So you think you can PLS-DA?

Background Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented, namely Principal Component Analysis (PCA). Results We demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector. In some cases, it outperforms PLS-DA, which is made aware of the class labels in its input. Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models encountered when analyzing bioinformatics and clinical data. Other methods were also evaluated. Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the ground truth for the feature selection was available. All the 3D figures shown in this paper as well as the supplementary ones can be viewed interactively at Conclusions Our results highlighted the strengths and weaknesses of PLS-DA in comparison with PCA for different underlying data models. ### Competing Interest Statement The authors have declared no competing interest. * PLS-DA : Partial Least-Squares Discriminant Analysis PCA : Principal Component Analysis CV : Cross-Validation PC : Principal Components sPLS-DA : Sparse Partial Least-Squares Discriminant Analysis tp : true positives tn : true negatives fp : false positives fn : false negatives SPCA : Sparse Principal Component Analysis ICA : Independent Component Analysis RLDA : Regularized Linear Discriminant Analysis SVD : Singular Value Decomposition