tagm-map: The 'logPosteriors' function can be used to extract the...

In lgatto/pRoloc: A unifying bioinformatics framework for spatial proteomics

The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.

Description

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.

Usage

## S4 method for signature 'MAPParams'
show(object)

logPosteriors(x)

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
)

Arguments

object	An MSnbase::MSnSet containing the spatial proteomics data to be passed to tagmMapTrain and tagmPredict.
x	An object of class 'MAPParams'.
fcol	The feature meta-data containing marker definitions. Default is markers.
method	A charachter() describing the inference method for the TAGM algorithm. Default is "MAP".
numIter	The number of iterations of the expectation-maximisation algorithm. Default is 100.
mu0	The prior mean. Default is colMeans of the expression data.
lambda0	The prior shrinkage. Default is 0.01.
nu0	The prior degreed of freedom. Default is ncol(exprs(object)) + 2
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 MAPParams, as generated by tagmMapTrain().
probJoint	A logical(1) indicating whether to return the joint probability matrix, i.e. the probability for all classes as a new tagm.map.joint feature variable.
probOutlier	A logical(1) indicating whether to return the probability of being an outlier as a new tagm.map.outlier feature variable. A high value indicates that the protein is unlikely to belong to any annotated class (and is hence considered an outlier).

Details

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.

Value

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.

Slots

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

Author(s)

Laurent Gatto

Oliver M. Crook

References

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

See Also

The plotEllipse() function can be used to visualise TAGM models on PCA plots with ellipses. The tagmMapTrain() function to use the TAGM MAP method.

lgatto/pRoloc documentation built on Nov. 27, 2024, 12:14 a.m.