CoGA analyses are also available in the R command line interface. Below, we describe how to perform the differential network analyses. For a detailed description of the function parameters and return values, check it out in the R command line interface, by typing the command ?[function_name].
expr <- readExprTxtFile([path_to_txt_file])
labels <- readClsFile([path_to_cls_file])
annotation <- readChipFile([path_to_chip_file])
collapsingMethod <- [collapsing_method_name] connectivityBasedCollapsing <- list("connectivityBasedCollapsing"=TRUE_or_FALSE) expr <- collapseExprData(expr, annotation, collapsingMethod, connectivityBasedCollapsing)
The available options for [collapsing_method_name] are:
If [collapsing_method_name] is "me", the "connectivityBasedCollapsing" parameter will be ignored.
geneSets <- readGmtFile([path_to_gmt_file])
For using the absolute correlation coefficient as the association measure, create the following variables:
networkInference <- [correlation_measure_name]Cor
abs <- TRUE
pvalue <- FALSE
fdr <- FALSE
For using one minus the test p-value to measure the association between the gene products, create the following variables:
networkInference <- [correlation_measure_name]Test
abs <- TRUE
pvalue <- TRUE
fdr <- FALSE
For using one minus the test q-value as the association measure, create the following variables:
networkInference <- [correlation_measure_name]Test
abs <- TRUE
pvalue <- TRUE
fdr <- TRUE
The [correlation_measure_name] must be replaced by one of the following options:
If your graph is weighted, you must set the following variables:
weighted <- TRUE threshold <- NULL
If the graph is unweighted, then set them to:
weighted <- FALSE threshold <- value
Replaces "value" by the desired threshold (only gene links with value higher then "value" will remain in the network).
Then, create the function for performing the network inference:
adjacencyMatrix <- adjacencyMatrix(networkInference, abs, pvalue, fdr, weighted, threshold)
method <- [network_test_name]
If the graph is unweighted, the available values for [network_test_name] are:
For weighted networks, the available methods are:
numPermutations < [value]
Where [value] is the desired number of sample permutations
To use different sample label permutations for each gene set, define the "seed" as NULL:
seed < NULL
Otherwise, set it to the desired value:
seed < [value]
If [network_test_name] is "spectralDensityTest", "spectralEntropyTest" or "degreeDistributionTest" you must set a criterion for the bandwidth selection:
options <- list("bandwidth"="Sturges")
or:
options <- list("bandwidth"="Silverman")
To save the partial results in a file, set the parameter:
resultsFile <- [path_to_results_file]
If [path_to_results_file] is NULL, then the partial results will not be saved.
To print execution messages, set the parameter "print" to TRUE:
print <- TRUE
To print execution messages, set the parameter "print" to TRUE:
print <- TRUE
To perform the differential network analysis, type the command:
results <- diffNetAnalysis(method, options, expr, labels, geneSets, adjacencyMatrix, numPermutations, print, resultsFile, seed)