Abstract
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 http://biorg.cs.fiu.edu/plsda
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.
Footnotes
Added some experiments and edited some typos. Changes were based on reviewer comments.
List of abbreviations
- 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
