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Seattle Startup Develops Regression Analysis Program for Large-scale Array Experiments


When microarray experiments involve hundreds of samples, powerful statistical approaches are going to be needed to analyze this mass of data.

Lue Ping Xhao, a professor at the Fred Hutchinson Cancer Research Center in Seattle, has come up with an alternative to cluster analysis that may prove particularly useful for large-scale clinical studies.

Xhao’s software, GenePlus, uses applied multiple regression analysis to analyze microarray data. In multiple regression analysis, different variables such as age, gender, and smoking status are looked at in relationship to the dependent variable — in this case gene expression level. This way, the causes of differences in expression levels can theoretically be more precisely pinpointed in different groups.

“It is quite different from the current state of the art,” said Xhao. “Cluster analysis is just visualizations, but what you have here is an analytical approach. You are doing specific hypothesis testing and linking up the experimental data with clinical data and time.”

Xhao and others working on GenePlus have spun out a company, Enodar Biological, to commercialize this software concept. They have received limited funding from four members of their fledgling board, but are actively seeking venture capital funding to jump-start their efforts.

In the future, the company hopes to apply the multiple regression analysis model not only to gene expression experiments, but also to proteomics and SNP analysis.

The method will allow researchers to enter dozens of covariants into the model, although the maximum number of covariants will depend on the sample size. For example, if a person enters four or five covariants into analysis of a sample size of 20, the model would break down, but a large sample of 1,000 or more could accommodate this number of variables.

As the viability of this multiple regression method is best for a larger sample size, it also relies on the prediction that microarrays will get cheaper over time, enabling researchers to conduct studies on large samples. Numerous industry observers have predicted this will happen, but it remains to be seen whether Xhao’s idea is commercially ahead of its time.

Currently, Enodar and several beta sites are testing version 1.1 of the software. The company has employed 11 programmers to work on the project, and to develop technical support for it. “We are now trying to go through the multi-stage development process, trying to make sure the software is stable,” Xhao said.

Several researchers at Fred Hutchinson have also used and developed applications for the software. James Olson, a pediatric oncologist, has used the model for studies on Huntington’s disease, and yeast researcher Linda Breeden has used the software in microarray studies to determine which yeast genes are cell-cycle dependent.

For more information about GenePlus, Zhao can be reached at [email protected]



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