Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics

Area of Science:

• Proteomics
• Biomarker Discovery
• Analytical Chemistry

Background:

• Mass spectrometry-based proteomics is vital for identifying disease biomarker candidates.
• Experimental reproducibility is essential for reliable quantitative profiling in proteomics.
• Variations in sample handling and LC-MS conditions hinder data reproducibility.

Purpose of the Study:

• To develop an automated software tool, Chaorder, for assessing the reproducibility of large-scale LC-MS experiments.
• To provide a quantitative definition of similarity between LC-MS experiments.
• To establish a basis for systematic quality control and data comparability.

Main Methods:

• Development of Chaorder, a fully automatic software tool.
• Formal, quantitative definition of similarity between LC-MS experiments.
• Application of Chaorder to diverse datasets from multiple laboratories and instruments.

Main Results:

• Chaorder effectively assesses experimental reproducibility in large-scale LC-MS data.
• Analysis revealed biases stemming from sample processing, experimental protocols, and instrument selection.
• Simple bias correction methods, like randomizing run order, showed limited impact on statistical power for biomarker discovery.

Conclusions:

• Chaorder enables systematic quality control for mass spectrometry data.
• Understanding and mitigating biases are critical for reliable biomarker discovery.
• Further research is needed to optimize methods for improving statistical power in proteomics studies.