Considering the broad similarities between networks and phylogenies it is amazing that they have, up until recently, been very separate approaches. In the world of epidemiology transmission trees have been gaining momentum over the last 5 years (see the excellent review by Hall et al: https://www.ncbi.nlm.nih.gov/pubmed/27217184) as they turn phylogenies into something that more-or-less equates to transmission. Now it appears that ecologists are doing the same thing with this really interesting paper just out in Methods in Ecology and Evolution (see link below). The package attached to Schliep et al looks really cool and I can imagine will be of use to a broad array of disciplines. I’m looking forward to trying it out my self…..
Here is the link: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12760/full
Pathogen subtype really does matter – different subtypes of FIV (feline HIV) get around the Serengeti lions is remarkably different ways. This was the general conclusion from our paper just out in the Journal of Animal Ecology (see link below). After many years work I’m thrilled that this paper is out. This paper hopefully highlights some of the ways in which cool community phylogenetic methods (coupled with phylodynamic approaches) can help understand disease transmission in a wild population.
Here is a link: http://onlinelibrary.wiley.com/doi/10.1111/1365-2656.12751/full
Studies applying metacommunity concepts to understanding parasite and symbiotic communities are still pretty rare. That’s what make a new paper by Mihalijevic et al in Journal of Animal Ecology that much more exciting. Aside from the impressive data sets assembled, I particularly like how they use multi-species occupancy models with detection error estimates built in. I agree with the authors that this is particularly useful for parasites. I also like how they estimated how well their models predicted out of sample data – I’ve never seen this in occupancy models before. I did think they interchanged ‘symbiont’ and ‘parasite’ in a confusing way to me at least – but that’s just a minor quibble. Overall it was interesting that host richness and identity were important in explaining parasite composition – this is logical but rarely (if at all?) demonstrated. I think these approaches really are of value for disease ecology and hopefully are used more broadly in the future.
Here is the link: http://onlinelibrary.wiley.com/doi/10.1111/1365-2656.12735/full
Don’t you just hate when you run J model test (or similar software) to find the most parsimonious substitution model for a given set of sequences and the best model is something obscure and often not directly implementable in phylogenetics platforms like BEAST?
I stumbled across this excellent post by Justin Bagley that provides really useful information on how to put all sorts of substitution models in BEAST: http://www.justinbagley.org/1058/setting-dna-substitution-models-beast
This is definitely a valuable resource and makes incorporating J model test results much easier.
A couple of weeks ago I had the pleasure to attend the disease genomics boot camp (more formally the genomics of wildlife disease workshop) at Colorado State with a great bunch of people including members of the Craft lab (see below). It was the first time the workshops been held but overall it was a success and I can highly recommend it to others interested in the topic.
It really was a broad (and nearly overwhelming) overview of the entire next-gen process from getting sequences from Ilumina runs to a variety of downstream analytical approaches. There was also a a lot of material incorporating the host genome too which I thought was particularly useful. As our NSF project was responsible for the workshop I was an ‘auditor’ and assisted with the BEAST afternoon. Not only was the course material (and the lecturers) good but the guest speakers were excellent and help to frame things really well. It was also a great opportunity to network with like-minded researchers and was a nice to chat with the other NSF postdocs/phD students all things puma (and bobcat) disease.
Now to get ready for EEID (Ecology and Evolution of Infectious Disease) 2017 in Santa Barbara…..
Understanding how the vast collection of organisms within us (‘the ‘microbiome’) is linked to human (and ecosystem) health is one of the most exciting scientific topics today. It really does have the possibility of improving our lives considerably though is often over-hyped (see the link below). However, I’ve recently I’ve been reading quite a few microbiome papers (it was our journal clubs topic of the month) and have been struck by the poor study design and lack of understanding of the statistical methodology. Talking to colleagues in the microbiome field – these problems maybe more widespread and could be hindering our progress in understanding this important component of the ecosystem within us.
