Description Usage Arguments Value Author(s) Examples
View source: R/cluster.patients.R
A wrapper function for using consensus clustering to subtype patients
1 2 3 4 | 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);
|
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 |
consensus_cluster_result |
consensus clustering function return value |
subtype_table |
the table written to subtype.table.file |
Natalie Fox
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ## 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)
|
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