Final answer:
PCA simplifies complex high-dimensional data by transforming it into principal components that capture variance, which is useful for finding patterns in multivariate datasets such as protein abundances in biological samples.
Step-by-step explanation:
Principal component analysis (PCA) is a statistical method used to simplify the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the original data into a new set of variables, the principal components, which are uncorrelated and which are ordered by the amount of variance they capture from the original data set. The process of PCA involves eigen-based techniques to find the directions (principal components) that maximize variance in the data.
In the context of proteomics, PCA can be utilized to discern systematic differences in protein composition between groups, such as control and treated samples, by clustering the data. In the study described, PCA was used to separate treated and control tomato radicle samples based on protein abundance levels, successfully showing a systematic difference in protein abundances due to treatment. The PCA also revealed the variance in the data with the first principal component explaining a large majority of the variability (94.87%) and the second much less (2.7%), indicating that the primary differences are substantial and mostly captured by the first principal component.