Proteomics of Cellular Response to Stress: Taking Control of False Positive Results

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Ildar T. Gabdrakhmanov¹, Mikhail V. Gorshkov^2,3, and Irina A. Tarasova^3,a*

¹Skolkovo Institute of Science and Technology, 121205 Skolkovo, Moscow, Russia

²Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Moscow Region, Russia

³Talrose Institute for Energy Problems of Chemical Physics, Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia

^* To whom correspondence should be addressed.

Received August 9, 2020; Revised November 3, 2020; Accepted December 1, 2020

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One of the main goals of quantitative proteomics is molecular profiling of cellular response to stress at the protein level. To perform this profiling, statistical analysis of experimental data involves multiple testing of a hypothesis about the equality of protein concentrations between the cells under normal and stress conditions. This analysis is then associated with the multiple testing problem dealing with the increased chance of obtaining false positive results. A number of solutions to this problem are known, yet, they may lead to the loss of potentially important biological information when applied with commonly accepted thresholds of statistical significance. Using the proteomic data obtained earlier for the yeast samples containing proteins at known concentrations and the biological models of early and late cellular responses to stress, we analyzed dependences of distributions of false positive and false negative rates on the protein fold changes and thresholds of statistical significance. Based on the analysis of the density of data points in the volcano plots, Benjamini–Hochberg method, and gene ontology analysis, visual approach for optimization of the statistical threshold and selection of the differentially regulated proteins has been suggested, which could be useful for researchers working in the field of quantitative proteomics.

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KEY WORDS: proteomics, bioinformatics, cell response, mass spectrometry

DOI: 10.1134/S0006297921030093

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