Package 'SIMMS' reference manual

Title:	Subnetwork Integration for Multi-Modal Signatures
Description:	Algorithms to create prognostic biomarkers using biological genesets or networks.
Authors:	Syed Haider [aut, cre], Paul C. Boutros [aut], Michal Grzadkowski [ctb]
Maintainer:	Syed Haider <[email protected]>
License:	GPL-2
Version:	1.3.2
Built:	2025-02-13 04:28:15 UTC
Source:	https://github.com/cran/SIMMS

SIMMS - Subnetwork Integration for Multi-Modal Signatures

Description

Algorithms to create prognostic biomarkers using biological networks

Details

Package:	SIMMS
Type:	Package
License:	GPL-2
LazyLoad:	yes

Author(s)

Syed Haider, Michal Grzadkowski & Paul C. Boutros

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
    );
  }

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
    );
  }

Fit a meta-analytic Cox proportional hazards model to a single feature

Description

Takes a meta-analysis data object and fits a Cox proportional hazards model (possibly with adjustment for some specific covariates) by median-dichotomizing patients within each individual dataset.

Usage

calculate.meta.survival(
  feature.name,
  expression.data,
  survival.data,
  rounding = 3,
  other.data = NULL,
  data.type.ordinal = FALSE,
  centre.data = "median"
)
calculate.meta.survival(
  feature.name,
  expression.data,
  survival.data,
  rounding = 3,
  other.data = NULL,
  data.type.ordinal = FALSE,
  centre.data = "median"
)

Arguments

`feature.name`	Character indicate what feature (gene/probe/etc.) should be extracted for analysis
`expression.data`	A list where each component is an expression matrix (patients = columns, genes = rows) for a different dataset
`survival.data`	A list where each component is an object of class Surv
`rounding`	How many digits after the decimal place to include
`other.data`	A list of other covariates to be passed to the Cox model (all elements in this list are used
`data.type.ordinal`	Logical indicating whether to treat this datatype as ordinal. Defaults to FALSE
`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'

Value

Returns a vector containing the HR, p-value, n, and 95% confidence limits of the HR (see fit.coxmodel() for details)

Author(s)

Paul C. Boutros

Examples


data.directory <- get.program.defaults()[["test.data.dir"]];
data.types <- c("mRNA");
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = data.types,
  data.directory = data.directory
  );
x2 <- calculate.meta.survival(
  feature.name = "1000_at",
  expression.data = x1$all.data[[data.types[1]]],
  survival.data = x1$all.survobj
  );

data.directory <- get.program.defaults()[["test.data.dir"]];
data.types <- c("mRNA");
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = data.types,
  data.directory = data.directory
  );
x2 <- calculate.meta.survival(
  feature.name = "1000_at",
  expression.data = x1$all.data[[data.types[1]]],
  survival.data = x1$all.survobj
  );

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
)
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.

Author(s)

Syed Haider & Paul C. Boutros

Examples


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
  );

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
  );

Computes sensitivity measures

Description

Computes sensitivity measures: TP, FP, TN, FN, Sensitivity, Specificity, Accuracy

Usage

calculate.sensitivity.stats(all.data = NULL)
calculate.sensitivity.stats(all.data = NULL)

Arguments

all.data

A data matrix containing predicted and real risk groups

Value

A vector containing TP, FP, TN, FN, Sensitivity, Specificity, Accuracy

Author(s)

Syed Haider

Centre and scale a data matrix

Description

Centre and scale a data matrix. Scaling is done on each column separately

Usage

centre.scale.dataset(x = NULL, centre.data = "median")
centre.scale.dataset(x = NULL, centre.data = "median")

Arguments

`x`	A sample by feature data matrix
`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'

Value

A centred and scaled data matrix

Author(s)

Syed Haider

Examples


tmp <- matrix(data = rnorm(100, 10, 2), nrow = 20);
tmp.scaled.median <- centre.scale.dataset(x = tmp);
tmp.scaled.mean <- centre.scale.dataset(x = tmp, centre.data = "mean");
tmp.scaled.custom <- centre.scale.dataset(x = tmp, centre.data = 0.3);

tmp <- matrix(data = rnorm(100, 10, 2), nrow = 20);
tmp.scaled.median <- centre.scale.dataset(x = tmp);
tmp.scaled.mean <- centre.scale.dataset(x = tmp, centre.data = "mean");
tmp.scaled.custom <- centre.scale.dataset(x = tmp, centre.data = 0.3);

Trains and tests a multivariate survival model

Description

Trains a model on training datasets. Predicts the risk score for all the training & datasets, independently. This function also predicts the risk score for combined training datasets cohort and validation datasets cohort. The risk score estimation is done by multivariate models fit by fit.survivalmodel. The function also predicts risk scores for each of the top.n.features independently.

