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Fig. 8 | Clinical Proteomics

Fig. 8

From: A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy

Fig. 8

Predicted statistical power of selected proteins plotted against sample size. Given the 10 control samples, the protein SPINK1 (ranked 85th percentile across protein effect sizes) reached a power of just under 0.8 (gray horizontal line). Thus, in future experiments, a protein with similar effect size would correctly identify a statistically significant difference 80% of the time. Note that NEO1 (74th percentile effect size) shows power less than 0.6 at 10 samples and would require 14 samples to reach a power of 0.8. While this experiment combined multiple TMT plexes into a unified abundance matrix, an alternative approach to achieving sufficient sample size could be to use a single 16-plex TMT approach with a balanced experimental design (8 control + 8 test); assuming this simpler, single-plex approach, only those proteins with the highest effect sizes (e.g., protein CA2) could reach a power of 0.8 at 8 samples per group. Note that setting the power threshold to 0.8 is common but arbitrary; in practice, a different threshold may be more appropriate for a given experiment. Note also that CA2 is included for consistency with plots above; while it accurately reflects the stringency of power at lower sample sizes, a precise power calculation for this protein should incorporate the non-normality of dispersions alluded to in previous figures (see Additional file 1). In general, effect size ranges and qualitative measures of effect magnitude (e.g., small, medium, large) can inform the experimental design of untargeted experiments; however, a close examination of abundance distributions in specific proteins of interest enables more meaningful and reliable power calculations. This plot was generated with ssize-fdr R library; the calculations assume an FDR = 0.05, and pi0 = 0.7 [33]

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