de.jstacs.sequenceScores.statisticalModels.differentiable

Class DifferentiableStatisticalModelFactory

java.lang.Object

de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModelFactory

public class DifferentiableStatisticalModelFactory
extends Object

This class allows to easily create some frequently used models. It offers only one way of creating each model and set some of the parameters to default values. If you like to set further models please check the constructors of the individual classes.

Author:

Jan Grau, Jens Keilwagen

Constructor Summary

Constructors

Constructor and Description
DifferentiableStatisticalModelFactory()

Method Summary

Modifier and Type	Method and Description
static HomogeneousMMDiffSM	createHomogeneousMarkovModel(AlphabetContainer con, double ess, int order, int priorLength) This method returns a homogeneous Markov model with user-specified order.
static BayesianNetworkDiffSM	createInhomogeneousMarkovModel(AlphabetContainer con, int length, double ess, int order) This method returns a inhomogeneous Markov model (IMM) with user-specified order.
static MarkovRandomFieldDiffSM	createMarkovRandomField(AlphabetContainer con, int length, String constraintType) This method allows to create a MarkovRandomFieldDiffSM of the specified length and with the given constraint type.
static MixtureDiffSM	createMixtureModel(DifferentiableStatisticalModel[] models) This method allows to create a MixtureDiffSM that models a mixture of individual component DifferentiableStatisticalModels.
static BayesianNetworkDiffSM	createPWM(AlphabetContainer con, int length, double ess) This method returns a position weight matrix (PWM).
static StrandDiffSM	createStrandModel(DifferentiableStatisticalModel model) This method allows to create a StrandDiffSM that allows to score binding sites on both strand of DNA.
static ExtendedZOOPSDiffSM	createZOOPS(int length, DifferentiableStatisticalModel motif, HomogeneousDiffSM bg) This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a DataSet.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

DifferentiableStatisticalModelFactory

public DifferentiableStatisticalModelFactory()

Method Detail

createPWM

public static BayesianNetworkDiffSM createPWM(AlphabetContainer con,
                                              int length,
                                              double ess)
                                       throws Exception

This method returns a position weight matrix (PWM). A PWM assumes that all positions of a sequence are statistically independent.

Parameters:

con - the AlphabetContainer of the PWM

length - the length of the PWM, i.e., the length of the sequences that can be modeled

ess - the equivalent sample size (ess) of the PWM for the BDeu prior on its parameters

Returns:

the PWM

Throws:

Exception - if the model can not be created correctly

createInhomogeneousMarkovModel

public static BayesianNetworkDiffSM createInhomogeneousMarkovModel(AlphabetContainer con,
                                                                   int length,
                                                                   double ess,
                                                                   int order)
                                                            throws Exception

This method returns a inhomogeneous Markov model (IMM) with user-specified order.

Parameters:

con - the AlphabetContainer of the IMM

length - the length of the IMM, i.e., the length of the sequences that can be modeled

ess - the equivalent sample size (ess) of the IMM for the BDeu prior on its parameters

order - the order of the IMM, i.e., the number of directly preceding random variables (=positions) that might have an influence on the probability of outcome of a random variable (=position)

Returns:

the IMM

Throws:

Exception - if the model can not be created correctly

createHomogeneousMarkovModel

public static HomogeneousMMDiffSM createHomogeneousMarkovModel(AlphabetContainer con,
                                                               double ess,
                                                               int order,
                                                               int priorLength)

This method returns a homogeneous Markov model with user-specified order.

Parameters:

con - the AlphabetContainer of the model

ess - the equivalent sample size (ess) of the class of this model, used for the BDeu prior on its parameters in conjunction with priorLength

order - the order of the model, i.e., the number of directly preceding random variables (=positions) that might have an influence on the probability of outcome of a random variable (=position)

priorLength - the a-priorily expected length of input sequences, is multiplied by ess before computing hyper-parameters

Returns:

the homogeneous Markov model

createStrandModel

public static StrandDiffSM createStrandModel(DifferentiableStatisticalModel model)
                                      throws CloneNotSupportedException,
                                             WrongAlphabetException

This method allows to create a StrandDiffSM that allows to score binding sites on both strand of DNA. The strand preferences is learned together with the supplied DifferentiableStatisticalModel.

Parameters:

model - the internally used model

Returns:

the StrandDiffSM

Throws:

CloneNotSupportedException - if the supplied DifferentiableStatisticalModel could not be cloned

WrongAlphabetException - if the AlphabetContainer of the supplied DifferentiableStatisticalModel is not AlphabetContainer.isReverseComplementable()

createMixtureModel

public static MixtureDiffSM createMixtureModel(DifferentiableStatisticalModel[] models)
                                        throws CloneNotSupportedException

This method allows to create a MixtureDiffSM that models a mixture of individual component DifferentiableStatisticalModels.

Parameters:

models - the internally used models

Returns:

the MixtureDiffSM

Throws:

CloneNotSupportedException - if the supplied DifferentiableStatisticalModels could not be cloned

createZOOPS

public static ExtendedZOOPSDiffSM createZOOPS(int length,
                                              DifferentiableStatisticalModel motif,
                                              HomogeneousDiffSM bg)
                                       throws Exception

This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a DataSet.

Parameters:

length - the length of the input Sequences (only fixed length allowed)

motif - the internally used model for the binding sites

bg - the internally used model for the flanking sequence

Returns:

the ZOOPS model

Throws:

Exception - if the model can not be created correctly

createMarkovRandomField

public static MarkovRandomFieldDiffSM createMarkovRandomField(AlphabetContainer con,
                                                              int length,
                                                              String constraintType)

This method allows to create a MarkovRandomFieldDiffSM of the specified length and with the given constraint type.

Parameters:

con - the AlphabetContainer of the MarkovRandomFieldDiffSM

length - the length of the Sequences the MarkovRandomFieldDiffSM can handle

constraintType - the constraint type, see ConstraintManager.extract(int, String)

Returns:

the new MarkovRandomFieldDiffSM

See Also:

ConstraintManager.extract(int, String)