Description Usage Arguments Value Author(s) References See Also Examples
This function compares the expression pattern of a given gene/exon across two given conditions or tissues. The null is that the gene is not differentially expressed across the two conditions. The input is the counts of a given gene/exon at each position in the two tissues and is modeled as a Generalized Poisson. The function outputs -2 times The Log likelihood ratio which is distributed as a Chi-Square with 1 degree of freedom. The Newton Raphson method is used to estimate the parameters in the null model and therefore the results are only applicable if the algorithm converges.
1 | likelihood_ratio_tissue_generalized_poisson(x, lambda1, theta1, y, lambda2, theta2, w)
|
x |
Count vector of Gene/Exon in condition 1 |
lambda1 |
Lambda parameter for the Generalized Poisson for the Gene/Exon counts in Condition 1 |
theta1 |
Theta parameter for the Generalized Poisson for the Gene/Exon counts in Condition 2 |
y |
Count vector of Gene/Exon in condition 2 |
lambda2 |
Lambda parameter for the Generalized Poisson for the Gene/Exon counts in condition 1 |
theta2 |
Theta parameter for the Generalized Poisson for the Gene/Exon counts in condition 2 |
w |
Normalizing factor of condition 1 compared to condition 2 |
~Describe the value returned If it is a LIST, use
mark |
1 if the Newton Raphson algorithm converges. The test statistic is only valid if mark = 1 |
Gptest |
Chi Square Statistic = -2 * Log Likelihood Ratio |
Sudeep Srivastava, Liang Chen
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
generalized_poisson_likelihood
, likelihood_ratio_tissue_poisson
1 2 3 4 5 6 7 8 9 10 11 | set.seed(666);
x <- rpois(100,1);
y <- rpois(100,5);
outx = generalized_poisson_likelihood(x);
outy = generalized_poisson_likelihood(y);
w = runif(1,0,1);
output = likelihood_ratio_tissue_generalized_poisson(x,outx$lambda,outx$theta,y,outy$lambda,outy$theta,w);
cat("Converged = ",output$mark," Test Statistic = ",output$Gptest,"\n");
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