"Null Models for Community Detection in Spatially Embedded, Temporal Networks"

Another one of my papers finally got its volume, issue, and page numbers last week. (It came out in advanced access in November 2015.) I finally got my own copy of the document today, so here are some details.

Title: Null Models for Community Detection in Spatially Embedded, Temporal Networks

Authors: Marta Sarzynska, Elizabeth A. Leicht, Gerardo Chowell, and Mason A. Porter

Abstract: In the study of networks, it is often insightful to use algorithms to determine mesoscale features such as 'community structure', in which densely connected sets of nodes constitute 'communities' that have sparse connections to other communities. The most popular way of detecting communities algorithmically is to maximize the quality function known as modularity. When maximizing modularity, one compares the actual connections in a (static or time-dependent) network to the connections obtained from a random-graph ensemble that acts as a null model. The communities are then the sets of nodes that are connected to each other densely relative to what is expected from the null model. Clearly, the process of community detection depends fundamentally on the choice of the null model, so it is important to develop and analyse novel null models that take into account appropriate features of the system under study. In this paper, we investigate the effects of using null models that incorporate spatial information, and we propose a novel null model based on the radiation model of population spread. We also develop novel synthetic spatial benchmark networks in which the connections between entities are based on the distance or flux between nodes, and we compare the performance of static and time-dependent versions of the radiation null model to the standard ('Newman–Girvan') null model for modularity optimization and to a recently proposed gravity null model. In our comparisons, we use both the above synthetic benchmarks and time-dependent correlation networks that we construct using countrywide dengue fever incidence data for Peru. Our findings illustrate the need to use appropriate generative models for the development of spatial null models for community detection.

What Happens in Pittsburgh Stays in Pittsburgh (2015 Edition)

I am slightly late with this post, given that I just returned to Oxford this morning on a red-eye flight. But I was just at Carnegie Mellon University for a workshop on groups and interactions in data, networks, and biology. This workshop was on the mathematical side of things.

"Fight-Club Nodes"

First rule of fight-club nodes: You do not talk about fight-club nodes.

Nobel Prize in Physiology/Medicine Awarded for Work on the Brain's Navigation System

This year's Nobel Prize in Physiology/Medicine went to a trio for their work on the brain's navigation system. It's also great that neuroscience (and systems neuroscience, no less) was recognized with a Nobel.

A particularly relevant Scholarpedia entry is the one on grid cells (which, along with "place cells", help the brain to determine where it is and where it is going), whose authors --- a team of wife and husband --- comprise two of the three newly-minted Nobel Laureates.

Now about my horrible sense of navigation...

Update (10/10/14): I neglected to post the official announcement.

"Longdaysin"

I just learned (in a talk by Frank Doyle) about an appropriately-named compound called "longdaysin" because of its ability to slow down biological clocks. Even cooler than "ubiquitin". Those wacky biologists...

Congratulations to Dr. Sumeet Agarwal!

My Ph.D. student, Sumeet Agarwal, recently pass his viva. He had some proverbial "minor corrections" to make, but he mentions that it should only take a couple of days, so he'll officially be "Dr. Sumeet Agarwal" very soon. Congratulations! (Apparently, his Ph.D. defense lasted a whopping 4.5 hours, which is quite the marathon!)

The title of Sumeet's doctoral thesis is "Networks in Nature: Evolution, Dynamics and Modularity in Biological Networks". It includes work on protein-protein interaction networks but also more general things on machine learning in networks.

"Pollock Plot"

Our "Pollock plot" is featured on the back cover of the spring 2011 issue of Biomedical Computation Review.

"The Function of Communities in Protein Interaction Networks at Multiple Scales"

Here is a just-published paper on protein interaction networks by my collaborators and me.

Title: The Function of Communities in Protein Interaction Networks at Multiple Scales

Authors: Anna C. F. Lewis, Nick S. Jones, Mason A. Porter, and Charlotte M. Deane

(There seems to be a bit of a name transposition of the last author in the official published paper... I'm not sure whether to laugh or cry.)

Abstract (in 3 parts, as per journal requirements)

Background

If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network.

Results

Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein.

Conclusions

We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

"Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks"

My first paper on biological networks has just been officially published.

Title: Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

Authors: Sumeet Agarwal, Charlotte M. Deane, Mason A. Porter, Nick S. Jones

Abstract: The idea of ‘‘date’’ and ‘‘party’’ hubs has been influential in the study of protein–protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs
to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we
show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.


As you can see from the discussion in this paper, the idea of "date" versus "party" hubs has been controversial ever since it was introduced in 2004. We started this project as agnostics regarding whether or not such a sharp distinction exists. We used a different perspective from what was previously in the literature, and we ended up concluding after lots of work that this kind of sharp distinction does not really exist (despite claims to the contrary).

Nearest-Neighbor Coupling (aka: Best. Bathroom door sign. Ever.)

I think you're just going to have to look at the picture for this one. I approve!