Fit a multi-group negative-binomial model to SAGE data, with Pearson estimation of the common overdispersion parameter.
fitNBP(y, group=NULL, lib.size=colSums(y), tol=1e-5, maxit=40, verbose=FALSE)numeric matrix giving counts. Rows correspond to tags (genes) and columns to SAGE libraries.
factor indicating which library belongs to each group. If NULL then one group is assumed.
vector giving total number of tags in each library.
small positive numeric tolerance to judge convergence
maximum number of iterations permitted
logical, if TRUE then iteration progress information is output.
List with components
numeric matrix of rates for each tag (gene) and each group
numeric matrix of fitted values
estimated dispersion parameter
The overdispersion parameter is estimated equating the Pearson goodness of fit to its expectation. The variance is assumed to be of the form Var(y)=mu*(1+phi*mu) where E(y)=mu and phi is the dispersion parameter. All tags are assumed to share the same dispersion.
For given dispersion, the model for each tag is a negative-binomial generalized linear model with log-link and log(lib.size) as offset.
The coefficient parametrization used is that corresponding to the formula ~0+group+offset(log(lib.size).
Except for the dispersion being common rather than genewise, the model fitted by this function is equivalent to that proposed by Lu et al (2005). The numeric algorithm used is that of alternating iterations (Smyth, 1996) using Newton's method as the outer iteration for the dispersion parameter starting at phi=0. This iteration is monotonically convergent for the dispersion.
Lu, J, Tomfohr, JK, Kepler, TB (2005). Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach. BMC Bioinformatics 6,165.
Smyth, G. K. (1996). Partitioned algorithms for maximum likelihood and other nonlinear estimation. Statistics and Computing, 6, 201-216.
# NOT RUN {
# True value for dispersion is 1/size=2/3
# Note the Pearson method tends to under-estimate the dispersion
y <- matrix(rnbinom(10*4,mu=4,size=1.5),10,4)
lib.size <- rep(50000,4)
group <- c(1,1,2,2)
fit <- fitNBP(y,group=group,lib.size=lib.size)
logratio <- fit$coef %*% c(-1,1)
# }
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