E-learning in analysis of genomic and proteomic data 2. Data analysis 2.3. Analysis of high-density proteomic data 2.3.2. MASS spectrometry General conclusions

In proteomics, careful attention to study design is essential to avoid bias and improve efficiency. Even the most sophisticated of statistical methods will not usually be able to compensate for poor study design. Proper understanding of (sources of) variation is needed, so that extraneous variation can be minimized. Complex analyses should be preceded by more simple descriptive analyses to identify the key features of the data.