wrapper_human_mutations: Wrapper of Simpati workflow for a human gene study

In LucaGiudice/Simpati: Pathway-based classifier exploits patient similarity network paradigm

Description

This function performs all Simpati operations, both classification and pathway enrichment, on a study which includes genes as features describing the human patient's profiles. This function is intended to help users which do not want to go through all the Simpati operations manually and their study falls into the wrapper application.

Usage

wrapper_human_mutations(
  geno,
  info,
  net,
  pathways_l,
  dataset_name,
  disease_type,
  key_words,
  n_cores = 5,
  n_LOO = 0.3,
  seed = 0,
  test_run = TRUE
)

Arguments

geno	A numeric matrix of patient profiles: features (e.g. genes) x samples
info	A character matrix of info about the patients: patient_ID | class (e.g. clinical information)
net	Feature interaction network as adjacency matrix (e.g. gene-gene interactio network)
pathways_l	List of feature sets (e.g. gene lists)
dataset_name	The name of the dataset or project
disease_type	The DISGNET semantic type of the sample condition: "Neoplastic Process" or "Disease or Syndrome"
key_words	A character vector of one or more words related to the sample condition (e.g. KIRC: Kidney, Carcinoma)
n_cores	Default 5, An integer value greater than 0 indicating the number of cores to use for parallel computing
n_LOO	Default 0.3, A double value lower than 1 indicating the percentage of patients to test in the LOO (e.g. 0.3 is 30 percentage)
seed	Default 0, An integer value to set the random seed generator of the run
test_run	Default TRUE, boolean indicating if to run the function with a limited numbers of sets from pathways_l to check if Simpati works properly on the input data

Value

A list of results

LucaGiudice/Simpati documentation built on Jan. 27, 2022, 11:42 p.m.