oplsda {ASICS} | R Documentation

## Orthogonal projections to latent structures discriminant analysis (OPLS-DA) on a `SummarizedExperiment` object

### Description

Perform an OPLS-DA with the function of the `ropls` package on a `SummarizedExperiment` object obtained with the `formatForAnalysis` function

### Usage

    oplsda(
      analysis_data,
      condition,
      cross.val = 1,
      thres.VIP = 1,
      type.data = "quantifications",
      seed = 12345,
      ...
    )

### Arguments

`analysis_data` | A `SummarizedExperiment` object obtained with the `formatForAnalysis` function.
`condition` | The name of the design variable (two level factor) specifying the response to be explained.
`cross.val` | Number of cross validation folds.
`thres.VIP` | A number specifying the VIP threshold used to identify influential variables.
`type.data` | Type of data used for the analyses (e.g., `"quantifications"`, `"buckets"`...). Default to `"quantifications"`.
`seed` | Random seed to control randomness of cross validation folds.
`...` | Further arguments to be passed to the function `opls` for specifying the parameters of the algorithm, if necessary.

### Value

A S4 object of class AnalysisResults containing OPLS-DA results.

### References

Trygg, J. and Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119–128.

Thevenot, E.A., Roux, A., Xu, Y., Ezan, E., Junot, C. 2015. Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research. 14:3322-3335.

### See Also

AnalysisResults

### Examples

    # Import quantification results
    if (require("ASICSdata", quietly = TRUE)) {
      quantif_path <- system.file("extdata", "results_ASICS.txt",
                                  package = "ASICSdata")
      quantification <- read.table(quantif_path, header = TRUE, row.names = 1)

      # Import design
      design <- read.table(system.file("extdata", "design_diabete_example.txt",
                                       package = "ASICSdata"), header = TRUE)
      design$condition <- factor(design$condition)

      # Create object for analysis and remove features with more than 25% of
      # zeros
      analysis_obj <- formatForAnalysis(quantification,
                                        zero.threshold = 25, design = design)
      res_oplsda <- oplsda(analysis_obj, "condition", orthoI = 1)
    }

[Package ASICS version 2.6.1 Index]