Target factory for feature preselection based on Median Absolute Deviation

Creates a list of targets to perform feature preselection on datasets from a MultiDataSet object by retaining features with the highest Median Absolute Deviation (MAD).

Usage

feature_preselection_mad_factory(
  mo_data_target,
  to_keep_ns,
  to_keep_props = NULL,
  with_ties = TRUE,
  target_name_prefix = "",
  filtered_set_target_name = NULL
)

Arguments

mo_data_target

Symbol, the name of the target containing the MultiDataSet object.

to_keep_ns

Named integer vector, the number of feature to retain in each dataset to be prefiltered (names should correspond to a dataset name). Value should be less than the number of features in the corresponding dataset. Set to NULL in order to use to_keep_props instead.

to_keep_props

Named numeric vector, the proportion of features to retain in each dataset to be prefiltered (names should correspond to a dataset name). Value should be > 0 and < 1. Will be ignored if to_keep_ns is not NULL.

with_ties

Should ties be kept together? If TRUE, may return more features than requested. Default value is TRUE.

target_name_prefix

Character, a prefix to add to the name of the targets created by this target factory. Default value is "".

filtered_set_target_name

Character, the name of the final target containing the filtered MultiDataSet object. If NULL, a name will automatically be supplied. Default value is NULL.

Value

A list of target objects. With target_name_prefix = "" and filtered_set_target_name = NULL, the following targets are created:

• mad_spec: a target that generates a grouped tibble where each row corresponds to one dataset to be filtered, with the columns specifying each dataset name, and associated values from to_keep_ns, to_keep_props and with_ties.

• mad_mat: a dynamic branching target that run the get_dataset_matrix() function for each dataset.

• individual_mad_values: a dynamic branching target that runs the select_features_mad_matrix() function for each dataset.

• filtered_set_mad: a target to retain from the original MultiDataSet object only features selected based on their MAD values.

Examples

if (FALSE) { # \dontrun{
## in the _targets.R
library(moiraine)

list(
  ## add code here to load the different datasets

  ## the following target creates a MultiDataSet object from previously
  ## created omics sets (geno_set, trans_set, etc)
  tar_target(
    mo_set,
    create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)
  ),
  feature_preselection_mad_factory(
    mo_set,
    to_keep_ns = c("rnaseq" = 1000, "metabolome" = 500),
    filtered_set_target_name = "mo_set_filtered"
  ),

  ## Another example using to_keep_props
  feature_preselection_mad_factory(
    mo_set,
    to_keep_ns = NULL,
    to_keep_props = c("rnaseq" = 0.3, "metabolome" = 0.5),
    filtered_set_target_name = "mo_set_filtered"
  )
)
} # }