GMM

From Computational Biology Group

Revision as of 20:13, 18 December 2009 by Armand (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search


Deletion, insertion and duplication events giving rise to copy number variations (CNVs) have been found genome-wide in the humans and other species. Such genomic aberrations were identified already more than a decade ago using array-based comparative hybridization. They can also be detected using data from SNP genotyping arrays, typically by combining the intensities of the two probes for a given SNP and comparing to the same SNP from other arrays (thus deriving a copy number ratio). Significant shift from the baseline (unit ratio or zero log ratio) reflects copy number changes. Such changes can be identified in many ways, for example, one can use segmentation algorithms to partition the signal then try to classify such segments into gain, copy neutral and loss status. Yet, for large datasets, one can take advantage of the signal distribution at each SNP, and cluster each individual from the distribution into a component that would reflect a given copy number change.

We developped a Gaussian Mixture Model, which detect copy number variation from the distribution of copy number ratios. From the data, it will fit one component for each of the following copy number states: deletion, copy-neutral, 1 and 2 additional copy; with a constraint on the difference between the mixture means. Then for a given individual, it will determine the probabilities for each copy number state and compute the expected copy number (dosage).


Contents

License

The GMM algorithm is licensed under the GNU General Public License, version 2 or later. For details, see http://www.gnu.org/licenses/old-licenses/gpl-2.0.html.


Usage

The GMM can be applied to identify CNVs from any rectangular matrix of copy number ratio.


Requirements

If you have the MATLAB software, you can directly use the source code.

Otherwise, you will need to download the Matlab Component Runtime to use the executables (see Download section).


Download

Description File Name Size md5sum
MCR for 64-bit Linux MCR2007_x86_64.zip[1] 224M 451c54a811b3e01402b6a46a1b814c4d
Linux Executables GMM_CNV.zip[2] 556k bd579f39c340a50de2bb80a649643be3
Source code GMM_CNV_SOURCE.zip[3] 16k 3cb7799bf3e180b33a6742ef382b105e
Example output files GMM_CNV_outputs.zip[4] 460k 6b621a6a8e279697f610db35810777ce