tagmMapTrain {pRoloc} | R Documentation |
These functions implement the T augmented Gaussian mixture (TAGM) model for mass spectrometry-based spatial proteomics datasets using the maximum a posteriori (MAP) optimisation routine.
tagmMapTrain(object, fcol = "markers", method = "MAP", numIter = 100, mu0 = NULL, lambda0 = 0.01, nu0 = NULL, S0 = NULL, beta0 = NULL, u = 2, v = 10, seed = NULL) tagmMapPredict(object, params, fcol = "markers", probJoint = FALSE, probOutlier = TRUE) ## S4 method for signature 'MAPParams' show(object) logPosteriors(x)
object |
An |
fcol |
The feature meta-data containing marker definitions.
Default is |
method |
A |
numIter |
The number of iterations of the expectation-maximisation algorithm. Default is 100. |
mu0 |
The prior mean. Default is |
lambda0 |
The prior shrinkage. Default is 0.01. |
nu0 |
The prior degreed of freedom. Default is
|
S0 |
The prior inverse-wishary scale matrix. Empirical prior used by default. |
beta0 |
The prior Dirichlet distribution concentration. Default is 1 for each class. |
u |
The prior shape parameter for Beta(u, v). Default is 2 |
v |
The prior shape parameter for Beta(u, v). Default is 10. |
seed |
The optional random number generator seed. |
params |
An instance of class |
probJoint |
A |
probOutlier |
A |
x |
An object of class 'MAPParams'. |
The tagmMapTrain
function generates the MAP parameters (object or class
MAPParams
) based on an annotated quantitative spatial proteomics dataset
(object of class MSnbase::MSnSet
). Both are then passed to the
tagmPredict
function to predict the sub-cellular localisation of protein
of unknown localisation. See the pRoloc-bayesian vignette for details and
examples. In this implementation, if numerical instability is detected in
the covariance matrix of the data a small multiple of the identity is
added. A message is printed if this conditioning step is performed.
tagmMapTrain
returns an instance of class MAPParams()
.
tagmPredict
returns an instance of class
MSnbase::MSnSet
containing the localisation predictions as
a new tagm.map.allocation
feature variable.
method
A character()
storing the TAGM method name.
priors
A list()
with the priors for the parameters
seed
An integer()
with the random number generation seed.
posteriors
A list()
with the updated posterior parameters
and log-posterior of the model.
datasize
A list()
with details about size of data
Oliver M. Crook
Laurent Gatto
A Bayesian Mixture Modelling Approach For Spatial Proteomics Oliver M Crook, Claire M Mulvey, Paul D. W. Kirk, Kathryn S Lilley, Laurent Gatto bioRxiv 282269; doi: https://doi.org/10.1101/282269
The plotEllipse()
function can be used to visualise
TAGM models on PCA plots with ellipses. The tagmMapTrain()
function to use the TAGM MAP method.