Usage

create.classifier.multivariate(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3"),
  learning.algorithms = c("backward", "forward"),
  alpha.glm = c(1),
  k.fold.glm = 10,
  seed.value = 51214,
  cores.glm = 1,
  rf.ntree = 1000,
  rf.mtry = NULL,
  rf.nodesize = 15,
  rf.samptype = "swor",
  rf.sampsize = function(x) {     x * 0.66 },
  ...
)
create.classifier.multivariate(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3"),
  learning.algorithms = c("backward", "forward"),
  alpha.glm = c(1),
  k.fold.glm = 10,
  seed.value = 51214,
  cores.glm = 1,
  rf.ntree = 1000,
  rf.mtry = NULL,
  rf.nodesize = 15,
  rf.samptype = "swor",
  rf.sampsize = function(x) {     x * 0.66 },
  ...
)

Arguments

`data.directory`	Path to the directory containing datasets as specified by `feature.selection.datasets`, `training.datasets`, `validation.datasets`
`output.directory`	Path to the output folder where intermediate and results files will be saved
`feature.selection.datasets`	A vector containing names of datasets used for feature selection in function `derive.network.features()`
`feature.selection.p.threshold`	One of the P values that were used for feature selection in function `derive.network.features()`. This function does not support vector of P values as used in `derive.network.features()` for performance reasons
`training.datasets`	A vector containing names of training datasets
`validation.datasets`	A vector containing names of validation datasets
`top.n.features`	A numeric value specifying how many top ranked features will be used for univariate survival modelling
`models`	A character vector specifying which of the models ('1' = N+E, '2' = N, '3' = E) to run
`learning.algorithms`	A character vector specifying which learning algorithm to be used for model fitting and feature selection. Defaults to c('backward', 'forward'). Available options are: c('backward', 'forward', 'glm', 'randomforest')
`alpha.glm`	A numeric vector specifying elastic-net mixing parameter alpha, with range alpha raning from [0,1]. 1 for LASSO (default) and 0 for ridge. For multiple values of alpha, most optimal value is selected through cross validation on training set
`k.fold.glm`	A numeric value specifying k-fold cross validation if glm was chosen in `learning.algorithms`
`seed.value`	A numeric value specifying seed for glm k-fold cross or random forest validation if glm was chosen in `learning.algorithms`
`cores.glm`	An integer value specifying number of cores to be used for glm if it was chosen in `learning.algorithms`
`rf.ntree`	An integer value specifying the number of trees in random forest. Defaults to 1000. This should be tuned after starting with a large forest such as 1000 in the initial run and assessing the results in output\/OOB_error__TRAINING_* to see where the OOB error rate stablises, and then rerunning with the stablised rf.ntree parameter
`rf.mtry`	An integer value specifying the number of variables randomly selected for splitting a node. Defaults to sqrt(features), which is the same as in the underlying R package random survival forest `randomForestSRC::rfsrc`
`rf.nodesize`	An integer value specifying number of unique cases in a terminal node. Defaults to 15, which is the same as in the underlying R package random survival forest `randomForestSRC::rfsrc`
`rf.samptype`	An character string specifying name of sampling. Defaults to sampling without replacement 'swor'. Available options are: c('swor', 'swr')
`rf.sampsize`	A function specifying sampling size when `rf.samptype` is set to sampling without replacement ('swor'). Defaults to 66%: `function(x){x * .66}`
`...`	other params to be passed on to the random forest call to the underlying R package random survival forest `randomForestSRC::rfsrc`

Value

The output files are stored under output.directory/output/

Author(s)

Syed Haider & Vincent Stimper

Examples


# see package's main documentation

# see package's main documentation

Trains and tests a univariate (per subnetwork module) survival model

Description

Usage

create.classifier.univariate(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3")
)
create.classifier.univariate(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3")
)

Arguments

`data.directory`	Path to the directory containing datasets as specified by `feature.selection.datasets`, `training.datasets`, `validation.datasets`
`output.directory`	Path to the output folder where intermediate and results files will be saved
`feature.selection.datasets`	A vector containing names of datasets used for feature selection in function `derive.network.features()`
`feature.selection.p.threshold`	One of the P values that were used for feature selection in function `derive.network.features()`. This function does not support vector of P values as used in `derive.network.features()` for performance reasons
`training.datasets`	A vector containing names of training datasets
`validation.datasets`	A vector containing names of validation datasets
`top.n.features`	A numeric value specifying how many top ranked features will be used for univariate survival modelling
`models`	A character vector specifying which of the models ('1' = N+E, '2' = N, '3' = E) to run

Value

The output files are stored under output.directory/output/

Author(s)

