GPseq (version 0.5)

likelihood_ratio_generalized_poisson_exon_gene: Log Likelihood ratio statistic for comparison of the splicing ratio of an exon in two conditions

Description

Comparing the splicing rate of Exon (count vector : z in Tissue/Condition 1, v in Tissue/Condition 2) in a given Gene ( count vector : x in Tissue/Condition 1, y in Tissue/Condition 2). The Log Likelihood ratio is distributed as a Chi-Square distribution with degrees of freedom 1. Newton Raphson method is used to estimate the parameters in the null model. The results are only applicable if the Netwon Raphson algorithm converges. The Null is that the exon has the same splicing rate in both conditions.

Usage

likelihood_ratio_generalized_poisson_exon_gene(z, theta1, lambda1, x, theta2, lambda2, v, theta3, lambda3, y, theta4, lambda4)

Arguments

z

Count vector of Exon in Tissue/Condition 1

theta1

Maximum likelihood estimator for theta when z is modeled as a Generalized Poisson random variable.

lambda1

Maximum likelihood estimator for lambda when z is modeled as a Generalized Poisson random variable.

x

Count vector of Gene in Tissue/Condition 1

theta2

Maximum likelihood estimator for theta when x is modeled as a Generalized Poisson random variable.

lambda2

Maximum likelihood estimator for theta when x is modeled as a Generalized Poisson random variable.

v

Count vector of Exon in Tissue/Condition 2

theta3

Maximum likelihood estimator for theta when v is modeled as a Generalized Poisson random variable.

lambda3

Maximum likelihood estimator for theta when v is modeled as a Generalized Poisson random variable.

y

Count vector of Gene in Tissue/Condition 2

theta4

Maximum likelihood estimator for theta when y is modeled as a Generalized Poisson random variable.

lambda4

Maximum likelihood estimator for theta when y is modeled as a Generalized Poisson random variable.

Value

mark

1 if the Newton Raphson Algorithm converges

Gptest

-2*Log Likelihood Ratio Statistic

References

Consul, P. C. (1989) Generalized Poisson Distributions: Properties and Applications. New York: Marcel Dekker. Sudeep Srivastava, Liang Chen A two-parameter generalized Poisson model to improve the analysis of RNA-Seq data Nucleic Acids Research Advance Access published July 29,2010 doi : 10.1093/nar/gkq670

See Also

generalized_poisson_likelihood , likelihood_ratio_poisson_exon_gene

Examples

set.seed(666);
z = rpois(100,1);
x = c(z,rpois(200,5));
v = rpois(100,4);
y = c(v,rpois(200,8));

outz = generalized_poisson_likelihood(z);
outx = generalized_poisson_likelihood(x);
outv = generalized_poisson_likelihood(v);
outy = generalized_poisson_likelihood(y);

if(outz$mark == 1 && outx$mark == 1 && outv$mark == 1 && outy$mark == 1)
{
  output = likelihood_ratio_generalized_poisson_exon_gene(z,outz$theta,outz$lambda,x,outx$theta,outx$lambda,v,outv$theta,outv$lambda,y,outy$theta,outy$lambda);
  cat("Converged = ",output$mark," Test Statistic = ",output$Gptest,"\n");
}