pathClassifier {NetPathMiner} | R Documentation |
HME3M Markov pathway classifier.
pathClassifier(paths, target.class, M, alpha = 1, lambda = 2, hme3miter = 100, plriter = 1, init = "random")
paths |
The training paths computed by |
target.class |
he label of the targe class to be classified. This label must be present
as a label within the |
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 |
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)