Syed Haider

Examples


# see package's main documentation

# see package's main documentation

Plots Kaplan-meier survival curve for a given risk grouping & survival params

Description

A generic method to plot KM curves

Usage

create.KM.plot(
  riskgroup = NULL,
  survtime = NULL,
  survstat = NULL,
  file.name = NULL,
  main.title = "",
  resolution = 100
)
create.KM.plot(
  riskgroup = NULL,
  survtime = NULL,
  survstat = NULL,
  file.name = NULL,
  main.title = "",
  resolution = 100
)

Arguments

`riskgroup`	A vector containing dichotomized risk groups
`survtime`	A vector containing survival time of the samples
`survstat`	A vector containing survival status of the samples
`file.name`	A string containing full qualified path of the output tiff file
`main.title`	A string specifying main title of the image
`resolution`	A numeric value specifying resolution of the tiff image of KM survival curves. Defaults to 100

Value

The KM survival curves are stored under output.dir/graphs/

Author(s)

Syed Haider

Plots sensitivity analysis for class label dichotomization at supplied survtime cutoffs

Description

A method to computer sensitivity, specificity and accuracy at all the survtime cutoff steps provided

Usage

create.sensitivity.plot(
  riskscore = NULL,
  riskgroup = NULL,
  survtime = NULL,
  survstat = NULL,
  survtime.cutoffs = c(seq(5, 10, 1)),
  output.directory = ".",
  file.stem = NULL,
  main.title = "",
  resolution = 100
)
create.sensitivity.plot(
  riskscore = NULL,
  riskgroup = NULL,
  survtime = NULL,
  survstat = NULL,
  survtime.cutoffs = c(seq(5, 10, 1)),
  output.directory = ".",
  file.stem = NULL,
  main.title = "",
  resolution = 100
)

Arguments

`riskscore`	A vector containing predicted risk scores
`riskgroup`	A vector containing dichotomized risk groups
`survtime`	A vector containing survival time of the samples
`survstat`	A vector containing survival status of the samples
`survtime.cutoffs`	A vector containing cutoff time points used to dichotomize patients into low- and high-risk groups
`output.directory`	Path to the output folder where intermediate and results files will be saved
`file.stem`	A string containing base name for image and text files produced by this method
`main.title`	A string specifying main title of the image
`resolution`	A numeric value specifying resolution of the tiff image of KM survival curves. Defaults to 100

Value

The sensitivity analysis plots are stored under output.directory/graphs/. The sensitivity analysis results are stored under output.directory/output/

Author(s)

Syed Haider

Plots Kaplan-meier survival curves

Description

Plots Kaplan-meier survival curves for all the training & datasets, independently as well as combined training datasets cohort and validation datasets cohort. The function also plots KM survival curves for each of the top.n.features independently.

Usage

create.survivalplots(
  data.directory = ".",
  output.directory = ".",
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  learning.algorithms = c("backward", "forward"),
  truncate.survival = 100,
  survtime.cutoffs = c(seq(5, 10, 1)),
  main.title = FALSE,
  KM.plotting.fun = "create.KM.plot",
  plot.univariate.data = FALSE,
  plot.multivariate.data = TRUE,
  resolution = 100
)
create.survivalplots(
  data.directory = ".",
  output.directory = ".",
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  learning.algorithms = c("backward", "forward"),
  truncate.survival = 100,
  survtime.cutoffs = c(seq(5, 10, 1)),
  main.title = FALSE,
  KM.plotting.fun = "create.KM.plot",
  plot.univariate.data = FALSE,
  plot.multivariate.data = TRUE,
  resolution = 100
)

Arguments

`data.directory`	Path to the directory containing datasets as specified by `training.datasets`, `validation.datasets`
`output.directory`	Path to the output folder where intermediate and results files were saved
`training.datasets`	A vector containing names of training datasets
`validation.datasets`	A vector containing names of validation datasets
`top.n.features`	A numeric value specifying how many top ranked features will be used for univariate survival modelling
`learning.algorithms`	A character vector specifying which learning algorithm to be used for model fitting and feature selection. Defaults to c('backward', 'forward'). Available options are: c('backward', 'forward', 'glm', 'randomforest')
`truncate.survival`	A numeric value specifying survival truncation in years. Defaults to 100 years which effectively means no truncation
`survtime.cutoffs`	A vector containing survival cutoff time points to be used for dichotomization of patients into risk groups for senstivity analysis
`main.title`	A logical to specify plot's main title. Defaults to FASLE
`KM.plotting.fun`	A string containing the name of the method to use for plotting KM curves. Defaults to `create.KM.plot`
`plot.univariate.data`	Logical to indicate whether to plot univariate results for all subnetworks. Default to FALSE
`plot.multivariate.data`	Logical to indicate whether to plot multivariate results for all subnetworks. Defaults to TRUE
`resolution`	A numeric value specifying resolution of the png images of KM survival curves. Defaults to 100

Value

The KM survival curves are stored under output.directory/graphs/

Author(s)

Syed Haider

Examples


# see package's main documentation

# see package's main documentation

Utility function for loading meta-analysis lists

Description

Create Surv objects from an annotation-matrix with handling for different time units.

