Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach

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Partial least squares: a versatile tool for the analysis of high-dimensional genomic data (2006, neutral): “[ 26 ]: various binary outcomes such as (i) before v.s after chemotherapy treatment”
Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice (2010, methodology): “The application of PLS-DA to bar-coded pyrosequencing data was robust and also sensitive enough to identify specific members of gut microbiota with health-relevant responses to diet, genotype or health phenotypes, including both minor species (at 1–5”
Human Intestinal Lumen and Mucosa-Associated Microbiota in Patients with Colorectal Cancer (2012, background): “1 were considered to be important contributors to the model [5,23,24,25].”
OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification (2006, methodology): “The methodology has successfully been used in a large number of areas, including metabonomic [12] and transcriptomic [13] studies.”
A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock Omics data fusion. (2013, methodology): “As Omics ata are highly multivariate (k variables n observations), predicive models based on latent variables such as partial least squares egression (PLS) are particularly well suited to handle these data tructures for discriminant analysis [2,3].”