Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10231953 | Computational Biology and Chemistry | 2005 | 18 Pages |
Abstract
In this note we intend to explain basic principles of five (5) major groups of analytical techniques used in studies of microarray data and their interpretation: the principal component analysis (PCA), the independent component analysis (ICA), the t-test, the analysis of variance (ANOVA), and self organizing maps (SOM). We discuss answers to selected practical questions related to the analysis of microarray data. We also take a closer look at the experimental setup and the rules, which have to be observed in order to exploit microarrays efficiently. Finally, we discuss in detail the scope and limitations of microarray-based methods. We emphasize the fact that no amount of statistical analysis can compensate for (or replace) a well thought through experimental setup. We conclude that microarrays are indeed useful tools in life sciences but by no means should they be expected to generate complete answers to complex biological questions. We argue that even well posed questions, formulated within a microarray-specific terminology, cannot be completely answered with the use of microarray analyses alone.
Keywords
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Bioengineering
Authors
Alessandra Riva, Anne-Sophie Carpentier, Bruno Torrésani, Alain Hénaut,