aggregate.tbl_biogridr: Aggregate interaction data from BioGRID

In EricEdwardBryant/biogridr: BioGRID Interaction Data

Description

Aggregates BioGRID interaction data based on several categories. For more information see Details.

Usage

## S3 method for class 'tbl_biogridr'
aggregate(x, neg = NULL, pos = NULL, phy = NULL,
  und = NULL)

Arguments

x	An object of class tbl_biogridr (i.e. interaction data derived from the 'interactions' table in your local BioGRID database).
neg	Character vector of interaction system names that correspond to negative interactions (e.g. 'Negative Genetic').
pos	Character vector of interaction system names that correspond to positive interactions (e.g. 'Positive Genetic').
phy	Character vector of interaction system names that correspond to physical interactions (e.g. 'Two-hybrid').
und	Character vector of interaction system names that should be treated as undirected (see Details).

Details

Interactions in the BioGRID database are stored in an edge list format where each row corresponds to an observed interaction between two gene identifiers. Interactions are defined by the experimental system used to identify the relationship between two genes. Most of these experimental systems can be categorized into genetic interactions, which can be negative or positive, and physical interactions.

Undirected interactions:
Some experimental systems such as 'Synthetic Lethality' (SL) involve the deletion of either interacting gene, which makes them undirected since A_mutant <-SL-> B_mutant is equivalent to B_mutant <-SL-> A_mutant. For this reason, the negative interaction A -> B can be aggregated with the negative interaction B -> A. In this case the resulting table will include a row for A -> B and B -> A. Interaction systems such as 'Synthetic Dosage Lethality' are directed because A_overexpressed <-SDL-> B_mutant is not equivalent to B_overexpressed <-SDL-> A_mutant. By default, such directed interaction systems are aggregated into the category 'other'. For growth based experimental systems nodes would ideally be based on perturbation rather than gene, which would allow one to easily aggregate A_perturbation <-neg/pos-> B_purturbation with B_perturbation <-neg/pos-> A_perturbation.

Examples

## Not run: 
src_biogridr() %>%
  outer_net('CTF4') %>%
  aggregate

## End(Not run)

EricEdwardBryant/biogridr documentation built on May 6, 2019, 4:02 p.m.