Design experiment

The study design, also known as ‘experimental design’, is of paramount importance for every study. It is essential to make sure that the samples collected reflect and represent the biology in question. In order to determine and examine the most influential factors that are relevant for the hypothesis under investigation, external factors that can affect the experiment have to be eliminated or identified so that they can be accounted for during data analysis.

In the study design, factors like sample size, randomisation, and storage must all be taken into account. These help guarantee reproducible and successful experiments that minimise erroneous variability. Figure 5 shows some of the many considerations that might need to be accounted for during experimental design.

Figure 5 Several important considerations when designing a metabolomics study.

Noise (or error) is an important consideration to factor in because it distorts the signals in your data. There are two types of noise:

  • Random noise – this results from contaminants and general technological limitations. It produces signal spikes and discontinuous data that could be mistaken for meaningful data.
  • Systematic noise – this results from external factors that are not relevant for the study. Baseline drift is one example of systematic noise and is a common problem in liquid chromatography-mass spectrometry (LC-MS) where the gradient of the mobile phase causes the chromatographic baseline to be irregular.