msqrobQB: Function to fit msqrob models to peptide counts using glm

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

Function to fit msqrob models to peptide counts using glm

Description

Low-level function for parameter estimation with msqrob by modeling peptide counts using quasibinomial glm

Usage

## S4 method for signature 'SummarizedExperiment'
msqrobQB(
  object,
  formula,
  modelColumnName = "msqrobQbModels",
  overwrite = FALSE,
  priorCount = 0.1,
  binomialBound = TRUE
)

## S4 method for signature 'QFeatures'
msqrobQB(
  object,
  i,
  formula,
  modelColumnName = "msqrobQbModels",
  overwrite = FALSE,
  priorCount = 0.1,
  binomialBound = TRUE
)

Arguments

object	SummarizedExperiment or QFeatures instance
formula	Model formula. The model is built based on the covariates in the data object.
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".
overwrite	boolean(1) to indicate if the column in the rowData has to be overwritten if the modelColumnName already exists. Default is FALSE.
priorCount	A 'numeric(1)', which is a prior count to be added to the observations to shrink the estimated log-fold-changes towards zero. Default is 0.1.
binomialBound	logical, if ‘TRUE’ then the quasibinomial variance estimator will be never smaller than 1 (no underdispersion). Default is TRUE.
i	character or integer to specify the element of the QFeatures that contains the log expression intensities that will be modelled.

Value

SummarizedExperiment or QFeatures instance

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)

# Aggregate by counting how many peptide we observe for each protein
pe <- aggregateFeatures(pe, i = "peptide", fcol = "Proteins", name = "protein")

# Fit MSqrob model to peptide counts using a quasi-binomial model
# For summarized SummarizedExperiment
se <- pe[["protein"]]
se
colData(se) <- colData(pe)
se <- msqrobQB(se, formula = ~condition)
getCoef(rowData(se)$msqrobQbModels[[1]])

# For features object
pe <- msqrobQB(pe, i = "protein", formula = ~condition)

statOmics/msqrob2 documentation built on Dec. 21, 2024, 8:26 a.m.