Alex Lewin

Software

BGmix: mixture model for differential expression

Bayesian mixture model for differential gene expression. Three components model non-differentially expressed, over and under-expressed genes separately. A number of parametric choices are available, along with predictive model checks.

This is the code for the model in **Lewin et al. (2007), Stat. Appl. Gen. Mol. Biol.**.

LDtests: An R package providing several exact tests of Linkage Disequilibrium and Hardy-Weinberg Equilibrium.

Several exact 2-sided and 1-sided tests for LD and HWE are provided, including tests using conditional p-values proposed in Kulinskaya (2008) to overcome the problems of asymetric distributions.

This is the code for the tests used in **Kulinskaya and Lewin (2008).**.

fuzzyFDR: An R package to find fuzzy decision rules for multiple testing of hypotheses with discrete data.

Exact calculation of fuzzy decision rules for multiple
testing. Choose to control FDR (false discovery rate) using the
Benjamini and Hochberg method, or FWER (family wise error rate)
using the Bonferroni method.

This is the code for the model in **Kulinskaya and Lewin (2007).**.

BayesDE: WinBUGS code for differential gene expression

This code can be used to find differential gene expression between two experimental conditions, using a Bayesian hierarichal model. The prior on the log fold changes between the conditions is unstructured, meaning that the genes are not grouped, but ranked. Non-linear normalization between arrays is included in the model.

This is the code for the model in **Lewin et al. 2006, Biometrics**.

gmix: a semi-parametric mixture model

This model is a fully Bayesian Normal mixture model with variable number of components (programmed in Fortran using reversible jump MCMC). One mixture component has mixed mean (and possibly variance), allowing 'hypothesis testing' between a Normal null and an unknown alternative (modelled semi-parametrically using the rest of the mixture components).

This is the code for the model in **Broët et al. 2004, Bioinformatics**.

PoGO: Gene Ontology and differential expression

Software for finding statistically over-represented groups of Gene Ontology categories in microarray experiments.

This software was used for **Lewin and Grieve 2006, BMC Bioinformatics**.

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