Uses and misuses of the fudge factor in quantitative discovery proteomics.
Selecting proteins with significant differential abundance is the cornerstone of many relative quantitative proteomics experiments. To do so, a trade-off between p-value thresholding and fold-change thresholding can be performed because of a specific parameter, named fudge factor, and classically noted s0 . We have observed that this fudge factor is routinely turned away from its original (and statistically valid) use, leading to important distortion in the distribution of p-values, jeopardizing the protein differential analysis, as well as the subsequent biological conclusion. In this article, we provide a comprehensive viewpoint on this issue, as well as some guidelines to circumvent it.
BIG-BGE (Université Grenoble-Alpes, CNRS, CEA, INSERM), Grenoble, France.
Quentin
Q
Giai Gianetto
BIG-BGE (Université Grenoble-Alpes, CNRS, CEA, INSERM), Grenoble, France.
Yohann
Y
Couté
BIG-BGE (Université Grenoble-Alpes, CNRS, CEA, INSERM), Grenoble, France.
Christophe
C
Bruley
BIG-BGE (Université Grenoble-Alpes, CNRS, CEA, INSERM), Grenoble, France.
Thomas
T
Burger
D000465:Algorithms; D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D066264:Datasets as Topic; D017510:Protein Folding; D011506:Proteins / Q000302:isolation & purification*; D020543:Proteome / Q000302:isolation & purification*; D040901:Proteomics / Q000379:methods / Q000706:statistics & numerical data*
D011506:Proteins; D020543:Proteome
Bioinformatics; Differential abundance; Discovery proteomics; Statistical analysis