SIMMS (version 1.3.2)

calculate.network.coefficients: Calculate Cox statistics for input dataset

Description

Function to compute hazard ratios for the genes in pathway-derived networks, by aggregating input datasets into one training cohort. The hazard ratios are computed for each pair by calculating the HR of each gene independently and as an interaction (i.e. y = HR(A) + HR(B) + HR(A:B)

Usage

calculate.network.coefficients(
  data.directory = ".",
  output.directory = ".",
  training.datasets = NULL,
  data.types = c("mRNA"),
  data.types.ordinal = c("cna"),
  centre.data = "median",
  subnets.file.flattened = NULL,
  truncate.survival = 100,
  subset = NULL
)

Arguments

data.directory

Path to the directory containing datasets as specified by training.datasets

output.directory

Path to the output folder where intermediate and results files will be saved

training.datasets

A vector containing names of training datasets

data.types

A vector of molecular datatypes to load. Defaults to c('mRNA')

data.types.ordinal

A vector of molecular datatypes to be treated as ordinal. Defaults to c('cna')

centre.data

A character string specifying the centre value to be used for scaling data. Valid values are: 'median', 'mean', or a user defined numeric threshold e.g. '0.3' when modelling methylation beta values. This value is used for both scaling as well as for dichotomising data for estimating univariate betas from Cox model. Defaults to 'median'

subnets.file.flattened

File containing all the binary ineractions derived from pathway-derived networks

truncate.survival

A numeric value specifying survival truncation in years. Defaults to 100 years which effectively means no truncation

subset

A list with a Field and Entry component specifying a subset of patients to be selected whose annotation Field matches Entry

Value

Returns a list of matrices for each of the data types. Matrices contain nodes HR/P, edges HR and edges P.

Examples

# NOT RUN {
options("warn" = -1);
program.data <- get.program.defaults(networks.database = "test");
data.directory <- program.data[["test.data.dir"]];
subnets.file.flattened <- program.data[["subnets.file.flattened"]];
output.directory = tempdir();
coef.nodes.edges <- calculate.network.coefficients(
  data.directory = data.directory,
  output.directory = output.directory,
  training.datasets = c("Breastdata1"),
  data.types = c("mRNA"),
  subnets.file.flattened = subnets.file.flattened
  );

# }