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Aggregate Data by Subject

Source: R/mStat_aggregate_data.R
mStat_aggregate_data.Rd

Aggregates feature counts and metadata by subject and optional strata.

Usage

mStat_aggregate_data(
  data.obj,
  subject.var,
  strata.var = NULL,
  meta.handle.conflict = c("first", "stop", "summarise")
)

Arguments

data.obj

A MicrobiomeStat data object, which is a list containing at minimum the following components:

  • feature.tab: A matrix of feature abundances (taxa/genes as rows, samples as columns)

  • meta.dat: A data frame of sample metadata (samples as rows)

Optional components include:

  • feature.ann: A matrix/data frame of feature annotations (e.g., taxonomy)

  • tree: A phylogenetic tree object (class "phylo")

  • feature.agg.list: Pre-aggregated feature tables by taxonomy

Data objects can be created using converters like mStat_convert_phyloseq_to_data_obj or importers like mStat_import_qiime2_as_data_obj.

subject.var

Character string specifying the column name in meta.dat that uniquely identifies each subject or sample unit. Required for longitudinal and paired designs to track repeated measurements.

strata.var

Character string specifying the column name in meta.dat for stratification. When provided, analyses and visualizations will be performed separately within each stratum (e.g., by site, batch, or sex).

meta.handle.conflict

Character string specifying how to handle metadata conflicts:

  • "first": Use first record (default)

  • "stop": Error on conflicts

  • "summarise": Mean for numeric, first for character/factor

Value

A MicrobiomeStat data object with aggregated data.

Examples

if (FALSE) { # \dontrun{
# Prepare data for the function
data(peerj32.obj)

# Call the function with the default subject variable "subject"
aggregated_data <- mStat_aggregate_data(
  data.obj = peerj32.obj,
  subject.var = "subject",
  strata.var = NULL
)

# Example with a different subject variable name
# Let's pretend the subject ID column is called "participant"
# peerj32.obj$meta.dat$participant <- peerj32.obj$meta.dat$subject
# aggregated_data_2 <- mStat_aggregate_data(
#   data.obj = peerj32.obj,
#   subject.var = "participant",
#   strata.var = "group"
# )
} # }