Usage

create.survobj(annotation = NULL, truncate.survival = 100)
create.survobj(annotation = NULL, truncate.survival = 100)

Arguments

`annotation`	A patient annotation matrix (patients = rows) with (at least) columns for survtime, survstat, and survtime.unit
`truncate.survival`	A numeric value specifying survival truncation in years. Defaults to 100 years which effectively means no truncation

Value

Returns an object of class Surv

Author(s)

Paul C. Boutros

Examples


annotation.file <- paste(
  get.program.defaults()[["test.data.dir"]],
  "/Breastdata2/patient_annotation.txt", sep = ""
  );
annotation <- read.table(
  annotation.file,
  header = TRUE,
  row.names = 1,
  sep = "\t"
  );

# select the appropriate survtime and survstat variable for this dataset
annotation$survstat      <- annotation[,'e.dfs'];
annotation$survtime      <- annotation[,'t.dfs'];
annotation$survtime.unit <- annotation[,'t.dfs.unit'];

# only keep samples with survival data
annotation <- annotation[!is.na(annotation$survstat) & !is.na(annotation$survstat),];

surv.obj <- create.survobj(annotation = annotation);

annotation.file <- paste(
  get.program.defaults()[["test.data.dir"]],
  "/Breastdata2/patient_annotation.txt", sep = ""
  );
annotation <- read.table(
  annotation.file,
  header = TRUE,
  row.names = 1,
  sep = "\t"
  );

# select the appropriate survtime and survstat variable for this dataset
annotation$survstat      <- annotation[,'e.dfs'];
annotation$survtime      <- annotation[,'t.dfs'];
annotation$survtime.unit <- annotation[,'t.dfs.unit'];

# only keep samples with survival data
annotation <- annotation[!is.na(annotation$survstat) & !is.na(annotation$survstat),];

surv.obj <- create.survobj(annotation = annotation);

Derive univariate features from pathway-derived networks

Description

This function fits Cox model to features as well as interaction between features. The coefficients of features are subsequently used to compute impact score of each of the pathway-derived networks.

Usage

derive.network.features(
  data.directory = ".",
  output.directory = ".",
  data.types = c("mRNA"),
  data.types.ordinal = c("cna"),
  centre.data = "median",
  feature.selection.fun = "calculate.network.coefficients",
  feature.selection.datasets = NULL,
  feature.selection.p.thresholds = c(0.05),
  truncate.survival = 100,
  networks.database = "default",
  subset = NULL,
  ...
)
derive.network.features(
  data.directory = ".",
  output.directory = ".",
  data.types = c("mRNA"),
  data.types.ordinal = c("cna"),
  centre.data = "median",
  feature.selection.fun = "calculate.network.coefficients",
  feature.selection.datasets = NULL,
  feature.selection.p.thresholds = c(0.05),
  truncate.survival = 100,
  networks.database = "default",
  subset = NULL,
  ...
)

Arguments

`data.directory`	Path to the directory containing datasets as specified by `feature.selection.datasets`
`output.directory`	Path to the output folder where intermediate and results files will be saved
`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'
`feature.selection.fun`	Name of the function to be used to estimate network coefficients. Defaults to 'calculate.network.coefficients'
`feature.selection.datasets`	A vector containing names of training datasets to be used to compute cox statistics
`feature.selection.p.thresholds`	A vector containing P values to be used as threshold for including features into overall impact score of a network
`truncate.survival`	A numeric value specifying survival truncation in years. Defaults to 100 years which effectively means no truncation
`networks.database`	Name of the pathway networks database. Default to NCI PID/Reactome/Biocarta i-e "default"
`subset`	A list with a Field and Entry component specifying a subset of patients to be selected from each dataset whose annotation Field matches Entry
`...`	other params to be passed on to user-defined method for estimating coefficients of network features

Value

The output files are stored under data.directory/output/

Author(s)

Syed Haider

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");
feature.selection.p.thresholds <- c(0.05);

# estimate network coefficients for all the subnet features
derive.network.features(
  data.directory = data.directory,
  output.directory = output.directory,
  data.types = data.types,
  feature.selection.fun = "calculate.network.coefficients",
  feature.selection.datasets = feature.selection.datasets,
  feature.selection.p.thresholds = feature.selection.p.thresholds,
  networks.database = "test"
  );

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");
feature.selection.p.thresholds <- c(0.05);

# estimate network coefficients for all the subnet features
derive.network.features(
  data.directory = data.directory,
  output.directory = output.directory,
  data.types = data.types,
  feature.selection.fun = "calculate.network.coefficients",
  feature.selection.datasets = feature.selection.datasets,
  feature.selection.p.thresholds = feature.selection.p.thresholds,
  networks.database = "test"
  );

Dichotomize a single dataset

Description

Split a dataset into two groups by median-dichotomization

Usage

dichotomize.dataset(x, split.at = "median")
dichotomize.dataset(x, split.at = "median")

Arguments

`x`	A vector of values to be dichotomized
`split.at`	An character string or a numeric value that is be used to dichotomize. Valid values are: 'median', 'mean', or a user defined numeric threshold. Defaults to 'median'

Value

A vector of the data dichotomized onto a 0/1 (low/high) scale.

