Integration of Omics for Data Supporting

Similar to the effects of different cell types in a sample on interpreting lipidomics data, different lipid profiles present in different cellular compartments and microdomains are also a factor making data interpretation complicated. We extensively discuss the subcellular lipidomics in Chapter 20, which may aid in the understanding of this complication and interpreting the lipidomics data better. 

In summary, the presence of sample inhomogeneity at different levels may complicate the interpretation of lipidomics data. Recognizing these complications as well as any unique features resulted from the complications should help us interpreting lipidomics data better. 

Data validation is important for lipidomics analysis. Utilization of an alternative approach to verify the obtained results is an ultimate strategy for the purpose. For example, the data obtained by shotgun lipidomics could be validated by LC-MS analysis and vice versa. Other alternative approaches, including GC-MS, NMR, TLC, or other chromatographic analysis, could also be employed under certain situations. 

Jaumot, J. (2015) Lipidomic data analysis Tutorial, practical guidelines and applications. Anal. Chim. Acta 885,1-16. 

Niemela, P.S., Castillo, S., Sysi-Aho, M., Oresic, M. (2009) Bioinformatics and computational methods for lipidomics. J. Chromatogr. B 877, 2855-2862. 

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