hm {perturbatr} | R Documentation

## Jointly analyse multiple genetic perturbation screens using a hierarchical model

### Description

Analyse multiple different genetic perturbation screens at once using a hierarchical model. The model estimates general relative effect sizes for genes across all experiments. This could for instance be a pan-pathogenic host factor, i.e. a gene that decisively impacts the life-cycle of multiple pathogens.

### Usage

    hm(obj, formula = Readout ~ Condition + (1 | GeneSymbol) + (1 |
      Condition:GeneSymbol), drop = TRUE, weights = 1, bootstrap.cnt = 0)

    ## S4 method for signature 'PerturbationData'
    hm(obj, formula = Readout ~ Condition + (1 |
      GeneSymbol) + (1 | Condition:GeneSymbol), drop = TRUE, weights = 1,
      bootstrap.cnt = 0)

### Arguments

`obj` | an `PerturbationData` object
`formula` | a `formula` object that is used to model the readout of your data set. If no formula is provided, the formula 'Readout ~ Condition + (1|GeneSymbol) + (1|Condition:GeneSymbol)' is used. For other data sets with more variables, it might makes sense to use other fixed and random effects
`drop` | boolean if genes that are not found in every Condition should be dropped
`weights` | a numeric vector used as weights for the single perturbations
`bootstrap.cnt` | the number of bootstrap runs you want to do in order to estimate a significance level for the gene effects

### Value

returns a `HMAnalysedPerturbationData` object

### Examples

     data(rnaiscreen)
     res <- hm(rnaiscreen)

[Package perturbatr version 1.7.0 Index]