tagm-mcmc: Localisation of proteins using the TAGM MCMC method
In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description Usage Arguments Details Value References See Also

These functions implement the T augmented Gaussian mixture (TAGM) model for mass spectrometry-based spatial proteomics datasets using Markov-chain Monte-Carlo (MCMC) for inference.

tagmMcmcTrain(object, fcol = "markers", method = "MCMC",
  numIter = 1000L, burnin = 100L, thin = 5L, mu0 = NULL,
  lambda0 = 0.01, nu0 = NULL, S0 = NULL, beta0 = NULL, u = 2,
  v = 10, numChains = 4L, BPPARAM = BiocParallel::bpparam())

tagmMcmcPredict(object, params, fcol = "markers", probJoint = FALSE,
  probOutlier = TRUE)

tagmPredict(object, params, fcol = "markers", probJoint = FALSE,
  probOutlier = TRUE)

tagmMcmcProcess(params)

`object`	An `MSnbase::MSnSet` containing the spatial proteomics data to be passed to `tagmMcmcTrain` and `tagmPredict`.
`fcol`	The feature meta-data containing marker definitions. Default is `markers`.
`method`	A `charachter()` describing the inference method for the TAGM algorithm. Default is `"MCMC"`.
`numIter`	The number of iterations of the MCMC algorithm. Default is 1000.
`burnin`	The number of samples to be discarded from the begining of the chain. Default is 100.
`thin`	The thinning frequency to be applied to the MCMC chain. Default is 5.
`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-wishart 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.
`numChains`	The number of parrallel chains to be run. Default it 4.
`BPPARAM`	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.
`params`	An instance of class `MCMCParams`, as generated by `tagmMcmcTrain()`.
`probJoint`	A `logical(1)` indicating whether to return the joint probability matrix, i.e. the probability for all classes as a new `tagm.mcmc.joint` feature variable.
`probOutlier`	A `logical(1)` indicating whether to return the probability of being an outlier as a new `tagm.mcmc.outlier` feature variable. A high value indicates that the protein is unlikely to belong to any annotated class (and is hence considered an outlier).

The tagmMcmcTrain function generates the samples from the posterior distributions (object or class MCMCParams) 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.

tagmMcmcTrain returns an instance of class MCMCParams.

tagmMcmcPredict returns an instance of class MSnbase::MSnSet containing the localisation predictions as a new tagm.mcmc.allocation feature variable. The allocation probability is encoded as tagm.mcmc.probability (corresponding to the mean of the distribution probability). In additionm the upper and lower quantiles of the allocation probability distribution are available as tagm.mcmc.probability.lowerquantile and tagm.mcmc.probability.upperquantile feature variables. The Shannon entropy is available in the tagm.mcmc.mean.shannon feature variable, measuring the uncertainty in the allocations (a high value representing high uncertainty; the highest value is the natural logarithm of the number of classes).

tagmMcmcProcess returns an instance of class MCMCParams with its summary slot populated.

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.

pRoloc documentation built on Nov. 8, 2020, 6:26 p.m.