pathClassifier: HME3M Markov pathway classifier.

In aiminy/NetPathMiner: NetPathMiner for Biological Network Construction, Path Mining and Visualization

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)

aiminy/NetPathMiner documentation built on May 12, 2019, 3:38 a.m.