MACE : Lossless Microarray Image Compression

Description:

The widespread adoption of microarray technology coupled with the large volume of image-based data generated per experiment underlines the importance of microarray image compression. As part of this research we have developed a simple and practical compression technique that resolves a key problem, namely the dependence of compression methods on the complex and error-prone step of spot detection. In our method, the highly skewed intensity distribution of microarray images is directly utilized to find pixels that have non-informative bits in their representation. Significant compression is obtained by the exclusion of these bits and the subsequent combination of run-length encoding and dictionary-based compression. Thus, there is neither the need for spot demarcation nor the risk of loosing biologically valuable data. Moreover, the method can be applied to microarrays with arbitrary spot layouts since it does not require spot gridding.

Access to many of the microarray images used in our experiments were provided by the Stanford Microarray Database. This research has been funded in part by the National Science Foundation through the grant IIS-0644418.

Microarray Database : http://genome-www5.stanford.edu

Publications

• R Bierman and R. Singh, "Influence of Dictionary Size on the Lossless Compression of Microarray Images", IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp. 237-242, 2007

• R. Bierman, N. Maniyar, C. Parson, and R. Singh, MACE: Lossless Compression and Analysis of Microarray Images, ACM Symposium on Applied Computing (SAC), pp. 167-172, 2006

See Also

• Designing Systems for Molecular Query-Retrieval and Molecular Informatics