Description Usage Arguments Details Value References Examples

Use PLS-DA method with the normalized count data to detect the most important features (miRNAs/isomiRs) that explain better the group of samples given by the experimental design. It is a supervised clustering method with permutations to calculate the significance of the analysis.

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`ids` |
Object of class |

`group` |
Column name in |

`validation` |
Type of validation, either NULL or "learntest". Default NULL. |

`learn` |
Optional vector of indexes for a learn-set. Only used when validation="learntest". Default NULL. |

`test` |
Optional vector of indices for a test-set. Only used when validation="learntest". Default NULL |

`tol` |
Tolerance value based on maximum change of cumulative R-squared coefficient for each additional PLS component. Default tol=0.001. |

`nperm` |
Number of permutations to compute the PLD-DA p-value based on R2 magnitude. Default nperm=400. |

`refinment` |
Logical indicating whether a refined model, based on filtering out variables with low VIP values. |

`vip` |
Variance Importance in Projection threshold value when a refinement process is considered. Default vip=1.2 . |

Partial Least Squares Discriminant Analysis (PLS-DA) is a technique specifically
appropriate for analysis of high dimensionality data sets and multicollinearity
(*Perez-Enciso, 2013*). PLS-DA is a supervised method (i.e. makes use of class
labels) with the aim to provide a dimension reduction strategy in a situation
where we want to relate a binary response variable (in our case young or old
status) to a set of predictor variables. Dimensionality reduction procedure is
based on orthogonal transformations of the original variables (miRNAs/isomiRs) into a
set of linearly uncorrelated latent variables (usually termed as components)
such that maximizes the separation between the different classes in the first
few components (*Xia, 2011*). We used sum of squares captured by the model (R2) as
a goodness of fit measure.

We implemented this method using the
DiscriMiner::DiscriMiner-package into `isoPLSDA()`

function.
The output
p-value of this function will tell about the statistical
significant of the group separation using miRNA/isomiR expression data.

Read more about the parameters related to the PLS-DA directly from
`DiscriMiner::plsDA()`

function.

A base::list with the following elements: `R2Matrix`

(R-squared coefficients of the PLS model),
`components`

(of the PLS, similar to PCs in a PCA),
`vip`

(most important isomiRs/miRNAs),
`group`

(classification of the samples),
`p.value`

and `R2PermutationVector`

obtained by the permutations.

If the option `refinment`

is set to TRUE, then the following
elements will appear:
`R2RefinedMatrix`

and `componentsRefinedModel`

(R-squared coefficients
of the PLS model only using the most important miRNAs/isomiRs). As well,
`p.valRefined`

and `R2RefinedPermutationVector`

with p-value
and R2 of the
permutations where samples were randomized. And finally,
`p.valRefinedFixed`

and `R2RefinedFixedPermutationVector`

with
p-value and R2 of the
permutations where miRNAs/isomiRs were randomized.

Perez-Enciso, Miguel and Tenenhaus, Michel. Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human Genetics. 2003.

Xia, Jianguo and Wishart, David S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols. 2011.

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