R/SIMMS-package.R

In SIMMS: Subnetwork Integration for Multi-Modal Signatures

#' SIMMS - Subnetwork Integration for Multi-Modal Signatures
#' 
#' Algorithms to create prognostic biomarkers using biological networks
#' 
#' \tabular{ll}{ Package: \tab SIMMS\cr Type: \tab Package\cr License: \tab
#' GPL-2\cr LazyLoad: \tab yes\cr }
#' 
#' @name SIMMS-package
#' @aliases SIMMS-package SIMMS
#' @docType package
#' @author Syed Haider, Michal Grzadkowski & Paul C. Boutros
#' @keywords package
#' 
#' @importFrom grDevices dev.off png
#' @importFrom graphics legend lines par plot text title
#' @importFrom stats as.formula coef median p.adjust pchisq predict
#' @importFrom utils read.table write.table
#' @importFrom survival coxph Surv survfit survdiff
#' @importFrom MASS stepAIC
#' @importFrom glmnet cv.glmnet
#' @importFrom randomForestSRC rfsrc
#' @importFrom doParallel registerDoParallel
#' @importFrom parallel makeCluster stopCluster
#' @importFrom foreach %dopar% foreach registerDoSEQ
#' 
#' @examples
#' 
#' options("warn" = -1);
#' 
#' # get data directory 
#' data.directory <- get.program.defaults(networks.database = "test")[["test.data.dir"]];
#' 
#' # initialise params
#' output.directory  <- tempdir();
#' data.types <- c("mRNA");
#' feature.selection.datasets <- c("Breastdata1");
#' training.datasets <- c("Breastdata1");
#' validation.datasets <- c("Breastdata2");
#' feature.selection.p.thresholds <- c(0.5);
#' feature.selection.p.threshold <- 0.5;
#' learning.algorithms <- c("backward", "forward", "glm");
#' top.n.features <- 5;
#' 
#' # compute network HRs for all the subnet features
#' derive.network.features(
#'   data.directory = data.directory,
#'   output.directory = output.directory,
#'   data.types = data.types,
#'   feature.selection.datasets = feature.selection.datasets,
#'   feature.selection.p.thresholds = feature.selection.p.thresholds,
#'   networks.database = "test"
#'   );
#' 
#' # preparing training and validation datasets.
#' # Normalisation & patientwise subnet feature scores
#' prepare.training.validation.datasets(
#'   data.directory = data.directory,
#'   output.directory = output.directory,
#'   data.types = data.types,
#'   p.threshold = feature.selection.p.threshold,
#'   feature.selection.datasets = feature.selection.datasets,
#'   datasets = unique(c(training.datasets, validation.datasets)),
#'   networks.database = "test"
#'   );
#' 
#' # create classifier assessing univariate prognostic power of subnetwork modules (Train and Validate)
#' create.classifier.univariate(
#'   data.directory = data.directory,
#'   output.directory = output.directory,
#'   feature.selection.datasets = feature.selection.datasets,
#'   feature.selection.p.threshold = feature.selection.p.threshold,
#'   training.datasets = training.datasets,
#'   validation.datasets = validation.datasets,
#'   top.n.features = top.n.features
#'   );
#' 
#' # create a multivariate classifier (Train and Validate)
#' create.classifier.multivariate(
#'   data.directory = data.directory,
#'   output.directory = output.directory,
#'   feature.selection.datasets = feature.selection.datasets,
#'   feature.selection.p.threshold = feature.selection.p.threshold,
#'   training.datasets = training.datasets,
#'   validation.datasets = validation.datasets,
#'   learning.algorithms = learning.algorithms,
#'   top.n.features = top.n.features
#'   );
#' 
#' # (optional) plot Kaplan-Meier survival curves and perform senstivity analysis
#' if (FALSE){
#'   create.survivalplots(
#'     data.directory = data.directory,
#'     output.directory = output.directory,
#'     training.datasets = training.datasets,
#'     validation.datasets = validation.datasets,
#'     top.n.features = top.n.features,
#'     learning.algorithms = learning.algorithms,
#'     survtime.cutoffs = c(5),
#'     KM.plotting.fun = "create.KM.plot",
#'     resolution = 100
#'     );
#'   }
#' 
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