Apply cv.glmnet() for a list of module IDs and for each prevalence level

Usage

cvglm_to_coeffs_by_object(
  list_dfs,
  test_module = identify_module(),
  seed = NULL,
  ...
)

Arguments

list_dfs: List of dataframe. A normalized dataframe
test_module: List of string. The module IDs
seed: Numeric. The seed number, ensuring reproducibility
...: Additional arguments passed on to find_all_module_neighbors()

Value

Dataframe. Returns the module ID, its detected neighbor and the corresponding coefficient

Examples

data(data)
data(metadata)
# Simple example
normed_JPN <- norm_data(
  data$CRC_JPN,
  col_module_id = "msp_id",
  annotation_level = "species",
  prev_list = c(0.25, 0.30)
)
neighbors_JPN <- cvglm_to_coeffs_by_object(
  list_dfs = normed_JPN,
  test_module = c("msp_0030", "msp_0345"),
  seed = 20242025
)
# Example with covariate
# normed_CHN <- norm_data(
#   data$CRC_CHN,
#   col_module_id = "msp_id",
#   annotation_level = "species",
#   prev_list = c(0.25, 0.30)
# )
# neighbors_CHN <- cvglm_to_coeffs_by_object(
#   list_dfs = normed_CHN,
#   test_module = c("msp_0030", "msp_0345"),
#   seed = 20242025,
#   covar = ~study_accession,
#   meta_df = metadata$CRC_CHN,
#   sample_col = "secondary_sample_accession"
# )