Delete -Ome symbols or IDs without matching features recorded in a given assay data frame from a pathway collection

Given a bio-assay design matrix and a pathwayCollection gene pathways list (each within an Omics*-class object), delete the genes / proteins / lipids / metabolomes / transcriptomes symbols or IDs recorded in each pathway which are not recorded in the assay data frame.

IntersectOmicsPwyCollct(object, trim = 3, message = TRUE, ...)

# S4 method for OmicsPathway
IntersectOmicsPwyCollct(object, trim = 3, message = TRUE, ...)

Arguments

object	An object of class OmicsPathway, OmicsSurv, OmicsReg, or OmicsCateg.
trim	The minimum cutoff of matching -Ome measures before a pathway is excluded. Defaults to 3.
message	Should this function return diagnostic messages? Messages concern the percentage of genes included in the pathways list but not measured in the data, genes measured in the data but not called for in the pathways, and the number of pathways ignored due to too few number of genes present after trimming. Defaults to TRUE.
...	Dots for additional internal arguments (as necessary).

Value

A valid Omics*-class object. This output object will be identical to the input object, except that any genes present in the pathways list, but not present in the MS design matrix, will have been removed. Additionally, the pathway list will have the number of genes in each trimmed pathway stored as the n_tested object.

Details

This function takes in a data frame with named columns and a pathwayCollection list, all through one of the Omics* classes. This function will then copy the pathway collection, iterate over the list of copied pathways, delete symbols or IDs from that pathway without matches from the bio-assay design matrix column names, and remove any pathways that have fewer than trim genes with corresponding columns in the assay. The genes not recorded in the bio-assay design matrix are removed from the copy of the pathway collection (the trimPathwayCollection object), but remain in the original pathway collection.

NOTE: some genes will be included in more than one pathway, so these pathways are not mutually exclusive. Further note that there may be many genes in the assay design matrix that are not included in the pathway sets, so these will not be extracted to the list. It is then vitally important to use either a very broad and generic pathwayCollection list or a pathwayCollection list that is appropriate for the assay data supplied. While you can create your own pathway lists, create proper pathwayCollection list objects by importing .gmt files with the read_gmt function.

Examples

# DO NOT CALL THIS FUNCTION DIRECTLY. USE CreateOmics() INSTEAD.

if (FALSE) {
  ###  Load the Example Data  ###
  data("colonSurv_df")
  data("colon_pathwayCollection")

  ###  Create an OmicsSurv Object  ###
  colon_Omics <- CreateOmics(
    assayData_df = colonSurv_df[, -(2:3)],
    pathwayCollection_ls = colon_pathwayCollection
  )
}