phenoDisco
is 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,
dimred = c("PCA", "t-SNE"),
...
)
An instance of class MSnSet
.
A character
indicating the organellar markers
column name in feature meta-data. Default is markers
.
Number of runs of tracking. Default is 100.
Group size, i.e how many proteins make a group. Default is 10 (the minimum group size is 4).
logical
, defining if predictions for all
iterations should be saved. Default is FALSE
.
Significance level for outlier detection. Default is 0.05.
Number of principal components to use as input for the disocvery analysis. Default is 2. Added in version 1.3.9.
A vector of characters indicating the models to
be fitted in the EM phase of clustering using
Mclust
. The help file for mclust::mclustModelNames
describes 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.
An integer vector specifying the numbers of mixture
components (clusters) for which the BIC is to be
calculated. The default is G=1:9
(as in Mclust
).
Support for parallel processing using the
BiocParallel
infrastructure. When missing (default),
the default registered BiocParallelParam
parameters are
used. Alternatively, one can pass a valid
BiocParallelParam
parameter instance: SnowParam
,
MulticoreParam
, DoparParam
, ... see the
BiocParallel
package for details. To revert to the
origianl serial implementation, use NULL
.
An optional character
to save a temporary
MSnSet
after 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.
An optional numeric
of length 1 specifing the
random number generator seed to be used. Only relevant when
executed in serialised mode with BPPARAM = NULL
. See
BPPARAM
for details.
Logical, indicating if messages are to be printed out during execution of the algorithm.
A characater
defining which of Principal
Component Analysis ("PCA"
) or t-Distributed Stochastic
Neighbour Embedding ("t-SNE"
) should be use to reduce
dimensions prior to running phenoDisco novelty detection.
Additional arguments passed to the dimensionality
reduction method. For both PCA and t-SNE, the data is scaled
and centred by default, and these parameters (scale
and
centre
for PCA, and pca_scale
and
pca_center
for t-SNE can't be set). When using t-SNE
however, it is important to tune the perplexity and max
iterations parameters. See the Dimensionality reduction
section in the pRoloc vignette for details.
An instance of class MSnSet
containing the
phenoDisco
predictions.
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 non-parametric 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.
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 high-throughput 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 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.
if (FALSE) {
library(pRolocdata)
data(tan2009r1)
pdres <- phenoDisco(tan2009r1, fcol = "PLSDA")
getPredictions(pdres, fcol = "pd", scol = NULL)
plot2D(pdres, fcol = "pd")
## to pre-process the data with t-SNE instead of PCA
pdres <- phenoDisco(tan2009r1, fcol = "PLSDA", dimred = "t-SNE")
}