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                            Library & Multimedia
 So you think you can PLS-DA?
NCJ Number
              304854
Journal

*Bmc Bioinformatics* Volume: 21 Dated: 2020
Author(s)
          D. Ruiz-Perez;
                        et al
Date Published

2020
Annotation
              Since 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).
Abstract
              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 athttp://biorg.cs.fiu.edu/plsda. (Publisher Abstract Provided)
      
  
          Additional Details
Grant Number(s)
        2017-NE-BX-0001


  
  
        
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        Implementation of epigenetics into forensic science: Development of a PCR based multiplex for the simultaneous analysis of age and body fluid identification
    
  
  
Date Published: January 1, 2020
    
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