# SIMMS - Subnetwork Integration for Multi-Modal Signatures Algorithms to create prognostic biomarkers using biological networks package ## Details ||| |:--|:--| |Package:|SIMMS| |Type:|Package| |License:|GPL-2| |LazyLoad:|yes| ## Examples ```r 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 ); } ``` ## Author(s) Syed Haider, Michal Grzadkowski & Paul C. Boutros

SIMMS-package