msqrobGlm {msqrob2} | R Documentation

## 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]])

[Package msqrob2 version 1.0.0 Index]