cluster.patients: Clustering to find patient subtypes

In iSubGen: Integrative Subtype Generation

Description

A wrapper function for using consensus clustering to subtype patients

Usage

cluster.patients(data.matrix, distance.metric, parent.output.dir,
new.result.dir, subtype.table.file = NULL, max.num.subtypes = 12, 
clustering.reps = 1000, proportion.features = 0.8, proportion.patients = 0.8, 
verbose = FALSE, consensus.cluster.write.table = TRUE);

Arguments

data.matrix	matrix with patients as rows and features as columns
distance.metric	distance metric for comparing patient profiles. ex. euclidean
parent.output.dir	directory where the consensus clustering function will create a directory of results
new.result.dir	directory name for consensus clustering results
subtype.table.file	filename for subtype assignment table for different number of clusters
max.num.subtypes	maximum number of clusters to separate patients into
clustering.reps	number of subsamples for consensus clustering function
proportion.features	proportion of features to sample for each clustering iteration
proportion.patients	proportion of patients to sample for each clustering iteration
verbose	logical, where TRUE indicates to print messages to the screen to indicate progress
consensus.cluster.write.table	logical, where TRUE indicates for the ConsensusClusterPlus function to writeTable

Value

consensus_cluster_result	consensus clustering function return value
subtype_table	the table written to subtype.table.file

Author(s)

Natalie Fox

Examples

## Not run: 

# For this example instead of clustering CIS and IRF matrices,
# create a data matrix to see how the function works without
# running through the whole iSubGen process.
# This example is created with to have 4 distinct clusters
set.seed(5);
ex.matrix <- matrix(
  c(
    sample(c(0,1), 30, replace = TRUE), rep(1,75), rep(0,25),
    sample(c(0,1), 30, replace = TRUE), rep(1,75), rep(0,25),
    sample(c(0,1), 30, replace = TRUE), rep(1,75), rep(0,25),
    sample(c(0,1), 30, replace = TRUE), rep(1,100),
    sample(c(0,1), 30, replace = TRUE), rep(1,100),
    sample(c(0,1), 30, replace = TRUE), rep(1,100),
    sample(c(0,1), 30, replace = TRUE), rep(0,100),
    sample(c(0,1), 30, replace = TRUE), rep(0,100),
    sample(c(0,1), 30, replace = TRUE), rep(0,100),
    sample(c(0,1), 30, replace = TRUE), rep(0,75), rep(1,25),
    sample(c(0,1), 30, replace = TRUE), rep(0,75), rep(1,25),
    sample(c(0,1), 30, replace = TRUE), rep(0,75), rep(1,25)
    ),
  nrow=130);
rownames(ex.matrix) <- paste0('gene',1:130);
colnames(ex.matrix) <- paste0('patient',LETTERS[1:12]); 

# Use Consensus clustering to subtype the patient profiles
subtyping.results <- cluster.patients(
  data.matrix = ex.matrix,
  distance.metric = 'euclidean',
  parent.output.dir = './',
  new.result.dir = 'example_subtyping',
  max.num.subtypes = 6,
  clustering.reps = 50,
  consensus.cluster.write.table = FALSE
  );	

## End(Not run)

iSubGen documentation built on April 22, 2021, 5:11 p.m.