Nothing
## ----xtable, echo=FALSE, results="asis"---------------------------------------
library(xtable);
x <- matrix(
data = c(
"{ *x ∈ ℝ* : *x ≥ 0* }", "0, 2, 1.37, 7.04, 9.68", "log2 mRNA or miRNA abundance",
"{ *x ∈ ℝ* : *x ≥ 0* }", "0.1, 0.38, 0.78, 0.22, 0.98", "DNA methylation beta values",
"{ *x ∈ ℤ* }", "-2, -1, 0, 1, 2", "copy-number calls. Reference group = 0 (Neutral/Diploid)",
"{ *x ∈ ℤ* : *x ≥ 0* }", "0, 1, 2, 3", "mutation data. Reference group = 0 (Wildtype)",
"{ *x ∈ ℤ* : *x ≠ 0* }", "-2, -1, 1, 2", "Unsupported due to missing reference group 0",
"{ *x ∉ ℝ* }", "WT, Mutant, Gain, Deleted", "Unsupported due to alphabets"
),
nrow = 6, ncol = 3, byrow = TRUE,
dimnames = list(
NULL,
c("Data type", "Example data", "Example profile")
)
);
tab <- xtable(x);
print(tab, type = "html", include.rownames = FALSE);
## ---- results = "hide", message = FALSE, eval = TRUE--------------------------
options("warn" = -1);
# load SIMMS library
library("SIMMS");
# path of the data directory containing Breastdata1/ and Breastdata2/ subdirectories
data.directory <- SIMMS::get.program.defaults(networks.database = "test")[["test.data.dir"]];
# path of the directory where results will be stored
output.directory <- tempdir();
# molecular profiles to be used
data.types <- c("mRNA");
# feature selection datasets
# name of the dataset directory containing mRNA abundance and annotation profiles of training dataset/s
feature.selection.datasets <- c("Breastdata1");
# model training datasets, ideally same as feature selection datasets
# name of the dataset directory containing mRNA abundance and annotation profiles of training dataset/s
training.datasets <- feature.selection.datasets;
# validation datasets
# name of the dataset directory containing mRNA abundance and annotation profiles of validation dataset/s
validation.datasets <- c("Breastdata2");
# all the possible P value thresholds that one may consider applying to feature selection process.
# its the P value of univariate (genewise) Cox model statistics
feature.selection.p.thresholds <- c(0.5);
# one of the P values above, to be used for subsequent analysis. Not a vector for performance reasons
feature.selection.p.threshold <- 0.5;
# names of the learning algorithms to be used for the final multivarite model
learning.algorithms <- c("backward", "forward", "glm", "randomforest");
# top features to be used for model selection (Backwards elimination, Forward selection, GLM, Random survival forest)
# you can try a number of different model selection runs by specifying a vector of top n features
top.n.features <- c(5);
# truncate survival
truncate.survival <- 10;
# calculate per feature univariate coefficients in training sets
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", # or "default" for Reactome/BioCarta/NCI-PID
truncate.survival = truncate.survival
);
# calculate per-subnetwork scores in both training and validation sets
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 = c(training.datasets, validation.datasets),
networks.database = "test", # or "default" for Reactome/BioCarta/NCI-PID
truncate.survival = truncate.survival
);
# iterate over varying top n features, identify and validate survival models
for (top.n in top.n.features) {
# create classifier assessing univariate prognostic power of subnetwork modules (Train and Validate)
ret <- 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
);
# create a multivariate classifier (Train and Validate)
ret <- 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
);
# perform Kaplan-Meier analysis and generate plots
create.survivalplots(
data.directory = data.directory,
output.directory = output.directory,
training.datasets = training.datasets,
validation.datasets = validation.datasets,
top.n.features = top.n,
learning.algorithms = learning.algorithms,
truncate.survival = truncate.survival,
survtime.cutoffs = c(5),
main.title = FALSE,
KM.plotting.fun = "create.KM.plot",
resolution = 100
);
}
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