pathClassifier: HME3M Markov pathway classifier.
In NetPathMiner: NetPathMiner for Biological Network Construction, Path Mining and Visualization

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

HME3M Markov pathway classifier.

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

`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.

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

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.

Timothy Hancock and Ichigaku Takigawa

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

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

	## 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)