From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome

Analysis of the lipidomic profile of vegetable oils and animal fats and changes during aging by UPLC-Q-exactive orbitrap mass spectrometry

“…The vast amount of data generated by lipidomics necessitates effective processing through pattern recognition techniques, namely unsupervised and supervised learning methods. 31,32 Unsupervised learning searches for patterns within the data, while supervised learning identifies patterns in a training set and applies them to test data. 33 PCA is an unsupervised learning method that identifies data patterns through linear dimension reduction.…”