This class is a subclass of MyOneVersusAllGainAlgo class which implements the GeneMANIA algorithm (Mostafavi, et al, 2008). This algorithm is a ---------- algorithm where the discriminant values of the negative and positive examples are allowed to change throught the running of the algorithm. More...

#include <gain-algorithm.h>

Inheritance diagram for MyOneVersusAllGeneManiaGainAlgo:

List of all members.

Public Member Functions
	MyOneVersusAllGeneManiaGainAlgo (MyGainGraph &g, MyAnnotations &a, MyGainParams &p, Reporter &r, GeneOntology *go=NULL)
virtual void	maskNodeStates (const MyNodeIdList &nodes)
virtual void	initialiseNodeStates (const BioFunction &function, MyNodeIdList &nodesToAnnotate)
virtual void	computePredictions (const BioFunction &function, const MyNodeIdList &nodesToAnnotate, MyNodeIdList &predictedNodes)
virtual void	computeCrossValidationResults (const BioFunction &function, const MyNodeIdList &cvNodes)
	Compute results of cross validation.
virtual MyGainTriState	computeState (MyNode &node)
virtual MyGainTriState	computeStateImplicit (MyNode &node)
virtual bool	hasConverged (const MyNodeIdList &nodes)
	Check if the states of the nodes in the argument nodes have converged.
virtual void	setCrossValidationList (MyNodeIdList &nodesToAnnotate, const MyNodeIdList &cvList)
virtual void	redoBias ()
virtual string	getName ()
virtual void	run (const MyNodeIdList &nodesToAnnotate)
Public Attributes
MyNT	hypotheticalNodeBiasValue
Protected Attributes
const MyNT	_ANN_RES
const MyNT	_NOT_RES
const unsigned int	sinkSourceTest

Detailed Description

This class is a subclass of MyOneVersusAllGainAlgo class which implements the GeneMANIA algorithm (Mostafavi, et al, 2008). This algorithm is a ---------- algorithm where the discriminant values of the negative and positive examples are allowed to change throught the running of the algorithm.

Citation: Mostagavi, S, Ray, D, Warde-Farley, D, Grouios, C, & Morris, Q. (2008). GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biology, 9(S4). Link to Paper: http://www.ncbi.nlm.nih.gov/pubmed/18613948 Full Text: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447538/?tool=pubmed

Member Function Documentation

void MyOneVersusAllGeneManiaGainAlgo::computeCrossValidationResults	(	const BioFunction &	function,
		const MyNodeIdList &	cvNodes
	)		`[virtual]`

Compute results of cross validation.

After invoking crossValidate(), compute the results. Each algorithm needs to define this method, since the algorithm stores the node states.

Reimplemented from MyOneVersusAllGainAlgo.

void MyOneVersusAllGeneManiaGainAlgo::computePredictions	(	const BioFunction &	function,
		const MyNodeIdList &	nodesToAnnotate,
		MyNodeIdList &	predictedNodes
	)		`[virtual]`

Compute which genes in nodesToAnnotate MyOneVersusAllGainAlgo::run() predicts has the function. Since different algorithms may have different methods for predicting a function, this method must be implemented separately for each class.

Parameters:

nodesToAnnotate,an	instance of MyNodeIdList, the nodes that run() tried to annotate.
predictedNodes,an	instance of MyNodeIdList, the nodes that run() predicts as having the function.

Reimplemented from MyOneVersusAllGainAlgo.

MyGainTriState MyOneVersusAllGeneManiaGainAlgo::computeState ( MyNode & node ) [virtual]

In order to copmpute the state of any node in the network, GeneMANIA solves the following equation where f_{i} is the node score for gene i, y_{i} is the inital bias vale for gene i, and w_{ij} is the association weight between genes i and j. $f=min({i}{}(f_{i}-y_{i})^{2}+{i}{}{j}{}w_{ij}(f_{i}-f_{j})^{2}) This equation reduces down to $f(u)={y(u)+ w_{u}f(u)}{1+ w_{v}f(v)}$ For more information about this formula, see the paper (cited in the class documentation).

Reimplemented from MyOneVersusAllGainAlgo.

virtual string MyOneVersusAllGeneManiaGainAlgo::getName ( ) [inline, virtual]

Return the name of the algorithm.

Reimplemented from MyOneVersusAllGainAlgo.

bool MyOneVersusAllGeneManiaGainAlgo::hasConverged ( const MyNodeIdList & nodesToAnnotate ) [virtual]

Check if the states of the nodes in the argument nodes have converged.

Reimplemented from MyOneVersusAllGainAlgo.

void MyOneVersusAllGeneManiaGainAlgo::initialiseNodeStates	(	const BioFunction &	function,
		MyNodeIdList &	nodesToAnnotate
	)		`[virtual]`

********************************************* MyOneVersusAllGeneManiaGainAlgo

Reimplemented from MyOneVersusAllGainAlgo.

void MyOneVersusAllGeneManiaGainAlgo::maskNodeStates ( const MyNodeIdList & nodes ) [virtual]

For every node ID in nodes, "forget" its state information and unclamp it, so that MyOneVersusNoneGainAlgo::run() can modify the state of that node.

Parameters:

[in] nodes,the list of node whose initial state must be masked.

Reimplemented from MyOneVersusAllGainAlgo.

void MyOneVersusAllGeneManiaGainAlgo::redoBias ( ) [virtual]

Compute which genes in nodesToAnnotate MyOneVersusAllHeavisideGainAlgo::run() predicts has the function. After calling this method, invoking the report() method will print out information on all the predictions.

Parameters:

[in]	nodesToAnnotate,an	instance of MyNodeIdList, the nodes that run() tried to annotate.
[out]	predictedNodes,an	instance of MyNodeIdList, the nodes that run() predicts as having the function. For this algorithm, predictedNodes is the same as nodesToAnnotate, since the algorithm predicts all nodes as having the function but assings a confidence value for each prediction.

void MyOneVersusAllGeneManiaGainAlgo::run ( const MyNodeIdList & nodesToAnnotate ) [virtual]

This method repeatedly iterates over all the nodes in nodesToAnnotate in the order in which they occur in nodesToAnnotate. It computes the state of each node in this list. The method iterates until the states converge.

Reimplemented from MyOneVersusAllGainAlgo.

void MyOneVersusAllGeneManiaGainAlgo::setCrossValidationList	(	MyNodeIdList &	nodesToAnnotate,
		const MyNodeIdList &	cvList
	)		`[virtual]`

Given a list of unannotated nodes (nodesToAnnotate) and a list of nodes to cross validate (cvList),update nodesToAnnotate to include nodes in cvList, so that the annotation algorithm also annotates the nodes in cvList.

Warning:: Call this method only after invoking maskNodeStates().

Note:: This method is virtual since some algorithms can set nodesToAnnotate to be equal to cvList, for example, algorithms that use only one round of "guilt-by-association," where each node computes its state from the states of its neighbours.

Reimplemented from MyAbstractGainAlgo< MyGainTriStateType >.

The documentation for this class was generated from the following files:

/home/poirel/src/c++/biorithm/gain/gain-algorithm.h
/home/poirel/src/c++/biorithm/gain/gain-algorithm.C

Public Member Functions

Public Attributes

Protected Attributes

Detailed Description

Member Function Documentation