Getting your microarray data is only the first part of a microarray experiment. In most cases, the purpose of a microarray experiment is to discover which genes are differentially expressed among a set of conditions or timepoints. Deciding how best to do that and what tools to use can be a challenge.
After the initial list of genes of interest is generated, further analyses will be necessary to mine as much biological information as possible from the data. Array experiments are often just the starting point for the generation of new hypotheses and novel directions for future studies.
There are several routes researchers can take in their data analysis. The CAT recommends several freeware tools (see the learning page on our site) that allow analysis of microarray data. MultiExperiment Viewer allows researchers to statistically analyze their data as well as visualize their results in heatmaps and clustergrams. GoMiner and ErmineJ are ontology mining tools that leverage the Gene Ontology Consortium's cataloging efforts to reveal underlying biological trends in array data. GenMAPP also leverages a knowledge pool of pathway information to suggest how different biological pathways have responded to your experimental conditions. With these tools, researchers can perform their own basic analysis and data mining expeditions.
Researchers can also seek to form collaborations with trained biostatisticians at the UW and the Fred Hutchinson Cancer Research Center. The CAT can provide names for potential collaborators.
A third option is a collaboration with the Bumgarner lab. Please contact the CAT manager for more details (robhall@u.washington.edu).