R: Log Likelihood ratio statistic for comparison of the splicing...

likelihood_ratio_generalized_poisson_exon_gene {GPseq} | R Documentation

## 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

### Author(s)

Sudeep Srivastava, Liang Chen

### 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");
    }

[Package GPseq version 0.5 Index]