msqrobAggregate: Method to fit msqrob models with robust regression and/or...

In statOmics/msqrob2: Robust statistical inference for quantitative LC-MS proteomics

Method to fit msqrob models with robust regression and/or ridge regression and/or random effects It models multiple features simultaneously, e.g. multiple peptides from the same protein.

Description

Parameter estimation of msqrob models for QFeaturesinstance. The method aggregates features within the model e.g. from peptides to proteins. It provides fold change estimates and their associated uncertainty at the aggregated level (e.g. protein level) while correcting for the peptide species that are observed in each sample. It also addresses the correlation in the data, e.g. the peptide data for the same protein in a sample are correlate because they originate from the same protein pool. The method however does not return aggregated expression values for each sample. For visualisation purposes aggregated expression values are provide by the aggregateFeatures function from the QFeatures Package

Usage

## S4 method for signature 'QFeatures'
msqrobAggregate(
  object,
  formula,
  i,
  fcol,
  name = "msqrobAggregate",
  aggregateFun = MsCoreUtils::robustSummary,
  modelColumnName = "msqrobModels",
  robust = TRUE,
  ridge = FALSE,
  maxitRob = 1,
  tol = 1e-06,
  doQR = TRUE,
  lmerArgs = list(control = lmerControl(calc.derivs = FALSE))
)

Arguments

object	QFeatures instance
formula	Model formula. The model is built based on the covariates in the data object.
i	character or integer to specify the element of the QFeatures that contains the log expression intensities that will be modelled.
fcol	The feature variable of assay ‘i’ defining how to summerise the features.
name	A ‘character(1)’ naming the new assay. Default is ‘newAssay’. Note that the function will fail if there's already an assay with ‘name’.
aggregateFun	A function used for quantitative feature aggregation. Details can be found in the documentation of the aggregateFeatures of the QFeatures package.
modelColumnName	character to indicate the variable name that is used to store the msqrob models in the rowData of the SummarizedExperiment instance or of the assay of the QFeatures instance. Default is "msqrobModels".
robust	boolean(1) to indicate if robust regression is performed to account for outliers. Default is TRUE. If FALSE an OLS fit is performed.
ridge	boolean(1) to indicate if ridge regression is performed. Default is FALSE. If TRUE the fixed effects are estimated via penalized regression and shrunken to zero.
maxitRob	numeric(1) indicating the maximum iterations in the IRWLS algorithm used in the M-estimation step of the robust regression.
tol	numeric(1) indicating the tolerance for declaring convergence of the M-estimation loop.
doQR	boolean(1) to indicate if QR decomposition is used when adopting ridge regression. Default is TRUE. If FALSE the predictors of the fixed effects are not transformed, and the degree of shrinkage can depend on the encoding.
lmerArgs	a list (of correct class, resulting from ‘lmerControl()’ containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the ‘lmerControl’ documentation of the lme4 package for more details. Default is list(control = lmerControl(calc.derivs = FALSE))

Value

A ‘QFeatures’ object with an additional assay.

Author(s)

Lieven Clement

Examples

# Load example data
# The data are a Feature object with containing
# a SummarizedExperiment named "peptide" with MaxQuant peptide intensities
# The data are a subset of spike-in the human-ecoli study
# The variable condition in the colData of the Feature object
# contains information on the spike in condition a-e (from low to high)
data(pe)

# Fit MSqrob model using robust ridge regression starting from peptide intensities
# The fold changes are calculated at the protein level while correcting for
# the different peptide species in each sample and the correlation between
# peptide intensities of peptides of the same protein in the same sample.
colData(pe)$samples <- rownames(colData(pe))
pe <- msqrobAggregate(pe, i = "peptide", fcol = "Proteins", 
     formula = ~condition + (1|samples) + (1|Sequence),
     ridge = TRUE)
getCoef(rowData(pe[["msqrobAggregate"]])$msqrobModels[["P00956"]])

statOmics/msqrob2 documentation built on March 13, 2023, 6:24 a.m.