gespeR-class {gespeR} | R Documentation

## gespeR

### Description

Class that represents a gespeR model. It contains a SSP `Phenotypes` and `TargetRelations` representing a siRNA knockdown experiment. When the model is fitted, it additionaly contains estimated GSP `Phenotypes`.

### Usage

    gespeR(phenotypes, target.relations, ...)

    ## S4 method for signature 'Phenotypes,TargetRelations'
    gespeR(phenotypes, target.relations,
      mode = c("cv", "stability"), alpha = 0.5, nbootstrap = 100,
      fraction = 0.67, threshold = 0.9, EV = 1, weakness = 0.8,
      ncores = 1, ...)

    ## S4 method for signature 'numeric,Matrix'
    gespeR(phenotypes, target.relations, ...)

### Arguments

`phenotypes` | The siRNA-spefic phenotypes. Single object for univariate phenotypes and list of `Phenotypes` objects for multivariate phenotypes.
`target.relations` | The siRNA-to-gene target relations
`...` | Additional arguments
`mode` | The mode of covariate selectino ("cv" or "stability")
`alpha` | The `glmnet` mixing parameter
`nbootstrap` | The number of bootstrap samples
`fraction` | The fraction for each bootstrap sample
`threshold` | The selection threshold
`EV` | The expected value of wrongly selected elements
`weakness` | The weakness parameter for randomised lasso
`ncores` | The number of cores for parallel computation

### Value

A `gespeR` object

### Slots

`SSP`

  The observed siRNA-specific phenotypes

`GSP`

  The deconvoluted gene-specific phenotypes

`target.relations`

  The siRNA-to-gene target relations, e.g. predicted by TargetScan

`is.fitted`

  An indicator wheter the gespeR model was fitted

`model`

  The fitted regularized linear regression model

### Author(s)

Fabian Schmich

### See Also

`gespeR-package`

`plot.gespeR`

`gsp`

`ssp`

`scores`

`stability`

`target.relations`

### Examples

    phenos <- Phenotypes(system.file("extdata", "Phenotypes_screen_A.txt", package = "gespeR"),
    type = "SSP",
    col.id = 1,
    col.score = 2)
    trels <- TargetRelations(readRDS(system.file("extdata", "TR_screen_A.rds", package = "gespeR")))
    res <- gespeR(phenotypes = phenos,
        target.relations = trels,
        mode = "stability",
        nbootstrap = 100,
        fraction = 0.67,
        threshold = 0.75,
        EV = 1,
        weakness = 0.8,
        ncores = 1)
    gsp(res)

[Package gespeR version 1.12.0 Index]