Description Usage Arguments Details Value Author(s) References Examples
phenoDisco
is a semisupervised iterative approach to
detect new protein clusters.
1 2 3 4 
object 
An instance of class 
fcol 
A 
times 
Number of runs of tracking. Default is 100. 
GS 
Group size, i.e how many proteins make a group. Default is 10 (the minimum group size is 4). 
allIter 

p 
Significance level for outlier detection. Default is 0.05. 
ndims 
Number of principal components to use as input for the disocvery analysis. Default is 2. Added in version 1.3.9. 
modelNames 
A vector of characters indicating the models to
be fitted in the EM phase of clustering using

G 
An integer vector specifying the numbers of mixture
components (clusters) for which the BIC is to be
calculated. The default is 
BPPARAM 
Support for parallel processing using the

tmpfile 
An optional 
seed 
An optional 
verbose 
Logical, indicating if messages are to be printed out during execution of the algorithm. 
dimred 
A 
... 
Additional arguments passed to the dimensionality
reduction method. For both PCA and tSNE, the data is scaled
and centred by default, and these parameters ( 
The algorithm performs a phenotype discovery analysis as described in Breckels et al. Using this approach one can identify putative subcellular groupings in organelle proteomics experiments for more comprehensive validation in an unbiased fashion. The method is based on the work of Yin et al. and used iterated rounds of Gaussian Mixture Modelling using the Expectation Maximisation algorithm combined with a nonparametric outlier detection test to identify new phenotype clusters.
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
the filterNA
method.
A parallel implementation, relying on the BiocParallel
package, has been added in version 1.3.9. See the BPPARAM
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.
An instance of class MSnSet
containing the
phenoDisco
predictions.
Lisa M. Breckels <lms79@cam.ac.uk>
Yin Z, Zhou X, Bakal C, Li F, Sun Y, Perrimon N, Wong ST. Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of highthroughput RNAi screens. BMC Bioinformatics. 2008 Jun 5;9:264. PubMed PMID: 18534020.
Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS and Trotter MWB. The Effect of Organelle Discovery upon SubCellular Protein Localisation. J Proteomics. 2013 Aug 2;88:12940. doi: 10.1016/j.jprot.2013.02.019. Epub 2013 Mar 21. PubMed PMID: 23523639.
1 2 3 4 5 6 7 8 9 10 11  ## Not run:
library(pRolocdata)
data(tan2009r1)
pdres < phenoDisco(tan2009r1, fcol = "PLSDA")
getPredictions(pdres, fcol = "pd", scol = NULL)
plot2D(pdres, fcol = "pd")
## to preprocess the data with tSNE instead of PCA
pdres < phenoDisco(tan2009r1, fcol = "PLSDA", dimred = "tSNE")
## End(Not run)

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