OmicSelector_prepare_split: OmicSelector_prepare_split

In kstawiski/OmicSelector: OmicSelector - a package for biomarker selection based on high-throughput experiments.

OmicSelector_prepare_split

Description

Create split required for 'OmicSelector_OmicSelector()' and all the following functions. It is obligatory to use it. The function devides the dataset into training, testing and validation set. Be default (as 'train_proc=0.6') 60 perc. of cases will be assigned to trainining datset. The rest is devided into testing and validation dataset in half, ending in 60 perc. of cases in training dataset, 20 perc. of cases in testing dataset and 20 perc. of cases in validation dataset. Metadata have to have 'Class' variable, with 'Case' and 'Control' values.

Usage

OmicSelector_prepare_split(
  metadane = metadane,
  ttpm = ttpm_features,
  train_proc = 0.6
)

Arguments

metadane	Metadata of cases. Must contain 'Class' variable with 'Case' and 'Control' values.
ttpm	Normalized counts used (primary data for the rest of the analysis).
train_proc	What perc. should be kept in training dataset?

Value

The mixed dataset is return. In working directory mixed_train.csv, mixed_test.csv and mixed_valid.csv are saved. This is a crucial step in data preprocessing.

kstawiski/OmicSelector documentation built on April 10, 2024, 11:11 p.m.