as.multiData: Create a `multiData` object from multiple omicsData objects

Description

Create a `multiData` object from multiple omicsData objects

Usage

as.multiData(
  ...,
  f_meta = NULL,
  sample_intersect = FALSE,
  match_samples = TRUE,
  keep_sample_info = FALSE,
  auto_fmeta = FALSE
)

Value

Object of class 'multiData' containing the omicsData objects, and the sample alignment information f_meta.

Arguments

...: two or more objects of type 'pepData', 'proData', 'metabData', 'lipidData', or 'nmrData', created by as.pepData
f_meta: A data.frame containing sample and group information for all omicsData objects supplied to the function.
sample_intersect: logical indicator for whether only the samples that are common across all datasets be kept in f_meta. See details for how samples will be dropped.
match_samples: logical indicator. If auto_fmeta = TRUE, whether to attempt to match the names in the sample columns in f_data across all objects in an attempt to align them in f_meta. Defaults to TRUE.
keep_sample_info: logical indicator for whether to attempt to append sample information contained in the objects' f_data to the final f_meta via a series of left joins. Defaults to FALSE.
auto_fmeta: logical indicator for whether to attempt to automatically construct f_meta from the objects' sample information. Defaults to FALSE.

Details

Object limits: Currently, as.multiData accepts at most one object from each of classes 'pepData/proData', 'metabData', 'nmrData', and at most two objects of class 'lipidData'.

sample_intersect will auto-align samples that occur in all datasets. Specifically, it creates a vector of all samples that are common across all datasets, and simply creates an f_meta by copying this vector for each dataset and column-binding them.

Examples

Run this code

if (FALSE) { # requireNamespace("pmartRdata", quietly = TRUE)

library(pmartRdata)

# Combine metabolomics and protein object into multidata, both must be log2
# and normalized.
mymetab <- edata_transform(omicsData = metab_object, data_scale = "log2")
mymetab <- normalize_global(omicsData = mymetab, subset_fn = "all",
                            norm_fn = "median", apply_norm = TRUE)

mypro <- pro_object

# Combine without specifically supplying f_meta, either directly, or as one
# of the f_datas in any object.
mymultidata <- as.multiData(mymetab, mypro, auto_fmeta = TRUE, sample_intersect = TRUE)

# Manually supply an f_meta
f_meta <- data.frame(
  "Proteins" = mypro$f_data$SampleID[match(mymetab$f_data$SampleID, mypro$f_data$SampleID)],
  "Metabolites" = mymetab$f_data$SampleID,
  "Condition" = mymetab$f_data$Phenotype[match(mymetab$f_data$SampleID, mypro$f_data$SampleID)]
)

mymultidata <- as.multiData(mymetab, mypro, f_meta = f_meta)
# remove samples that are not common across all data.
mymultidata <- as.multiData(mymetab, mypro, f_meta = f_meta, sample_intersect = TRUE)
}