Author(s)

Syed Haider & Paul C. Boutros

Examples


tmp <- rnorm(100);
tmp.groups.median <- dichotomize.dataset(tmp);
tmp.groups.mean <- dichotomize.dataset(tmp, split.at = "mean");
tmp.groups.custom <- dichotomize.dataset(tmp, split.at = 0.3);

tmp <- rnorm(100);
tmp.groups.median <- dichotomize.dataset(tmp);
tmp.groups.mean <- dichotomize.dataset(tmp, split.at = "mean");
tmp.groups.custom <- dichotomize.dataset(tmp, split.at = 0.3);

Dichotomize and unlist a meta-analysis list

Description

Takes a meta-analysis list (and possibly extra data) and dichotomizes based on a specific gene, then returns the unlisted data to the caller.

Usage

dichotomize.meta.dataset(
  feature.name,
  expression.data,
  survival.data,
  other.data = NULL,
  data.type.ordinal = FALSE,
  centre.data = "median"
)
dichotomize.meta.dataset(
  feature.name,
  expression.data,
  survival.data,
  other.data = NULL,
  data.type.ordinal = FALSE,
  centre.data = "median"
)

Arguments

`feature.name`	Character indicate what feature (gene/probe/etc.) should be extracted for analysis
`expression.data`	A list where each component is an expression matrix (patients = columns, genes = rows) for a different dataset
`survival.data`	A list where each component is an object of class Surv
`other.data`	A list of other covariates to be unlisted in the final output (all elements in this list are used)
`data.type.ordinal`	Logical indicating whether to treat this datatype as ordinal. Defaults to FALSE
`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'

Details

NB: other.data handling of missing components (i.e. those present in only some datasets) has not been debugged (but may work regardless).

Value

Returns a list containing components groups (after dichotomization), survtime (in the units of the input data), and survstat. Additional vectors are unlisted from other.data if that parameter is not NULL.

Author(s)

Syed Haider & Paul C. Boutros

Examples


data.directory <- get.program.defaults()[["test.data.dir"]];
data.types <- c("mRNA");
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = data.types,
  data.directory = data.directory
  );
x2 <- dichotomize.meta.dataset(
  feature.name = "1000_at",
  expression.data = x1$all.data[[data.types[1]]],
  survival.data = x1$all.survobj
  );

data.directory <- get.program.defaults()[["test.data.dir"]];
data.types <- c("mRNA");
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = data.types,
  data.directory = data.directory
  );
x2 <- dichotomize.meta.dataset(
  feature.name = "1000_at",
  expression.data = x1$all.data[[data.types[1]]],
  survival.data = x1$all.survobj
  );

Fit a Cox proportional hazards model

Description

Fit a Cox model (possibly with some linear adjustments) and return key statistics about the fit.

Usage

fit.coxmodel(
  groups,
  survobj,
  stages = NA,
  rounding = 3,
  other.data = NULL,
  data.type.ordinal = FALSE
)
fit.coxmodel(
  groups,
  survobj,
  stages = NA,
  rounding = 3,
  other.data = NULL,
  data.type.ordinal = FALSE
)

Arguments

`groups`	Grouping of patients (passed directly to coxph, so factors & continuous variables are okay)
`survobj`	An object of class Surv (from the survival package) – patient ordering needs to be identical as for groups
`stages`	DEPRECATED! Use other.data instead.
`rounding`	How many digits of precision should be returned?
`other.data`	A data-frame (or matrix?) of variables to be controlled in the Cox model. If null, no adjustment is done. No interactions are fit.
`data.type.ordinal`	Logical indicating whether to treat this datatype as ordinal. Defaults to FALSE

Value

A list containing two elements. cox.stats containing a vector or matrix: HR, lower 95% CI of HR, upper 95% CI of HR, P-value (for groups), number of samples (total with group assignments, although some may not be included in fit for other reasons so this is an upper-limit). cox.obj containing coxph model object

Author(s)

Syed Haider & Paul C. Boutros

Examples


survtime <- sample(seq(0.1,10,0.1), 100, replace = TRUE);
survstat <- sample(c(0,1), 100, replace = TRUE);
survobj <- Surv(survtime, survstat);
groups <- sample(c('A','B'), 100, replace = TRUE);
fit.coxmodel(
  groups = as.factor(groups),
  survobj = survobj
  );

survtime <- sample(seq(0.1,10,0.1), 100, replace = TRUE);
survstat <- sample(c(0,1), 100, replace = TRUE);
survobj <- Surv(survtime, survstat);
groups <- sample(c('A','B'), 100, replace = TRUE);
fit.coxmodel(
  groups = as.factor(groups),
  survobj = survobj
  );

Cox model two features separately and together

Description

Using a meta-analysis dataset take two features and Cox model them separately and together and extract HRs and p-values.

