msqrobGlm: 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

msqrobGlm(y, npep, formula, data, priorCount = 0.1, binomialBound = TRUE)

Arguments

y	A matrix with the peptide counts. The features are along the rows and samples along the columns.
npep	A vector with number of peptides per protein. It has as length the number of rows of y. The counts are equal or larger than the largest peptide count in y.
formula	Model formula. The model is built based on the covariates in the data object.
data	A DataFrame with information on the design. It has the same number of rows as the number of columns (samples) of y.
priorCount	A 'numeric(1)', which is a prior count to be added to the observations to shrink the estimated log-fold-changes towards zero.
binomialBound	logical, if ‘TRUE’ then the quasibinomial variance estimator will be never smaller than 1 (no underdispersion).

Value

A list of objects of the StatModel class.

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 peptide intensities in protein expression values
pe <- aggregateFeatures(pe, i = "peptide", fcol = "Proteins", name = "protein")
pe

# Fit MSqrob model using robust regression with the MASS rlm function
models <- msqrobGlm(
    aggcounts(pe[["protein"]]),
    rowData(pe[["protein"]])[[".n"]],
    ~condition,
    colData(pe)
)
getCoef(models[[1]])

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