGOs |
A binary matrix with ‘n’ protein coding genes (rows) by ‘m’ GO node labels (columns). |
dxCharacterized |
A data frame with ‘n’ protein coding genes (rows) by ‘f’ features (columns). |
kFold |
An integer for the number of folds. |
graphGO |
A graphNEL graph with ‘m’ GO node labels. |
rootNode |
A character indicating the root of the graph. |
kernelSVM |
The kernel used to calculate the variance (default: radial). |
Under the assumption of symmetrical (Gaussian) conditional probability distributions for observable variable node predictions y_i over a hidden variable node annotations x_i, variances η_i can be estimated using a validation dataset of positively annotated samples.
A vector named with the variance of each GO node.
Flavio E. Spetale and Javier Murillo <spetale@cifasis-conicet.gov.ar>
Spetale FE, Tapia E, Krsticevic F, Roda F, Bulacio P (2016). A Factor Graph Approach to Automated GO Annotation. PLOS ONE 11(1): e0146986
data(CfData) mygraphGO <- as(CfData[["graphCfGO"]], "graphNEL") rootGO <- leaves(mygraphGO, "in") mygraphGO <- subGraph(c("GO:0140110", "GO:0098772", "GO:0003674"), mygraphGO) myTableGO <- CfData[["tableCfGO"]][ CfData[["indexGO"]]$indexTrain, c("GO:0140110", "GO:0098772", "GO:0003674")] varianceGOs <- varianceGO(tableGOs = myTableGO, dxCharacterized = CfData[["dxCf"]], kFold = 2, graphGO = mygraphGO, rootNode = rootGO, kernelSVM = "radial")