Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis
| Autor: | Huub C. J. Hoefsloot, Age K. Smilde, Frank Suits, Theo H. Reijmers, Gooitzen Zwanenburg, Vikram Mitra, Natalia Govorukhina, Ate G.J. van der Zee, Peter Horvatovich, Inge Westra, Rainer Bischoff |
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| Přispěvatelé: | Biosystems Data Analysis (SILS, FNWI), Analytical Biochemistry, Pharmaceutical Technology and Biopharmacy, Targeted Gynaecologic Oncology (TARGON), Medicinal Chemistry and Bioanalysis (MCB) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Proteomics
0301 basic medicine Resolution (mass spectrometry) CHROMATOGRAPHY MICROARRAY EXPERIMENTS 01 natural sciences SERUM Analytical Chemistry STATISTICAL DESIGN 03 medical and health sciences Component analysis medicine Humans TOOL Shotgun proteomics Analysis of Variance Principal Component Analysis Chromatography Chemistry 010401 analytical chemistry Proteins Fractional factorial design MASS-SPECTROMETRY Factorial experiment medicine.disease Trypsin Hemolysis 0104 chemical sciences 030104 developmental biology Analysis of variance Peptides medicine.drug |
| Zdroj: | Analytical Chemistry, 88(8), 4229-4238. American Chemical Society Analytical Chemistry, 88(8), 4229-4238. AMER CHEMICAL SOC INC |
| ISSN: | 0003-2700 |
| Popis: | Complex shotgun proteomics peptide profiles obtained in quantitative differential protein expression studies, such as in biomarker discovery, may be affected by multiple experimental factors. These preanalytical factors may affect the measured protein abundances which in turn influence the outcome of the associated statistical analysis and validation. It is therefore important to determine which factors influence the abundance of peptides in a complex proteomics experiment and to identify those peptides that are most influenced by these factors. In the current study we analyzed depleted human serum samples to evaluate experimental factors that may influence the resulting peptide profile such as the residence time in the autosampler at 4 °C, stopping or not stopping the trypsin digestion with acid, the type of blood collection tube, different hemolysis levels, differences in clotting times, the number of freeze-thaw cycles, and different trypsin/protein ratios. To this end we used a two-level fractional factorial design of resolution IV (2(IV)(7-3)). The design required analysis of 16 samples in which the main effects were not confounded by two-factor interactions. Data preprocessing using the Threshold Avoiding Proteomics Pipeline (Suits, F.; Hoekman, B.; Rosenling, T.; Bischoff, R.; Horvatovich, P. Anal. Chem. 2011, 83, 7786-7794, ref 1) produced a data-matrix containing quantitative information on 2,559 peaks. The intensity of the peaks was log-transformed, and peaks having intensities of a low t-test significance (p-value > 0.05) and a low absolute fold ratio ( |
| Databáze: | OpenAIRE |
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