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Power study : some definitions

Power study : some definitions. Power is the probability of identifying a differentially expressed peak as such Power depends on (among others): Sample size Differential effect Δ/σ is an appropriate ratio to describe a differential effect. Simulations.

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Power study : some definitions

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  1. Power study : some definitions • Power is the probability of identifying a differentially expressed peak as such • Power depends on (among others): • Sample size • Differential effect • Δ/σ is an appropriate ratio to describe a differential effect

  2. Simulations • Simulations are helpful in mass spectrometry • They provide a gold standard • They are “fast” and easy to generate • Simulation strategy • A virtual experiment is made of 1000 spectra • Each spectra represents an individual, with a binary state (e.g. healthy VS disease, no relapse VS relapse...)‏ • Individuals of the two groups have different concentrations for a set of proteins : the differentially expressed proteins (DEP) : 8 in our simulations • For other simulated proteins, concentrations are not different between groups (about 60 in our simulations)‏

  3. Naive power as a first look • We can adjust a logistic model predicting the binary state based on intensities read in spectra • In a first time, we only use the 8 DEP to adjust our model • We include none of the non-differentially expressed protein Power Delta/Sigma KEY MESSAGE :knowledge is indeed hidden in spectra

  4. Using the whole set of peaks • Our 8 DEP are immersed in a set containing non differentially expressed proteins (NDEP)‏ • Power is greatly influenced by the NDEP number Power #NDEP KEY MESSAGE :do not include too many genes / proteins !

  5. Impact of type 1 error rate • Type 1 error occurs when you identify as DEP a protein that is in fact not differentially expressed • Power and type 1 error rate evolve in the same direction • Type 1 error rate control strategies were developed for transcriptomic studies KEY MESSAGE :allowing some false discoveries can make you win

  6. Individual power : a perspective • We can use the probability of identifying one given DEP as a measure of power • The effect of sample size n and differential effect Δ/σ on this measure KEY MESSAGE :we need large number of individuals

  7. Family power : another perspective • Do we really want to know what it takes to identify one specific DEP ? • Do we want to identify every single DEP ? • Or is it reasonable to consider the ability to identify at least i DEP ? KEY MESSAGE :forgetting perfection can make your work valuable

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