Usage

fit.interaction.model(
  feature1,
  feature2,
  expression.data,
  survival.data,
  data.type.ordinal = FALSE,
  centre.data = "median"
)
fit.interaction.model(
  feature1,
  feature2,
  expression.data,
  survival.data,
  data.type.ordinal = FALSE,
  centre.data = "median"
)

Arguments

`feature1`	String indicate what feature (gene/probe/etc.) should be extracted for analysis
`feature2`	String indicate what feature (gene/probe/etc.) should be extracted for analysis
`expression.data`	A list where each component is an expression matrix (patients = columns, features = rows) for a different dataset
`survival.data`	A list where each component is an object of class Surv
`data.type.ordinal`	Logical indicating whether to treat this datatype as ordinal. Defaults to FALSE
`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'

Details

The interaction model compares cases where feature1 and feature2 concord (both high or both low) to those where they do not. That is, the model is y = x1 + x2 + (x1 == x2) and not the typical y = x1 + x2 + x1:x2

Value

Returns a vector of six elements containing (HR,P) pairs for feature1, feature2, and the interaction

Author(s)

Syed Haider & Paul C. Boutros

Examples


data.dir <- get.program.defaults()[["test.data.dir"]];
data.types <- c("mRNA");
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = data.types,
  data.directory = data.dir
  );
x2 <- fit.interaction.model(
  feature1 = "1000_at", 
  feature2 = "2549_at",
  expression.data = x1$all.data[[data.types[1]]],
  survival.data = x1$all.survobj
  );

data.dir <- get.program.defaults()[["test.data.dir"]];
data.types <- c("mRNA");
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = data.types,
  data.directory = data.dir
  );
x2 <- fit.interaction.model(
  feature1 = "1000_at", 
  feature2 = "2549_at",
  expression.data = x1$all.data[[data.types[1]]],
  survival.data = x1$all.survobj
  );

Trains a multivariate survival model

Description

Trains a multivariate survival model and conducts feature selection using both backward elimination and forward selection, independently. TO BE DEPRECATED AND HAS BEEN REPLACED BY create.classifier.multivariate

Usage

fit.survivalmodel(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3")
)
fit.survivalmodel(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3")
)

Arguments

`data.directory`	Path to the directory containing datasets as specified by `feature.selection.datasets`, `training.datasets`
`output.directory`	Path to the output folder where intermediate and results files will be saved
`feature.selection.datasets`	A vector containing names of datasets used for feature selection in function `derive.network.features()`
`feature.selection.p.threshold`	One of the P values that were used for feature selection in function `derive.network.features()`. This function does not support vector of P values as used in `derive.network.features()` for performance reasons
`training.datasets`	A vector containing names of training datasets to be used to train multivariate survival model
`top.n.features`	A numeric value specifying how many top ranked features will be used to train the multivariate survival model
`models`	A character vector specifying which models ('1' = N+E, '2' = N, '3' = E) to run

Value

The output files are stored under output.directory/output/

Author(s)

Syed Haider

Examples


# see package's main documentation

# see package's main documentation

A utility function to convert tab delimited networks file into adjacency matrices

Description

A utility function to convert tab-delimited networks file into adjacency matrices

Usage

get.adjacency.matrix(subnets.file = NULL)
get.adjacency.matrix(subnets.file = NULL)

Arguments

subnets.file

A tab-delimited file containing networks. New networks start with a new line with '#' at the begining of network name and subsequent lines contain a binary interaction per line

Value

A list of adjacency matrices

Author(s)

Syed Haider

Examples


subnets.file <- get.program.defaults()[["subnets.file"]];
all.adjacency.matrices <- get.adjacency.matrix(subnets.file);

subnets.file <- get.program.defaults()[["subnets.file"]];
all.adjacency.matrices <- get.adjacency.matrix(subnets.file);

Applies survdiff function

Description

Applies survdiff on different prognoses groups and computes Logrank P using chisquare statistics.

