Finding differentially expressed proteins: Statistical Tests

Methods for statistical hypothesis testing in Delta2D are based on state-of-the-art algorithms that are also applied in the context of DNA array analysis.

Result of applying t-tests (control vs. treated) to expression profiles. Profiles and images were clustered to better visualize differentially expressed proteins. P-values are based on 1000 permutations, false discovery rate is controlled to be 5 elements or less (with overall alpha=1%).

In the simplest case, the experiment is a comparison of two samples, e.g. diseased vs. control tissue, mutant vs. wild type etc. The task then is finding those proteins that show significant differences in expression levels. Certainly the most popular test in this area is Student's t-Test, where the null hypothesis is that the means of expression levels in samples A and B are the same. Rejecting the null hypothesis then means that the protein under test is differentially expressed.

No normal distribution of spot intensities required

One has to keep in mind that the classical Student's t-test makes the assumption that spot quantities within replicates follow a normal distribution which should be tested separately. Depending on the staining method you use and other factors, spot quantities within replicate gels may not be normally distributed. Therefore it is advisable to use one of the provided methods that are based on permutations.

In the t-Test options dialog, click on the "Between subjects" choose "p-values based on permutation" and either "Use all permutations" or "Randomly group samples" and enter 1000.

Controlling the False Discovery Rate

When applying statistical tests to 2D gel data, one is faced with the so-called multiple hypothesis testing problem: For each expression profile, a separate test is done. Each test has a certain probability of giving a false positive result, i.e. a protein spot is declared to be differentially expressed while the difference was due to pure chance. The large number of tests can produce a high number of false positives. For example, in an experiment with 2000 spots per gel, an accepted false - positive rate alpha of 5% will result in 100 proteins that are found to be "differentially expressed" although the difference is the result of mere chance.

The MeV t-test module incorporated in Delta2D provides methods to control the proportion of false positives in the result set (False Discovery Rate - FDR). Overall, the False Discovery Rate approach allows one to strike a balance between the need to find statistically valid proteins of interest and the additional cost that is associated with following up on false positives.

In the t-Test options dialog, make sure you selected "p-values based on permutations". Select "Stepdown Westfall and Young methods". Choose bounds for the number of false positive spots in the result set using the "number of false positive genes should not exceed". Alternatively, choose a bound for the proportion of false positive spots in the result set, using the other radio button and text box.