msqrobLm: Function to fit msqrob models using lm and rlm

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

Function to fit msqrob models using lm and rlm

Description

Low-level function for parameter estimation with msqrob using the ordinary least squares or robust regression base on the MASS::rlm function.

Usage

msqrobLm(y, formula, data, robust = TRUE, maxitRob = 5)

Arguments

y	A matrix with the quantified feature intensities. The features are along the rows and samples along the columns.
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.
robust	boolean(1) to indicate if robust regression is performed to account for outliers. Default is TRUE. If FALSE an OLS fit is performed.
maxitRob	numeric(1) indicating the maximum iterations in the IRWLS algorithm used in the M-estimation step of the robust regression.

Value

A list of objects of the StatModel class.

Author(s)

Lieven Clement, Oliver M. Crook

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 <- msqrobLm(assay(pe[["protein"]]), ~condition, colData(pe))
#' getCoef(models[[1]])

statOmics/msqrob2 documentation built on April 23, 2024, 8:52 a.m.