| dcPipeline {dcanr} | R Documentation |
Run a differential co-expression pipeline on data from a simulation experiment. A default pipeline can be used which consists of methods in the package or custom pipelines can be provided.
dcPipeline(simulation, dc.func = "zscore", precomputed = FALSE, continuous = FALSE, ...)
simulation |
a list, storing data and results generated from simulations |
dc.func |
a function or character. Character represents one of the
method names from |
precomputed |
a logical, indicating whether the precomputed inference should be used or a new one computed (default FALSE) |
continuous |
a logical, indicating whether binary or continuous conditions should be used (default FALSE). No methods implemented currently use continuous conditions. This is to allow custom methods that require continuous conditions |
... |
additional parameters to |
If dc.func is a character, the existing methods in the
package will be run with their default parameters. The pipeline is as such:
dcScore -> dcTest -> dcAdjust -> dcNetwork, resulting in a igraph object.
Parameters to the independent processing steps can also be provided to this
function as shown in the examples.
If precomputed is TRUE while dc.func is a character,
pre-computed results will be used. These can then be evaluated using
dcEvaluate.
Custom pipelines need to be coded into a function which can then be provided instead of a character. Functions must have the following structure:
function(emat, condition, ...)
They must return either an igraph object or an adjacency matrix stored in a base R 'matrix' or the S4 'Matrix' class, containing all genes in the expression matrix 'emat'. See examples for how the in-built functions are combined into a pipeline.
a list of igraphs, representing the differential network for each independent condition (knock-out).
plot.igraph, dcScore,
dcTest, dcAdjust, dcNetwork,
dcMethods
data(sim102)
#run a standard pipeline
resStd <- dcPipeline(sim102, dc.func = 'zscore')
#run a standard pipeline and specify params
resParam <- dcPipeline(sim102, dc.func = 'zscore', cor.method = 'pearson')
#retrieve pre-computed results
resPrecomputed <- dcPipeline(sim102, dc.func = 'zscore', precomputed = TRUE)
#run a custom pipeline
analysisInbuilt <- function(emat, condition, dc.method = 'zscore', ...) {
#compute scores
score = dcScore(emat, condition, dc.method, ...)
#perform statistical test
pvals = dcTest(score, emat, condition, ...)
#adjust tests for multiple testing
adjp = dcAdjust(pvals, ...)
#threshold and generate network
dcnet = dcNetwork(score, adjp, ...)
return(dcnet)
}
resCustom <- dcPipeline(sim102, dc.func = analysisInbuilt)
plot(resCustom[[1]])