Riva Alessandra, Carpentier Anne-Sophie, Torrésani Bruno, Hénaut Alain
Laboratoire Génome et Informatique UMR 8116 Tour Evry2, 523 Place des Terrasses, 91034 Evry Cedex, France.
Comput Biol Chem. 2005 Oct;29(5):319-36. doi: 10.1016/j.compbiolchem.2005.08.006. Epub 2005 Oct 10.
Microarrays are becoming a ubiquitous tool of research in life sciences. However, the working principles of microarray-based methodologies are often misunderstood or apparently ignored by the researchers who actually perform and interpret experiments. This in turn seems to lead to a common over-expectation regarding the explanatory and/or knowledge-generating power of microarray analyses. 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.
微阵列正成为生命科学研究中一种普遍使用的工具。然而，基于微阵列的方法的工作原理常常被实际进行和解释实验的研究人员误解或明显忽视。这反过来似乎导致了对微阵列分析的解释和/或知识生成能力的普遍过度期望。在本笔记中，我们打算解释用于微阵列数据研究及其解释的五（5）大类分析技术的基本原理：主成分分析（PCA）、独立成分分析（ICA）、t检验、方差分析（ANOVA）和自组织映射（SOM）。我们讨论了与微阵列数据分析相关的一些实际问题的答案。我们还仔细研究了实验设置以及为了有效利用微阵列必须遵守的规则。最后，我们详细讨论了基于微阵列的方法的范围和局限性。我们强调这样一个事实，即再多的统计分析也无法弥补（或取代）精心设计的实验设置。我们得出结论，微阵列确实是生命科学中有用的工具，但绝不能期望它们能为复杂的生物学问题提供完整答案。我们认为，即使是用微阵列特定术语提出的恰当问题，仅使用微阵列分析也无法完全回答。
