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Where can I find help with designing a microarray experiment?

What is the issue?

In addition to the cost of running microarray gene expression experiments, high throughput experiments (of which microarray experiments are one type) require more careful design than traditional experiments in order to ensure that the results of the experiment are interpretable and robust.

Example problem

A frequent problem in microarray gene expression experiments is the lack of a crisp null hypothesis. Withouth a null hypothesis [definition], it is very easy to delude oneself that a given signal (e.g., a group of genes that are appear to be regulated as a function of experiment treatment) constitutes a biologically meaningful result. This is because when thousands of signals are generated, it is always possible to find variations that are not relevant to the research problem at hand.

The resources listed below should help increase the likelihood that your experiment will be productive. Nonetheless, the first thing you should do once you have an experiment design is consult a statistician before proceeding, e.g.:
  1. Stanford's Department of Statistics' free consulting service.
  2. Statisticians from the Stanford/Packard Center for Translational Research in Medicine, such as Dr. .

Selected tutorials

  • Mark Reimers' excellent Guide to Microarray Data Analysis
  • Excellent presentation on designing a microarray experiment, by Prof. Esa Uusipaikka, University of Turku.
  • Tutorial on analysis of gene expression data presented by Dr. Rainer Breitling (2005), University of Groningen.

Selected references

In PubMed: Outside of PubMed:

Finding expertise at Stanford

  • Microarray research at Stanford: Publications pertaining to microarray gene expression profiling experiments, performed by Stanford researchers and available via PubMed.
  • e-BOOK: Encyclopedia of Statistical Sciences - excellent reference source for all things statistical.

Source

Lane Librarian

Record created 6/1/2006.

ypouliot, September 24, 2009

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