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.

A retrospective study was conducted on 134 HIV-infected females evaluated at an HIV/AIDS centre in south India to characterize their socio-demographics, HIV risk factors and initial clinical presentations. The mean age was 29 years; 81% were housewives; 95% were currently or previously married; 89% reported heterosexual sex as their only HIV risk factor; and 88% reported a history of monogamy. The majority were of reproductive age, thus the potential for vertical transmission of HIV and devastating impacts on families is alarming. Nearly half of these women initially presented asymptomatically implying that partner recruitment can enable early HIV detection. Single partner heterosexual sex with their husband was the only HIV risk factor for the majority of women. HIV prevention and intervention strategies need to focus on married, monogamous Indian women whose self-perception of HIV risk may be low, but whose risk is inextricably linked to the behaviour of their husbands.