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Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object

Source: R/prefiltering.R
perf_splsda.Rd

Performs cross-validation for a PLS-DA run (implemented in the mixOmics package) on an omics dataset from a MultiDataSet object. This allows to estimate the optimal number of latent components to construct. This is intended for feature preselection in the omics dataset (see examples below).

Usage

perf_splsda(
  splsda_input,
  ncomp_max = 5,
  validation = "Mfold",
  folds = 5,
  nrepeat = 50,
  measure = "BER",
  distance = "centroids.dist",
  cpus = 1,
  progressBar = TRUE,
  seed = NULL
)

Arguments

splsda_input

Input for the sPLS-DA functions from mixOmics, created with get_input_splsda().

ncomp_max

Integer, the maximum number of latent components to test when estimating the number of latent components to use. Default value is 5.

validation

Character, which cross-validation method to use, can be one of "Mfold" or "loo" (see mixOmics::perf()). Default value is "Mfold".

folds

Integer, number of folds to use in the M-fold cross-validation (see mixOmics::perf()). Default value is 5.

nrepeat

Integer, number of times the cross-validation is repeated (see mixOmics::perf()).

measure

Performance measure used to select the optimal value of ncomp, can be one of "BER" or "overall" (see mixOmics::perf()). Default value is "BER".

distance

Distance metric used to select the optimal value of ncomp, can be one of "max.dist", "centroids.dist" or "mahalanobis.dist" (see mixOmics::perf()). Default value is "centroids.dist".

cpus

Integer, number of cpus to use.

progressBar

Logical, whether to display a progress bar during the optimisation of ncomp. Default value is TRUE.

seed

Integer, seed to use. Default is NULL, i.e. no seed is set inside the function.

Value

A list as per the output of the mixOmics::perf() function, with the following additional elements:

  • dataset_name: the name of the dataset analysed;

  • group: column name in the samples information data-frame used as samples group;

  • optim_ncomp: the optimal number of latent components as per the measure and distance specified;

  • optim_measure: the measure used to select the optimal number of latent components;

  • optim_distance: the distance metric used to select the optimal number of latent components. In addition, the name of the dataset analysed and the column name in the samples information data-frame used as samples group as stored as attributes dataset_name and group, respectively.

Details

This function uses the mixOmics::plsda() and mixOmics::perf() function from the mixOmics package.