Simultaneous Hypothesis Testing Using Internal Negative Controls with An Application to Proteomics

Understand

Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (''negative result'') is expected.

• Motivated by a real proteomic dataset, we will present three promising and closely connected methods of using negative controls to assist simultaneous hypothesis testing.
• The first method uses negative controls to construct a permutation p-value for every hypothesis under investigation, and we give several sufficient conditions for such p-values to be valid and positive regression dependent on the set (PRDS) of true nulls.
• The second method uses negative controls to construct an estimate of the false discovery rate (FDR), and we give a sufficient condition under which the step-up procedure based on this estimate controls the FDR.