NetPathMiner (version 1.8.0)

pathClassifier: 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.

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.

Details

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

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, plotClusterProbs, plotClusters; 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)