Apply NeighborFinder simplest version on raw data

Usage

apply_NF_simple(
  data_with_annotation,
  object_of_interest,
  col_module_id,
  annotation_level,
  prev_level = 0.3,
  filtering_top = 20,
  seed = NULL,
  ...
)

Arguments

data_with_annotation: Dataframe. The abundance table merged with the module names. Required format: modules are the rows and samples are the columns. The first column must be the modules name (e.g. species), the second is the module ID (e.g. msp), and each subsequent column is a sample
object_of_interest: String. The name of the bacteria or species of interest or a key word in the functional module definition
col_module_id: String. The name of the column with the module names in annotation_table
annotation_level: String. The name of the column with the level to be studied. Examples: species, genus, level_1
prev_level: Numeric. The prevalence to be studied. Required format is decimal: 0.20 for 20% of prevalence
filtering_top: Numeric. The filtering top percentage to be studied. Required format is: 10 for top 10%
seed: Numeric. The seed number, ensuring reproducibility
...: Additional arguments passed on to cvglm_to_coeffs_by_object()

Value

Dataframe. Returns results after using NeighborFinder(): for each module ID from 'object_of_interest', the names of their neighbors and the corresponding coefficients calculated by cv.glmnet()

Examples

data(data)
res_CRC_JPN <- apply_NF_simple(data$CRC_JPN, object_of_interest = "Escherichia coli", col_module_id = "msp_id", annotation_level = "species", seed = 20242025)