In our last group meeting, I talked about a recent paper which presents a vast amount of genetic interaction data as well as some spatial analysis of the created data. Constanzo et al. used temperature-sensitive mutant alleles to measure the interaction of ~6000 genes in the yeast Saccharomyces cerevisiae [1]. A typical way to analyse such data would be the use of community detection to find groups of genes with similar interaction pattern, see for example [2] for a review. Instead, the authors of this paper created a two-dimensional embedding of the network with a spring-layout, which places nodes close to each other if they show similar interaction pattern.

The network layout is then compared with Gene Ontology by applying a spatial analysis of functional enrichment (SAFE) [3].  Clusters enriched are associated for example with cell polarity, protein degradation, and ribosomal RNA. By filtering the network for different similarities they find a hierarchical organisation of genetic function with small dense modules of pathways or complexes at the bottom and sparse clusters representing different cell compartments at the top.

In this extensive paper, they then go further into detail to quantify gene pleiotropy, predict gene function, and how the interaction structure differs between essential and non-essential genes. They also provide that data online under http://thecellmap.org/costanzo2016/ .

(Left) A network of genetic interaction was embedded into a two-dimensional space using a spring-layout (Right) This embedding was compared with Gene Ontology terms to find regions of spatial enrichment.
Image from [1]

[1] Costanzo, Michael, et al. “A global genetic interaction network maps a wiring diagram of cellular function.” Science 353.6306 (2016): aaf1420.

[2] Fortunato, Santo. “Community detection in graphs.” Physics Reports 486.3 (2010): 75-174.

[3] Baryshnikova, Anastasia. “Systematic Functional Annotation and Visualization of Biological Networks.” Cell Systems (2016).