| Version: | 1.3.2 |
| Type: | Package |
| Title: | Subnetwork Integration for Multi-Modal Signatures |
| Date: | 2022-04-22 |
| Author: | Syed Haider [aut, cre], Paul C. Boutros [aut], Michal Grzadkowski [ctb] |
| Maintainer: | Syed Haider <Syed.Haider@icr.ac.uk> |
| Imports: | randomForestSRC (≥ 2.9.1) |
| Depends: | R (≥ 3.2.0), survival (≥ 2.36-2), MASS (≥ 7.3-12), glmnet (≥ 1.9-8), doParallel (≥ 1.0.10), foreach (≥ 1.4.3) |
| Description: | Algorithms to create prognostic biomarkers using biological genesets or networks. |
| License: | GPL-2 |
| LazyLoad: | yes |
| Encoding: | UTF-8 |
| Suggests: | knitr (≥ 1.4), rmarkdown (≥ 0.9.5), xtable (≥ 1.7-4) |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2022-04-22 15:11:30 UTC; shaider |
| Repository: | CRAN |
| Date/Publication: | 2022-04-24 14:50:05 UTC |
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
);
}
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"
)
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
);
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 |
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
);
Computes sensitivity measures
Description
Computes sensitivity measures: TP, FP, TN, FN, Sensitivity, Specificity, Accuracy
Usage
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")
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);
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
)
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
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 },
...
)
Arguments
data.directory |
Path to the directory containing datasets as specified
by |
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 |
feature.selection.p.threshold |
One of the P values that were used for
feature selection in function |
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 |
seed.value |
A numeric value specifying seed for glm k-fold cross or random forest
validation if glm was chosen in |
cores.glm |
An integer value specifying number of cores to be used for
glm if it was chosen in |
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 |
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 |
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 |
... |
other params to be passed on to the random forest call to the underlying
R package random survival forest |
Value
The output files are stored under output.directory/output/
Author(s)
Syed Haider & Vincent Stimper
Examples
# see package's main documentation
Trains and tests a univariate (per subnetwork module) 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.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 |
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 |
feature.selection.p.threshold |
One of the P values that were used for
feature selection in function |
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
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
)
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
)
Arguments
data.directory |
Path to the directory containing datasets as specified
by |
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 |
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
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)
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);
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,
...
)
Arguments
data.directory |
Path to the directory containing datasets as specified
by |
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"
);
Dichotomize a single dataset
Description
Split a dataset into two groups by median-dichotomization
Usage
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);
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"
)
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
);
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
)
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
);
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"
)
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
);
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")
)
Arguments
data.directory |
Path to the directory containing datasets as specified
by |
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 |
feature.selection.p.threshold |
One of the P values that were used for
feature selection in function |
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
See Also
create.classifier.multivariate
Examples
# 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)
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);
Applies survdiff function
Description
Applies survdiff on different prognoses groups and computes Logrank P using chisquare statistics.
Usage
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
);
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")
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();
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
)
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
);
Utility function used by get.adjacency.matrix()
Description
Utility function used by get.adjacency.matrix()
Usage
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");
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
)
Arguments
data.directory |
Path to the directory containing datasets as specified
by |
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 |
feature.selection.p.threshold |
One of the P values that were used for
feature selection in function |
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
See Also
create.classifier.multivariate
Examples
# 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
)
Arguments
data.directory |
Path to the directory containing datasets as specified
by |
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 |
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"
);