msqrobQB,SummarizedExperiment-method {msqrob2} | R Documentation |
Low-level function for parameter estimation with msqrob by modeling peptide counts using quasibinomial glm
## 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 )
object |
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formula |
Model formula. The model is built based on the covariates in the data object. |
modelColumnName |
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overwrite |
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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 |
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SummarizedExperiment or QFeatures instance
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 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)