- Why is normalization necessary for microarray data?
- The linear relationship between signal intensity data and mRNA amount is extremely definite, and the range of signals that represents the linear relationship varies from experiment to experiment. Thus, precise normalization is indispensable for accurate data comparison. It is the key to accuracy of further analyses. SuperNORM is the best among existing normalization methods.
- How do you calculate background levels?
- We apply EDA methods for measuring background levels based on a three-parameter lognormal model. SuperNORM calculates a background level so that the data distribution can be presented as close as possible to lognormal distribution.
- Is this service available for any DNA chips?
- This service is available for DNA chips designed for gene expression profile analysis with 100 or more DNA spots. Insufficient number of spots affects data accuracy. We can analyze regardless of nucleic acid types, DNA chip materials, spotting methods, and detection methods. We have already had experiences in handling DNA chips produced by several companies, including Affymetrix, Agilent Technologies, Hitachi Software Engineering, and BD Biosciences, and tailor-made DNA chips by spotters.
- What results can we expect?
- This service provides z-scores by calculating numerical data. Since most of the data analysis programs handle z-scores, the results this service provides can be introduced to such programs without modification. References are not required when comparing data because z-scores calculated for the same chip can be used quantitatively. Thus you can simultaneously compare various data. The data are comparable with those computed with other methods as differences between z-scores indicate ratios.
- How do you protect our data?
- We handle your data with meticulous care. We do not use the Internet to exchange data unless required, and data calculation is done by stand alone computers. To enhance security, we recommend sending data with experimental details unrecognizable.
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