Usage

get.chisq.stats(groups, survobj)
get.chisq.stats(groups, survobj)

Arguments

`groups`	Grouping of patients (passed directly to survdiff, so factors & continuous variables are okay)
`survobj`	An object of class Surv (from the survival package) – patient ordering needs to be identical as for groups

Value

A vector containing: Chisq, degrees of freedom (DOF) and Logrank P-value.

Author(s)

Syed Haider

Examples


survtime <- sample(seq(0.1,10,0.1), 100, replace = TRUE);
survstat <- sample(c(0,1), 100, replace = TRUE);
survobj <- Surv(survtime, survstat);
groups <- sample(c('A','B'), 100, replace = TRUE);
get.chisq.stats(
  groups = as.factor(groups),
  survobj = survobj
  );

survtime <- sample(seq(0.1,10,0.1), 100, replace = TRUE);
survstat <- sample(c(0,1), 100, replace = TRUE);
survobj <- Surv(survtime, survstat);
groups <- sample(c('A','B'), 100, replace = TRUE);
get.chisq.stats(
  groups = as.factor(groups),
  survobj = survobj
  );

A utility function to return the inst/ directory of the installed package and other default settings

Description

A utility function to return the inst/ directory of the installed package to get the test datasets and other program related data contents

Usage

get.program.defaults(networks.database = "default")
get.program.defaults(networks.database = "default")

Arguments

networks.database

Name of the pathway networks database. Default to NCI PID/Reactome/Biocarta i-e "default"

Value

Returns a list of paths to the input directories/files where the contents of this package are installed

Author(s)

Syed Haider

Examples


program.data <- get.program.defaults();

program.data <- get.program.defaults();

Load all cancer meta-analysis datasets

Description

Returns a list of lists containing all cancer meta-analysis datasets

Usage

load.cancer.datasets(
  tumour.only = TRUE,
  with.survival.only = TRUE,
  truncate.survival = 100,
  datasets.to.load = "all",
  data.types = c("mRNA"),
  datasets.file = "datasets.txt",
  data.directory = ".",
  verbose = FALSE,
  subset = NULL
)
load.cancer.datasets(
  tumour.only = TRUE,
  with.survival.only = TRUE,
  truncate.survival = 100,
  datasets.to.load = "all",
  data.types = c("mRNA"),
  datasets.file = "datasets.txt",
  data.directory = ".",
  verbose = FALSE,
  subset = NULL
)

Arguments

`tumour.only`	Logical indicating if we should only load tumour samples (TRUE, the default)
`with.survival.only`	Logical indicating if we should only load samples with survival data (TRUE, the default)
`truncate.survival`	A numeric value specifying survival truncation in years. Defaults to 100 years which effectively means no truncation
`datasets.to.load`	A vector of datasets to be loaded. If 'all', then all available datasets are loaded
`data.types`	A vector of molecular datatypes to load. Defaults to c('mRNA')
`datasets.file`	A file in data.directory containing a listing of all usable datasets
`data.directory`	A directory containing all data-files to be loaded
`verbose`	Logical indicating whether or not status messages should be given
`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 meta-analysis list of lists

Author(s)

Syed Haider & Paul C. Boutros

Examples


data.dir <- get.program.defaults()[["test.data.dir"]];
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = c("mRNA"),
  data.directory = data.dir
  );

data.dir <- get.program.defaults()[["test.data.dir"]];
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = c("mRNA"),
  data.directory = data.dir
  );

Utility function used by `get.adjacency.matrix()`

Description

Utility function used by get.adjacency.matrix()

Usage

make.matrix(vertices, interactions)
make.matrix(vertices, interactions)

Arguments

`vertices`	Comma separated list of nodes
`interactions`	Comma separated list of edges

Value

Returns adjacency matrix

Author(s)

Syed Haider

Examples


x1 <- make.matrix("a,b,c", "a:b,b:c");

x1 <- make.matrix("a,b,c", "a:b,b:c");

Apply a multivariate survival model to validation datasets

Description

Predicts the risk score for all the training & validation datasets, independently. This function also predicts the risk score for combined training datasets cohort and validation datasets cohort. The risk score estimation is done by multivariate models fit by fit.survivalmodel. The function also predicts risk scores for each of the top.n.features independently. TO BE DEPRECATED AND HAS BEEN REPLACED BY create.classifier.multivariate

Usage

pred.survivalmodel(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3"),
  write.risk.data = TRUE
)
pred.survivalmodel(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3"),
  write.risk.data = TRUE
)

Arguments

`data.directory`	Path to the directory containing datasets as specified by `feature.selection.datasets`, `training.datasets`, `validation.datasets`
`output.directory`	Path to the output folder where intermediate and results files will be saved
`feature.selection.datasets`	A vector containing names of datasets used for feature selection in function `derive.network.features()`
`feature.selection.p.threshold`	One of the P values that were used for feature selection in function `derive.network.features()`. This function does not support vector of P values as used in `derive.network.features()` for performance reasons
`training.datasets`	A vector containing names of training datasets
`validation.datasets`	A vector containing names of validation datasets
`top.n.features`	A numeric value specifying how many top ranked features will be used for univariate survival modelling
`models`	A character vector specifying which of the models ('1' = N+E, '2' = N, '3' = E) to run
`write.risk.data`	A toggle to control whether risk scores and patient risk groups should be written to file

