Binarization of expression data
| Category:||Data Analysis
| Supervisor:||Raivo Kolde
| Abstract:||Binarization of expression data can be beneficial if we are trying to aggregate data over various experiments from different labs on various microarray platforms. Using binarization can increase robustness by removing noise and retaining only the most essential signal. The goal is to find methods for the task and test these on actual data. The criterions for testing are classification accuracy using samples from different experiments and correlation over different microarray platforms.
* Get an overview of the normalization of Affymetrix microarrays, especially the expression detections algorithms like MAS5.0 and fRMA.