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Run Multi-Omics Integration

Source: R/run_multiomics_integration.R
run_multiomics_integration.Rd

Performs multi-omics integration using one of several available methods: MOFA, MCIA, RGCCA, or DIABLO. This function takes a MultiAssayExperiment object with two or more assays and computes shared latent factors across omics layers.

Usage

run_multiomics_integration(
  exposomicset,
  method = "MCIA",
  n_factors = 10,
  scale = TRUE,
  outcome = NULL,
  max.iter = 500,
  near.zero.var = TRUE,
  action = "add"
)

Arguments

exposomicset

A MultiAssayExperiment object with at least two assays.

method

Character. Integration method to use. Options are "MOFA", "MCIA", "RGCCA", or "DIABLO".

n_factors

Integer. Number of latent factors/components to compute. Default is 10.

scale

Logical. Whether to scale each assay before integration. Default is TRUE.

outcome

Character. Required if method = "DIABLO". Name of outcome variable in colData used for supervised integration.

max.iter

numeric. Option to increase the number of iterations for mixOmics::block.splsda the default is 500.

near.zero.var

Logical. Option to remove variables with near zero variance for mixOmics::block.splsda, default is TRUE .

action

Character. Whether to "add" results to the metadata or "get" them as a list. Default is "add".

Value

If action = "add", returns a MultiAssayExperiment with integration results stored in metadata(exposomicset)$multiomics_integration$integration_results. If action = "get", returns a list with integration method and result.

Details

  • "MOFA" runs Multi-Omics Factor Analysis using the MOFA2 package and returns a trained model.

  • "MCIA" runs multi-co-inertia analysis using the nipalsMCIA package.

  • "RGCCA" runs Regularized Generalized Canonical Correlation Analysis using the RGCCA package.

  • "DIABLO" performs supervised integration using the mixOmics package and a specified outcome.

Examples

# create example data
mae <- make_example_data(
    n_samples = 20,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.

# perform multiomics integration
mae <- run_multiomics_integration(
    mae,
    method = "DIABLO",
    outcome = "smoker",
    n_factors = 3
)
#> Scaling each assay in MultiAssayExperiment.
#> Running multi-omics integration using DIABLO...
#> Applying DIABLO supervised integration.
#> Design matrix has changed to include Y; each block will be
#>             linked to Y.