msqrobHurdle,SummarizedExperiment-method {msqrob2} | R Documentation

## Function to fit msqrob hurdle models

### Description

Fitting a hurdle msqrob model with an intensity component for assessing differential abundance and an count component that is modeling peptide counts using quasibinomial glm for differential detection of the number of features that were not missing and used for aggregation

### Usage

    ## S4 method for signature 'SummarizedExperiment'
    msqrobHurdle(
      object,
      formula,
      modelColumnName = "msqrobHurdle",
      overwrite = FALSE,
      robust = TRUE,
      ridge = FALSE,
      maxitRob = 1,
      tol = 1e-06,
      doQR = TRUE,
      lmerArgs = list(control = lmerControl(calc.derivs = FALSE)),
      priorCount = 0.1,
      binomialBound = TRUE
    )

    ## S4 method for signature 'QFeatures'
    msqrobHurdle(
      object,
      i,
      formula,
      modelColumnName = "msqrobHurdle",
      overwrite = FALSE,
      robust = TRUE,
      ridge = FALSE,
      maxitRob = 1,
      tol = 1e-06,
      doQR = TRUE,
      lmerArgs = list(control = lmerControl(calc.derivs = FALSE)),
      priorCount = 0.1,
      binomialBound = TRUE
    )

### Arguments

`object` | `SummarizedExperiment` or `QFeatures` instance with an assay that is generated with the `aggreateFeatures` function from the `QFeatures` package
`formula` | Model formula. Both model components are 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 "msqrobHurdle".
`overwrite` | `boolean(1)` to indicate if the column in the rowData has to be overwritten if the modelColumnName already exists. Default is FALSE.
`robust` | `boolean(1)` to indicate if robust regression is performed to account for outliers when fitting the intensity component of the hurdle model. Default is `TRUE`. If `FALSE` an OLS fit is performed.
`ridge` | `boolean(1)` to indicate if ridge regression is performed. Default is `FALSE`. If `TRUE` the fixed effects of the intensity component of the hurdle model are estimated via penalized regression and shrunken to zero.
`maxitRob` | `numeric(1)` indicating the maximum iterations in the IRWLS algorithm used in the M-estimation step of the robust regression for fitting the intensity component of the hurdle model.
`tol` | `numeric(1)` indicating the tolerance for declaring convergence of the M-estimation loop of the intensity component of the hurdle model.
`doQR` | `boolean(1)` to indicate if QR decomposition is used when adopting ridge regression for the intensity component of the model. Default is `TRUE`. If `FALSE` the predictors of the fixed effects are not transformed, and the degree of shrinkage can depend on the encoding.
`lmerArgs` | a list (of correct class, resulting from ‘lmerControl()’ containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the ‘lmerControl’ documentation of the lme4 package for more details. Default is `list(control = lmerControl(calc.derivs = FALSE))`
`priorCount` | A 'numeric(1)', which is a prior count to be added to the observations to shrink the estimated odds ratios of the count component 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 peptide intensities to protein expression values
    pe <- aggregateFeatures(pe, i = "peptide", fcol = "Proteins", name = "protein")

    # Fit Hurdle MSqrob model
    # For summarized SummarizedExperiment
    se <- pe[["protein"]]
    se
    colData(se) <- colData(pe)
    se <- msqrobHurdle(se, formula = ~condition)
    getCoef(rowData(se)$msqrobHurdleIntensity[[1]])
    getCoef(rowData(se)$msqrobHurdleCount[[1]])

    # For features object
    pe <- msqrobHurdle(pe, i = "protein", formula = ~condition)
    getCoef(rowData(pe[["protein"]])$msqrobHurdleIntensity[[1]])
    getCoef(rowData(pe[["protein"]])$msqrobHurdleCount[[1]])

[Package msqrob2 version 1.2.0 Index]