msqrobGlm {msqrob2} | R Documentation |
Low-level function for parameter estimation with msqrob by modeling peptide counts using quasibinomial glm
msqrobGlm(y, npep, formula, data, priorCount = 0.1, binomialBound = TRUE)
y |
A |
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 |
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). |
A list of objects of the StatModel
class.
Lieven Clement
# 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]])