msqrobQB,SummarizedExperiment-method {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

    ## 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
    )

### Arguments

`object` | `SummarizedExperiment` or `QFeatures` instance
`formula` | Model formula. The model is built based on the covariates in the data object.
`modelColumnName` | `character` to indicate the variable name that is used to store the msqrob models in the rowData of the SummarizedExperiment instance or of the assay of the QFeatures instance. Default is "msqrobModels".
`overwrite` | `boolean(1)` to indicate if the column in the rowData has to be overwritten if the modelColumnName already exists. Default is FALSE.
`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` | `character` or `integer` to specify the element of the `QFeatures` that contains the log expression intensities that will be modelled.

### Value

SummarizedExperiment or QFeatures instance

### 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 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)

[Package msqrob2 version 1.0.0 Index]