Of course microbiome research is simply microbe community ecology, but the way some microbiome practitioners use and report community ecology statistics is problematic and sometimes outright deceptive.This includes people publishing in the highest scientific journals. I won’t pick on any particular paper, but here are a few general observations (sorry for the technical detail).
- Effect sizes are often not reported or visualized using ordination techniques. They have a significant P value but how do you know how biologically relevant this is? My guess is that they are small as in often the case with free living communities.
- Little detail is given about how the particular test is performed. Usual example: “We did a PERMANOVA to test for XX”. Despite the fact that the PERMANOVA has some general issues (see the Warton et al paper below), no information is given about the test anyway e.g., was it a two way crossed design, did they use Type III sums of squares etc? Did they test for multivariate disperson using PERMDISP or similar? Literally that is one of the only assumptions of the test but I haven’t read any microbiome paper that has checked. If they haven’t we can’t trust the results. Have they read the original paper by Marti Anderson? Some cite it at least….
- I haven’t found any PCA or PCoA plot with % of variance explained. This is annoying – the axes shown may only explain a small amount of variance in the community, so thus the pretty little clusters of points shown maybe pretty artificial.
I’ll stop ranting. These issues really impair interpretation of the results and make the science difficult to replicate. It makes you ask “how do these papers get through the gates?’ I’m guessing that a significant proportion of authors, reviewers and editors have little experience in community biostats and don’t really understand what the tests are doing. They are relying on analytical pipelines such as QUIIME that claim to ‘ publication quality graphics and statistics’ and not thinking much more about it. More microbiome researchers need to go beyond these pipelines and keep up-to-date with community methods more broadly. The quality of the research will clearly improve.
Microbiome over-hype: http://www.nature.com/news/microbiology-microbiome-science-needs-a-healthy-dose-of-scepticism-1.15730).
Warton et al: http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2011.00127.x/full
Marti Anderson’s paper: http://onlinelibrary.wiley.com/doi/10.1111/j.1442-9993.2001.01070.pp.x/full
Having a thick skin and the ability to shrug off harsh and sometimes personal criticism is an often unrecognized trait of a scientist. You put your work out there to the world and get feedback from often anonymous peers(but this is changing slowly) . The system works usually pretty well and 99% of the time makes the paper better. When the comments are highly critical, you go through a mini five stages of grief but you always come around and the paper gets better. I’ve definitely had my fair share of critical feedback, but one of my recent favorites was a reviewer suggesting that my literature review “hadn’t gone beyond the literature”….(?) However, none have come close to the comments that this author received:
there are so many good lines but this one is the best: “This paper has merit and no errors, but I do not like it …”
Pleasing that it still got published in the journal anyway!
The May edition from the Journal of Animal Ecology is pretty much essential reading for anyone interested in disease ecology (particularly those using network approaches). Springer et al’s paper about dynamic networks and Cryptosporidium spread is particularly interesting – I really like the fact that they incorporated different transmission modes into their dynamic network model – this reflects the reality in lots of host-parasite systems. I also like that they used both empirically derived networks and simulated models. The comparison between static and dynamic models wasn’t particularly exciting – it seemed obvious that dynamic models were always going to lead to bigger outbreaks. Nonetheless really interesting work.
The study by Patterson et al on tuberculosis and meerkats was also really cool – combining both social and environmental predictors to understand tb risk in the Kalahari was interesting and is something I’m trying to with the Serengeti lions. They should have used machine learning though!
Furthermore the community ecology section is full of interesting papers as well – hopefully I’ll get around to reading them soon.
I just found this excellent series of articles by John Mount. Really intuitive and the explanations he gives are good. Furthermore there is really useful R code to recreate the figures they make. Really a must read if you are getting into data science.
Our article is out in Functional Ecology as a pre-print: http://www.functionalecology.org/view/0/summaries.html#fountainjones1069
Really nice that this research finally see the light of day after years of going through the hoops!