Random_spongEffects: build random classifiers

View source: R/fn_spongeffects_utility.R

Random_spongEffectsR Documentation

build random classifiers

Description

build random classifiers

Usage

Random_spongEffects(
  sponge_modules,
  gene_expr,
  min.size = 10,
  bin.size = 100,
  max.size = 200,
  min.expression = 10,
  replace = F,
  method = "OE",
  cores = 1
)

Arguments

sponge_modules

result of define_modules()

gene_expr

Input expression matri

min.size

minimum module size (default: 10)

bin.size

bin size (default: 100)

max.size

maximum module size (default: 200)

replace

Possibility of keeping or removing (default) central genes in the modules (default: F)

method

Enrichment to be used (Overall Enrichment: OE or Gene Set Variation Analysis: GSVA) (default: OE)

cores

number of cores to be used to calculate entichment scores with gsva or ssgsea methods. Default 1

train_gene_expr

expression data of train dataset, genenames must be in rownames

test_gene_expr

expression data of test dataset, genenames must be in rownames

train_meta_data

meta data of train dataset

test_meta_data

meta data of test dataset

train_meta_data_type

TCGA or METABRIC

test_meta_data_type

TCGA or METABRIC

metric

metric (Exact_match, Accuracy) (default: Exact_match)

tunegrid_c

defines the grid for the hyperparameter optimization during cross validation (caret package) (default: 1:100)

n.folds

number of folds to be calculated

repetitions

number of k-fold cv iterations (default: 3)

min.expr

minimum expression (default: 10)

Value

randomized prediction model Define random modules

A list with randomly defined modules and related enrichment scores


biomedbigdata/SPONGE documentation built on Feb. 6, 2023, 10:19 p.m.