Purpose of review: High-dimensional lipid analysis technologies (lipidomics) provide researchers with an opportunity to measure lipids on an unprecedented scale. They do not, however, guarantee a fast track to new knowledge. The vast amount of data produced by these platforms presents a major hurdle to assembling valid knowledge and to the discovery of mechanistic biomarkers. This review examines strategies for improving the quality of high-dimensional lipid data and streamlining data analysis to increase the value of lipidomics platforms to research and commercial applications.
Recent findings: Recent articles focus on careful study design and data analysis protocols. Authors offer detailed descriptions of study populations, analytical methods and data analysis, and highlight the use of practical data preprocessing and the incorporation of biological knowledge into data analysis.
Summary: The field is moving towards more methodical and structured approaches to biomarker identification. Experimental designs focusing on well-defined outcomes have a better chance of producing biologically relevant results. The high-dimensional lipid analysis techniques available are varied, have different strengths and weaknesses, and must be chosen carefully depending on the experimental design and application. Many techniques for data analysis are available, but the most successful are those incorporating existing biological knowledge into the statistical analysis.