phenoDiscois a semi-supervised iterative approach to detect new protein clusters.
phenoDisco(object, fcol = "markers", times = 100, GS = 10, allIter = FALSE, p = 0.05, ndims = 2, modelNames = mclust.options("emModelNames"), G = 1:9, BPPARAM, tmpfile, seed, verbose = TRUE)
characterindicating the organellar markers column name in feature meta-data. Default is
logical, defining if predictions for all iterations should be saved. Default is
Mclust. The help file for
mclustModelNamesdescribes the available models. Default model names are
c("EII", "VII", "EEI", "VEI", "EVI", "VVI", "EEE", "EEV", "VEV", "VVV"), as returned by
mclust.options("emModelNames"). Note that using all these possible models substantially increases the running time. Legacy models are
c("EEE","EEV","VEV","VVV"), i.e. only ellipsoidal models.
BiocParallelinfrastructure. When missing (default), the default registered
BiocParallelParamparameters are used. Alternatively, one can pass a valid
DoparParam, ... see the
BiocParallelpackage for details. To revert to the origianl serial implementation, use
characterto save a temporary
MSnSetafter each iteration. Ignored if missing. This is useful for long runs to track phenotypes and possibly kill the run when convergence is observed. If the run completes, the temporary file is deleted before returning the final result.
numericof length 1 specifing the random number generator seed to be used. Only relevant when executed in serialised mode with
BPPARAM = NULL. See
One requires 2 or more classes to be labelled in the data and at a
very minimum of 6 markers per class to run the algorithm. The
function will check and remove features with missing values using
A parallel implementation, relying on the
package, has been added in version 1.3.9. See the
arguent for details.
Important: Prior to version 1.1.2 the row order in the output was different from the row order in the input. This has now been fixed and row ordering is now the same in both input and output objects.
Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS and Trotter MWB. The Effect of Organelle Discovery upon Sub-Cellular Protein Localisation. J Proteomics. 2013 Aug 2;88:129-40. doi: 10.1016/j.jprot.2013.02.019. Epub 2013 Mar 21. PubMed PMID: 23523639.
## Not run: # library(pRolocdata) # data(tan2009r1) # pdres <- phenoDisco(tan2009r1, fcol = "PLSDA") # getPredictions(pdres, fcol = "pd", scol = NULL) # plot2D(pdres, fcol = "pd") # ## End(Not run)
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