A more workable definition is that “a family is defined as a collection of simultaneous tests, where a number of hypotheses are tested simultaneously using a single dataset from a single experiment or sampling program” ( Motulsky, 2010). The next possibility would be to define the family as the tests that a researcher does over a lifetime, the main corollary of which could be that we should put more trust to the results of people who die young. This number is both unknowable and very large. The first is: what comprises a family of tests? And the second is: what to do about it? One extreme would be to define the family of tests as every test that has ever been done with a publishable result. This means that when the number of parallel tests is 13, there is a 50% chance that at least one of the statistically significant results obtained is a type I error.Īs was discussed in Chapter 3, there are two questions pertaining to multiple testing that are as much philosophical as statistical and scientific. If the tests are independent of each other, the family-wise type I error rate can be calculated exactly as 1−(1− α) c, where c is the number of tests. The problem of increasing family-wise type I error rate can occur everywhere there are multiple significance tests that are considered simultaneously. This is the multiple testing problem in NPHT: how to treat results of multiple comparisons? The probability of making one or more type I errors in a set (or family) of tests is called the family-wise type I error rate. (She also tacitly assumes that a good fraction of the H 0s that she tests is false.) If, on the other hand, she tests many different H 0s in parallel and publishes as effects those that were rejected because statistically significant, then the probability of at least one statistically significant effect being a type I error is a lot higher than 5%. Thus she can assume that when she tests only one H 0 at a time and the result is statistically significant, then her rejection of this specific H 0 is either correct in the sense that the H 0 is actually false, or an instance of bad luck but in either case a rational choice. In following the procedure of NPHT at the 0.05 significance level the researcher makes a commitment of erroneously rejecting 5% of all null hypotheses she tests. Ülo Maiväli, in Interpreting Biomedical Science, 2015 4.8 Multiple Testing in the Context of NPHT Important ‘hits’ should always be confirmed using independent technologies such as Western blot. 106 Overall, it has to be noted that a positive ‘hit’ and its associated proposed structure can only be viewed as a hypothesis. 105 In addition, presently available databases are still fraught with problems such as redundancies, inconsistencies in nomenclature, fused genes and inappropriately translated introns. 104 Alternative approaches such as ‘reversed database’ searches have been explored. The PeptideProphet is an example of an algorithm that has been developed to achieve this goal. Statistical procedures are available that estimate the rates of false positive and false negative errors. Naturally, if more peptide matches for a specific protein can be identified, then there is greater confidence in its correct identification. Thus, it is important to assess the validity of the protein assignment and to associate a probability with the identification. This is caused by the fact that several peptides may be common to more than one protein. 103 Another major source of errors is protein identification. 100 False discovery rates can be reduced by more robust experimental design, improved quality of samples and analysis, the use of technologies that allow for a direct comparison of proteomes such as DIGE and labeling 100–102 and the use of appropriate sample sizes. This means that, theoretically, 5% of protein spots may be falsely identified as different. For example, 2D gels from treatment and controls or from different treatment groups are usually compared using multiple Students’ t-tests with a significance threshold of 0.05%. Jelena Klawitter, in Biomarkers of Kidney Disease, 2011 5.7.3 Validationįalse discovery rates (false positives) are a major problem in proteomics and can be caused by: (1) the statistical process used to identify significant protein signal differences, and (2) the algorithms used for identifying the structures of such proteins.
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