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mmgmos: R package for gamma models for oligonucleotide signal

mmgmos is an R package that estimates the expression levels and the confidence of measures for multiple arrays of the same type of Affymetrix GeneChips using the multi-chip modified gamma Model for Oligonucleotide Signal (multi-mgMOS) and the modified gamma Model for Oligonucleotide Signal (mgMOS). It is a part of PUMA project.

Why is mmgmos different from other Affy probe-level analysis methods? 

Affymetrix microarrays adopt multiple probes to measure the abundance of transcription, so it is possible to apply various statistical and probabilistic methods to provide confident gene expression results. The most popular probe-level analysis methods are statistic models which are able to calculate gene expression levels accurately. However, these methods are incapable of providing the credibility of the expression values that may be very useful for further statistical analyses. mmgmos is specifically designed to address this limitation.

There are two version of gMOS implemented in this package, modified gMOS (mgMOS) and multi-chip modified gMOS (multi-mgMOS). The original gMOS uses two gamma distributions to model Perfect Match intensities and Mismatch intensities with shared scale parameters on each chip. The mgMOS changes the scale parameters into latent variables to reflect the different binding affinity of probes within the probe-set. This modified distribution accurately captures the correlated changes in the binding affinity of probe-pairs within the probe-set. Both gMOS and mgMOS are single chip models. The multi-mgMOS is an extended version of gMOS and mgMOS. It shares the scale parameters in gamma distributions across all chips to reflect the intrinsic characteristic of probe sequences of the same type of chip. It also allows for a fraction of true signal binding to Mismatch probe. The likelihood function of all versions of gMOS can be written in closed form and the computation is therefore very fast compared with other probabilistic models.

The package mmgmos implements mgMOS in function mgmos and multi-mgMOS in function mmgmos. The fast C program donlp2 is used to optimist parameters. Both mgmos and mmgmos functions output the mean, median, standard deviation, 5%, 25%, 75% and 95% credibility intervals of the expression level for each gene.


mmgmos is free software; you can redistribute if and/or modify it under the terms of the GNU General Public License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. We do appreciate your citation of our publications or website.

A copy of user guide can be downloaded here. It is also included in the distributions.

Linux add-on package
Windows add-on package
R version requirement
Re-build for R 2.3.1 and modify to meet the new Affy import package affyio. The normalisation algorithms include mean centering on both raw and log scale, and median centering.
Adding a global scaling normalisation option.
Adding function justmmgMOS() and justmgMOS() to avoid the call of ReadAffy(). The new functions use memory more efficient and make process of large data sets possible.
Adding an option to save parameters of mgMOS in mgmos.
When \phi is unknown, set it zero.
Implementation as in Bioinformatics 21: 3637-3644

FAQ and bug report

1. What is the requirement of the installation of mmgmos?

In order to install mmgmos, you need to have R 2.2.0 and BioConductor 1.7 installed. For the installation of R and BioConductor please refer to R project and BioConductor.org respectively.

2. How to install mmgmos?

Download the add-on package from the links above and save it to your local disk. For Linux users, at the directory where it is saved type
>R CMD INSTALL mmgmos_x.x.x.tar.gz
to install it. For Windows users, use 'Install package(s) from local zip files ...' item in 'packages' menu to install.

3. What if I spot a fault in mmgmos?

We are keen for feedback on mmgmos. If you experience a problem or bug, please report it via mailto:liux@cs.man.ac.uk. Any suggestion and comment are welcome.

Back to PUMA project.