Reading note: “Multiple Hypothesis Testing in Microarray Experiments”

METADATA

Article link: http://t.co/aOGb3hm

Who: Biostatistics researchers

What: Multiple hypothesis testing

Where: Microarray

When: Identification of differentially expressed genes

How: Different approaches, survey

Why: Many genes need to be tested simultaneously

What’s new:

Review times: 1

MAIN STRUCTURE

Problems:

  1. DNA microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously.
  2. An important and common question in DNA microarray experiments is the identification of differentially expressed genes, that is, genes whose expression levels are associated with a response or covariate of interest

Solutions:

  1. Bref: The biological question of differential expression can be restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and the responses or covariates.
  2. Details: In any testing situation, two types of errors can be committed: a false positive, or Type I error, is committed bydeclaring that a gene is differentially expressed when it is not, and a false negative, or Type II error, is committed when the test fails to identify a truly differentiallyexpressed gene.
  3. Sub-problem: When many hypotheses are tested andeach test has a specified Type I error probability, thechance of committing some Type I errors increases,often sharply, with the number of hypotheses. Special problems that arise from the multiplicity aspect include defining an appropriate Type I error rateand devising powerful multiple testing procedures that control this error rate and account for the joint distribution of the test statistics. And the proposed solutions have not always been cast in the standard statistical framework.
  4. Sub-solutions: => this article


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