pathClassifier {NetPathMiner} | R Documentation

## HME3M Markov pathway classifier.

### Description

HME3M Markov pathway classifier.

### Usage

    pathClassifier(paths, target.class, M, alpha = 1, lambda = 2,
      hme3miter = 100, plriter = 1, init = "random")

### Arguments

`paths` | The training paths computed by `pathsToBinary`
`target.class` | he label of the targe class to be classified. This label must be present as a label within the `paths\$y` object
`M` | Number of components within the paths to be extracted.
`alpha` | The PLR learning rate. (between 0 and 1).
`lambda` | The PLR regularization parameter. (between 0 and 2)
`hme3miter` | Maximum number of HME3M iterations. It will stop when likelihood change is < 0.001.
`plriter` | Maximum number of PLR iteractions. It will stop when likelihood change is < 0.001.
`init` | Specify whether to initialize the HME3M responsibilities with the 3M model - random is recommended.

### Details

Take care with selection of lambda and alpha - make sure you check that the likelihood is always increasing.

### Value

A list with the following elements. A list with the following values

`h` | A dataframe with the EM responsibilities.
`theta` | A dataframe with the Markov parameters for each component.
`beta` | A dataframe with the PLR coefficients for each component.
`proportions` | The probability of each HME3M component.
`posterior.probs` | The HME3M posterior probability.
`likelihood` | The likelihood convergence history.
`plrplr` | The posterior predictions from each components PLR model.
`path.probabilities` | The 3M probabilities for each path belonging to each component.
`params` | The parameters used to build the model.
`y` | The binary response variable used by HME3M. A 1 indicates the location of the target.class labels in `paths\$y`
`perf` | The training set ROC curve AUC.
`label` | The HME3M predicted label for each path.
`component` | The HME3M component assignment for each path.

### Author(s)

Timothy Hancock and Ichigaku Takigawa

### References

Hancock, Timothy, and Mamitsuka, Hiroshi: A Markov Classification Model for Metabolic Pathways, Workshop on Algorithms in Bioinformatics (WABI) , 2009

Hancock, Timothy, and Mamitsuka, Hiroshi: A Markov Classification Model for Metabolic Pathways, Algorithms for Molecular Biology 2010

### See Also

Other Path clustering & classification methods: `pathCluster`, `pathsToBinary`, `plotClassifierROC`, `plotClusterMatrix`, `plotPathClassifier`, `plotPathCluster`, `predictPathClassifier`, `predictPathCluster`

### Examples

    	## Prepare a weighted reaction network.
    	## Conver a metabolic network to a reaction network.
     data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.
     rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)

    	## Assign edge weights based on Affymetrix attributes and microarray dataset.
     # Calculate Pearson's correlation.
    	data(ex_microarray)	# Part of ALL dataset.
    	rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph,
    		weight.method = "cor", use.attr="miriam.uniprot",
    		y=factor(colnames(ex_microarray)), bootstrap = FALSE)

    	## Get ranked paths using probabilistic shortest paths.
     ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
    					K=20, minPathSize=6)

    	## Convert paths to binary matrix.
    	ybinpaths <- pathsToBinary(ranked.p)
    	p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)

    	## Contingency table of classification performance
    	table(ybinpaths$y,p.class$label)

    	## Plotting the classifier results.
    	plotClassifierROC(p.class)
    	plotClusters(ybinpaths, p.class)

[Package NetPathMiner version 1.16.0 Index]