padma {padma} | R Documentation

## Calculate individualized deviation scores from multi-omic data

### Description

This is the primary user interface for the `padma` package. Generic S4 methods are implemented to calculate individualized pathway deviation scores on the basis of matched, multi-omic data. The supported classes for input are `list` and `MultiAssayExperiment`. The output of `padma` is an S4 object of class `padmaResults`.

### Usage

    padma(object, ...)

    ## S4 method for signature 'list'
    padma(
      object,
      colData,
      gene_map = padma::mirtarbase,
      base_ids = NULL,
      supp_ids = NULL,
      pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY",
      impute = FALSE,
      variance_threshold = 1e-04,
      full_results = TRUE,
      verbose = TRUE,
      ...
    )

    ## S4 method for signature 'MultiAssayExperiment'
    padma(
      object,
      gene_map = padma::mirtarbase,
      base_ids = NULL,
      supp_ids = NULL,
      pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY",
      impute = FALSE,
      variance_threshold = 1e-04,
      full_results = TRUE,
      verbose = TRUE,
      ...
    )

### Arguments

`object` | Matched multi-omic data. May be provided as (1) a `MultiAssayExperiment` or (2) a named `list`, with each element corresponding to a `data.frame` representing an omic, with biological entities in rows. Row names should include unique biological entity IDs (e.g., gene symbols, miRNA names); columns represent individuals. If more than one biological entity is used, a `gene_map` data.frame providing mappings between IDs and gene names should be provided if the default `mirtarbase` is not sufficient.
`...` | Optional additional arguments
`colData` | (optional) A `DataFrame` or `data.frame` of characteristics for all biological units, to be used in creating a `MultiAssayExperiment` from an `object` of class `list`
`gene_map` | (optional) Data frame mapping arbitrary biological entities (e.g. miRNAs) to genes. Contains two columns, where the first provides the IDs of the entity and the second provides the IDs of the corresponding target gene. By default, the miRNA-gene interactions of type 'Functional MTI' from miRTarBase are used (see the preloaded `'mirtarbase'` data in the package).
`base_ids` | (optional) Sample names to be used as reference base data. By default, all samples are used.
`supp_ids` | (optional) Sample names to be used as supplementary individuals to be projected onto the analysis based on the individuals identified in `base_ids`. By default, takes the value `NULL`, but should not overlap with `base_ids` if provided by the user.
`pathway_name` | Character of either a KEGG pathway identifier or MSigDB pathway names (e.g., see the pathway names in the `'geneset'` column of the preloaded `msigdb` data in the package), or a vector of gene symbols.
`impute` | If `TRUE`, impute missing values separately in base and supplementary data using MFA as implemented in the missMDA package; otherwise simple mean imputation is used (default).
`variance_threshold` | Minimal variance required across samples to retain a biological entity in the analysis
`full_results` | If `TRUE` (default), include full MFA results in function output; otherwise, provide concise output to save space.
`verbose` | If `TRUE`, provide verbose output.

### Value

An S4 object of class `padmaResults`, where individualized pathway deviation scores are stored as the assay data, and the corresponding pathway name, full MFA results, number of genes, and names of imputed or filtered genes are stored as slots that can be retrieved using the appropriate accessor functions.

### Author(s)

Andrea Rau

### Examples

    LUAD_subset <- padma::LUAD_subset
    ## Create MultiAssayExperiment object with LUAD data
    omics_data <-
      list(rnaseq = LUAD_subset$rnaseq,
           methyl = LUAD_subset$methyl,
           mirna = LUAD_subset$mirna,
           cna = LUAD_subset$cna)
    pheno_data <-
      data.frame(LUAD_subset$clinical,
                 row.names = LUAD_subset$clinical$bcr_patient_barcode)
    mae <-
      suppressMessages(
        MultiAssayExperiment::MultiAssayExperiment(
          experiments = omics_data, colData = pheno_data))

    ## Run padma
    run_padma <-
      padma(mae, gene_map = padma::mirtarbase,
            pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY", verbose = FALSE)

    summary(run_padma)

    ## padma plots
    factorMap(run_padma, dim_x = 1, dim_y = 2)
    factorMap(run_padma, dim_x = 1, dim_y = 2,
               partial_id = "TCGA-78-7536")
    omicsContrib(run_padma, max_dim = 10)

[Package padma version 1.0.0 Index]