Class State

java.lang.Object
  State

public class State
extends java.lang.Object

Holds the state name and the associated ratios expected

Eg: In a het deletion, we expect [observed coverage]/[average coverage in other samples]~1/2 and [observed heterozygosity]/[average heterozygosity in other samples]~0

Also contains methods to compute the emission and transition probabilities.

Author:

arthiramachandran

Field Summary

static int	computeAtRunTime
static int	DOUBLE_INSERT
(package private) int	E_LengthOfState
static int	HETEROZYGOUS_DELETE
static int	HOMOZYGOUS_DELETE
static int	INSERTION
static java.util.HashMap<java.lang.Integer,java.lang.Double>	LOG_FACTORIAL
static int	NORMAL
(package private) static double	rateOfOccurenceOfCNV
(package private) double	rpkmRatio
(package private) double	snpRatio
(package private) int	stateName
(package private) static java.util.HashMap<State,java.util.HashMap<State,java.lang.Double>>	transitionProbabilities

Constructor Summary

State()

Method Summary

boolean	equals(java.lang.Object aThat)
static double	getEmissionProbability(double observedFPKM, double observedSNPs, double expectedFPKM, double expectedSNPs, double stdDevFPKM, double stdDevSNPs, double normalizationFPKM, double normalizationSNPs, State s) To remove
static java.lang.Double	getEmissionProbability(Exon exon, Exon expected, Exon stdDev, State s) To remove
static double	getLogEmissionProbability(Exon exon, Exon expected, Exon stdDev, State s, int nSamples, cern.jet.random.Gamma gamma, double gammaK, double gammaTheta, double gammaSNPsK, double gammaSNPsTheta) Calculates the emission probability for the observation at a particular exon, in state s
java.lang.String	getStateName() For pretty-printing and for serving coffee with the code
static double	getTransitionProbability(State s1, State s2, int lengthOfIntron, int lengthOfExon) Gets the transition probability from state s1 -> state s2
static java.util.HashMap<java.lang.String,State>	initializeStates(java.lang.String parameterFile) Initializes all the states with the values in the parameter file
private static double	logFactorial(int number) Calculated the log of the factorial of the number
private static double	logGamma(double x, double k, double theta) Calculates the log of the pdf of a gamma distribution
private static double	logPoisson(double x, double lambda) Calculates log of pdf of Poisson distribution
java.lang.String	toString()
private static double	updateGammaK(double ratio, int nSamples, double gammaK, double expectedFPKM) Updates the k for the Gamma prior (computes the posterior hyperparameter)
private static double	updateGammaTheta(int nSamples, double gammaTheta) Updates the theta for the Gamma prior (computes the posterior hyperparameter)

Methods inherited from class java.lang.Object

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

Field Detail

stateName

int stateName

rpkmRatio

double rpkmRatio

snpRatio

double snpRatio

E_LengthOfState

int E_LengthOfState

rateOfOccurenceOfCNV

static double rateOfOccurenceOfCNV

transitionProbabilities

static java.util.HashMap<State,java.util.HashMap<State,java.lang.Double>> transitionProbabilities

NORMAL

public static final int NORMAL

See Also:

Constant Field Values

HOMOZYGOUS_DELETE

public static final int HOMOZYGOUS_DELETE

See Also:

Constant Field Values

HETEROZYGOUS_DELETE

public static final int HETEROZYGOUS_DELETE

See Also:

Constant Field Values

INSERTION

public static final int INSERTION

See Also:

Constant Field Values

DOUBLE_INSERT

public static final int DOUBLE_INSERT

See Also:

Constant Field Values

computeAtRunTime

public static final int computeAtRunTime

See Also:

Constant Field Values

LOG_FACTORIAL

public static java.util.HashMap<java.lang.Integer,java.lang.Double> LOG_FACTORIAL

Constructor Detail

State

public State()

Method Detail

getTransitionProbability

public static double getTransitionProbability(State s1,
                                              State s2,
                                              int lengthOfIntron,
                                              int lengthOfExon)

Gets the transition probability from state s1 -> state s2

For CNV state -> normal state, transition probability is dependent on length of intron

Parameters:

s1 - State transitioning from

s2 - State transitioning to

lengthOfIntron - length of the intron

lengthOfExon - length of the exon

Returns:

transition probability from s1 -> s2

initializeStates

public static java.util.HashMap<java.lang.String,State> initializeStates(java.lang.String parameterFile)

Initializes all the states with the values in the parameter file

Parameters:

parameterFile - name of parameter file

Returns:

HashMap of states indexed by their name

getStateName

public java.lang.String getStateName()

For pretty-printing and for serving coffee with the code

Returns:

String character describing the name of the state

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

equals

public boolean equals(java.lang.Object aThat)

Overrides:

equals in class java.lang.Object

getEmissionProbability

public static double getEmissionProbability(double observedFPKM,
                                            double observedSNPs,
                                            double expectedFPKM,
                                            double expectedSNPs,
                                            double stdDevFPKM,
                                            double stdDevSNPs,
                                            double normalizationFPKM,
                                            double normalizationSNPs,
                                            State s)

To remove

getEmissionProbability

public static java.lang.Double getEmissionProbability(Exon exon,
                                                      Exon expected,
                                                      Exon stdDev,
                                                      State s)

To remove

logFactorial

private static double logFactorial(int number)

Calculated the log of the factorial of the number

Parameters:

number -

Returns:

log(number!)

getLogEmissionProbability

public static double getLogEmissionProbability(Exon exon,
                                               Exon expected,
                                               Exon stdDev,
                                               State s,
                                               int nSamples,
                                               cern.jet.random.Gamma gamma,
                                               double gammaK,
                                               double gammaTheta,
                                               double gammaSNPsK,
                                               double gammaSNPsTheta)

Calculates the emission probability for the observation at a particular exon, in state s

Parameters:

exon - exon for which emission probability is computed

expected - expected observations for that exon (based on other samples)

stdDev - std deviations for that exon (based on other samples)

s - state

nSamples - number of samples

gamma - to remove

gammaK - gamma parameter k of the coverage

gammaTheta - gamma parameter Theta of the coverage

gammaSNPsK - gamma parameter k of the heterozygosity

gammaSNPsTheta - gamma parameter Theta of the heterozygosity

Returns:

log(emission probability(expected | s))

logGamma

private static double logGamma(double x,
                               double k,
                               double theta)

Calculates the log of the pdf of a gamma distribution

Parameters:

x - - observation

k - - parameter of Gamma distribution

theta - - parameter of Gamma distribution

Returns:

log(pdf(Gamma(k, Theta)))

logPoisson

private static double logPoisson(double x,
                                 double lambda)

Calculates log of pdf of Poisson distribution

Parameters:

x - - observed

lambda - - parameter of Poisson distribution

Returns:

log(pdf(Poisson(lambda)))

updateGammaTheta

private static double updateGammaTheta(int nSamples,
                                       double gammaTheta)

Updates the theta for the Gamma prior (computes the posterior hyperparameter)

Parameters:

nSamples - number of samples

gammaTheta - original Theta value

Returns:

new Theta value

updateGammaK

private static double updateGammaK(double ratio,
                                   int nSamples,
                                   double gammaK,
                                   double expectedFPKM)

Updates the k for the Gamma prior (computes the posterior hyperparameter)

Parameters:

ratio - ratio of observed to expected in the particular state

nSamples - number of samples

gammaK - original k value

expectedFPKM - expected coverage

Returns:

new k value