Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterwards. This chapter thus covers aspects of both statistical planning and the actual analysis of quantitative proteomic experiments.

[1]  S. Dudoit,et al.  Multiple Hypothesis Testing in Microarray Experiments , 2003 .

[2]  M. Mann,et al.  Mass spectrometry–based proteomics turns quantitative , 2005, Nature chemical biology.

[3]  Jeffrey S. Morris,et al.  The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales. , 2005, Briefings in functional genomics & proteomics.

[4]  Yongyi Mao,et al.  Informatics Platform for Global Proteomic Profiling and Biomarker Discovery Using Liquid Chromatography-Tandem Mass Spectrometry*S , 2004, Molecular & Cellular Proteomics.

[5]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[6]  Steven A Carr,et al.  Protein biomarker discovery and validation: the long and uncertain path to clinical utility , 2006, Nature Biotechnology.

[7]  Pei Wang,et al.  Bioinformatics Original Paper a Suite of Algorithms for the Comprehensive Analysis of Complex Protein Mixtures Using High-resolution Lc-ms , 2022 .

[8]  Martin Eisenacher,et al.  Peek a peak: a glance at statistics for quantitative label-free proteomics , 2010, Expert review of proteomics.

[9]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[10]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[11]  Kai Stühler,et al.  Adjusted Confidence Intervals for the Expression Change of Proteins Observed in 2-Dimensional Difference Gel Electrophoresis , 2009 .

[12]  S. Hanash,et al.  Mining the plasma proteome for cancer biomarkers , 2008, Nature.

[13]  K. Resing,et al.  Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics*S , 2005, Molecular & Cellular Proteomics.

[14]  John R. Yates,et al.  The biological impact of mass-spectrometry-based proteomics , 2007, Nature.

[15]  R. Aebersold,et al.  A statistical model for identifying proteins by tandem mass spectrometry. , 2003, Analytical chemistry.

[16]  S. Dudoit,et al.  STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .

[17]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

[18]  Kai A Reidegeld,et al.  An easy‐to‐use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications , 2008, Proteomics.

[19]  D. Chelius,et al.  Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. , 2002, Journal of proteome research.

[20]  Lukas N. Mueller,et al.  An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. , 2008, Journal of proteome research.

[21]  Alexey I Nesvizhskii,et al.  Interpretation of Shotgun Proteomic Data , 2005, Molecular & Cellular Proteomics.

[22]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[23]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[24]  J. Listgarten,et al.  Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry , 2005, Molecular & Cellular Proteomics.

[25]  John K Field,et al.  Sample size determination in clinical proteomic profiling experiments using mass spectrometry for class comparison , 2009, Proteomics.