hm-methods: Jointly analyse multiple genetic perturbation screens using a...

Description Usage Arguments Value Examples

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

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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

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cbg-ethz/knockout documentation built on Feb. 13, 2020, 7:27 p.m.