Optimization of Statistical Methods Impact on Quantitative Proteomics Data

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• 作者：Anna Pursiheimo ; Anni P. Vehmas ; Saira Afzal ; Tomi Suomi ; Thaman Chand ; Leena Strauss ; Matti Poutanen ; Anne Rokka ; Garry L. Corthals ; Laura L. Elo
• 刊名：Journal of Proteome Research
• 出版年：2015
• 出版时间：October 2, 2015
• 年：2015
• 卷：14
• 期：10
• 页码：4118-4126
• 全文大小：404K
• ISSN：1535-3907

文摘

As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards as well as 鈥渞eal鈥?experiments where differences in protein abundance are not known a priori. Our results suggest that data-driven reproducibility-optimization can consistently produce reliable differential expression rankings for label-free proteome tools and are straightforward in their application.