Value

The output files are stored under output.directory/output/

Author(s)

Syed Haider

Examples


# see package's main documentation

# see package's main documentation

Prepare training and validation datasets

Description

Computes per-patient pathway-derived network impact scores across all input datasets, independently

Usage

prepare.training.validation.datasets(
  data.directory = ".",
  output.directory = ".",
  data.types = c("mRNA"),
  data.types.ordinal = c("cna"),
  min.ordinal.threshold = c(cna = 3),
  centre.data = "median",
  p.threshold = 0.5,
  feature.selection.datasets = NULL,
  datasets = NULL,
  truncate.survival = 100,
  networks.database = "default",
  write.normed.datasets = TRUE,
  subset = NULL
)
prepare.training.validation.datasets(
  data.directory = ".",
  output.directory = ".",
  data.types = c("mRNA"),
  data.types.ordinal = c("cna"),
  min.ordinal.threshold = c(cna = 3),
  centre.data = "median",
  p.threshold = 0.5,
  feature.selection.datasets = NULL,
  datasets = NULL,
  truncate.survival = 100,
  networks.database = "default",
  write.normed.datasets = TRUE,
  subset = NULL
)

Arguments

`data.directory`	Path to the directory containing datasets as specified by `datasets`
`output.directory`	Path to the output folder where intermediate and results files will be saved
`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')
`min.ordinal.threshold`	A named vector specifying minimum percent threshold for each ordinal data type to be used prior to estimating coefficients. Coefficient for features not satisfying minimum threshold will not be estimated, and set to 0. Defaults to cna threshold as 3 percent
`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'
`p.threshold`	Cox P value threshold to be applied for selecting features (e.g. genes) which will contribute to patient risk score estimation. Defaults to 0.5
`feature.selection.datasets`	A vector containing names of datasets used for feature selection in function `derive.network.features()`
`datasets`	A vector containing names of all the datasets to be later used for training and validation purposes
`truncate.survival`	A numeric value specifying survival truncation in years. Defaults to 100 years which effectively means no truncation
`networks.database`	Name of the pathway networks database. Default to NCI PID/Reactome/Biocarta i-e "default"
`write.normed.datasets`	A toggle to control whether processed mRNA and survival data should be written to file
`subset`	A list with a Field and Entry component specifying a subset of patients to be selected whose annotation Field matches Entry

Value

The output files are stored under output.directory/output/

Author(s)

Syed Haider

Examples


# get data directory 
data.directory <- get.program.defaults()[["test.data.dir"]];

# initialise params
output.directory <- tempdir();
data.types <- c("mRNA");
feature.selection.datasets <- c("Breastdata1");
training.datasets <- c("Breastdata1");
validation.datasets <- c("Breastdata1", "Breastdata2");

# 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,
  feature.selection.datasets = feature.selection.datasets,
  datasets = unique(c(training.datasets, validation.datasets)),
  networks.database = "test"
  );

# get data directory 
data.directory <- get.program.defaults()[["test.data.dir"]];

# initialise params
output.directory <- tempdir();
data.types <- c("mRNA");
feature.selection.datasets <- c("Breastdata1");
training.datasets <- c("Breastdata1");
validation.datasets <- c("Breastdata1", "Breastdata2");

# 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,
  feature.selection.datasets = feature.selection.datasets,
  datasets = unique(c(training.datasets, validation.datasets)),
  networks.database = "test"
  );

Package 'SIMMS'

Help Index

SIMMS - Subnetwork Integration for Multi-Modal Signatures

Description

Details

Author(s)

Examples

Fit a meta-analytic Cox proportional hazards model to a single feature

Description

Usage

Arguments

Value

Author(s)

Examples

Calculate Cox statistics for input dataset

Description

Usage

Arguments

Value

Author(s)

Examples

Computes sensitivity measures

Description

Usage

Arguments

Value

Author(s)

Centre and scale a data matrix

Description

Usage

Arguments

Value

Author(s)

Examples

Trains and tests a multivariate survival model

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Trains and tests a univariate (per subnetwork module) survival model

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Plots Kaplan-meier survival curve for a given risk grouping & survival params

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Plots sensitivity analysis for class label dichotomization at supplied survtime cutoffs

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Plots Kaplan-meier survival curves

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Utility function for loading meta-analysis lists

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Derive univariate features from pathway-derived networks

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Utility function used by `get.adjacency.matrix()`