Diamond Age is hiring! We’re looking for a skilled computational biologist/bioinformatician to join us here as a consultant. We have flexible hours, a supportive team, and a pile of exciting projects to work on. We’re open to remote work, as well.
Check out our new Careers page for details.
One of the common analysis tasks we have at Diamond Age is to analyze single cell RNA-seq data. Our customers are largely therapeutics-development biotechs who use this new technology to assess the impact of their development candidates on gene expression in selected cell types. scRNA-seq is a very different beast than its apparent predecessor, bulk RNA-seq. There are gotchas in both the experimental design and analysis of this data that simply didn’t apply to the older technology. One of them relates to appropriate experimental design for differential expression studies.
Folks often think that when designing a scRNA-seq experiment, they need only collect data from one sample per treatment group to reliably find differences in gene expression. They are then surprised when we tell them that they need multiple biological replicates, even though each replicate provides them with measurements from 1000+ cells of their cell type of interest. A recent Twitter thread started by John Hogenesch (@jbhclock) makes it clear that this misconception is widespread.
Vito Zanotelli (@ZanotelliVRT) summed up the problem rather succinctly:
He’s right; the gene expression profile of the individual cells in a sample aren’t independent measurements. They are more accurately described as repeated measurements on the sample.
Consider a single patient; we expect that any B cells collected from one patient would have a more-similar expression profile to each other than to B cells collected from another patient. If we dose one patient with drug and the other with vehicle, how do we know that differences in expression between those two patients’ B cells aren’t driven by biological difference between the patients? Short answer: we don’t. We *must* collect data from more than one patient, so we must have more than one patient (or animal, or dish of cells) in each treatment group, no matter how many cells we collect from each.
To drive home the point: imagine if we measured blood glucose from a mouse 1000 times. Exsanguination aside, all 1000 of those replicated measurements give us a very good idea of what is happening in that one animal, but doesn’t tell us much about the rest of all mouse-kind. In single-cell RNA-seq, each gene expression profile collected from 1000 different B cells from that mouse are analogous to those glucose measurements.
If we want to figure out how a drug affects B-cells generally across all mice, we must treat multiple mice with the drug, and compare the gene expression of, say, 1000 B-cells from each animal in one treatment group against the profiles of the other group. We treat those 1000 cells as repeated measurements of one animal, or one biological replicate. That means that our N is still counted in animals: three animals means we have three replicates, not 3000.
The upshot of this is that a properly-powered single-cell RNA-seq experiment can get quite expensive. As of this writing, the total cost of a scRNA-seq experiment is in the thousands of dollars *per sample*. If we need a minimum of three samples per group (and we do), that’s a hefty price tag. But it’s worth it to get real data.
Once we have a well-designed experiment with biological replicates, how do we handle the analysis? Most of the differential expression methods for single-cell analysis are only suitable for within-sample analysis: they treat each cell as an independent measurement and can only reliably tell you about how one group of cells from the same sample compares to another group in that sample. Differential expression tests using these methods result in improbably low p-values.
One tool that does handle differential expression across multiple samples properly is an R package called MAST. It does this by essentially grouping expression profiles from each sample together, and comparing those groups rather than comparing individual cells. It uses what’s called a mixed-effects model to accomplish this. It’s quite computational intensive to use, but the results are solid. I’d love to hear from folks who have found other tools that do good work on these experimental designs.
One of the hardest things to do in this business is to tell clients that the experiment they ran – the one that cost so much – isn’t going to give them the answers they need. Single-cell RNA-seq experiments are repeat offenders in this space because they are expensive and very new; despite the somewhat familiar name, they are very different beasts than good old bulk-RNAseq.
We hate seeing good mice (or even cell lines) go to waste. Reach out if you’d like to chat about experimental design and making sure your investment pays off.
–Eleanor
Here we are in that funny week at the end of the year, when the projects have mostly wrapped up and we take a breather and look at both the year behind and the year ahead. In short, 2018 has been an amazing, exciting year with a lot of work and a lot of success. I’d like to share a little bit of that with all of you.
Over the course of the past twelve months, Diamond Age has served a wide variety of clients from across the therapeutics discovery space, in metabolic disease, hearing loss, neuroscience, cancer and many more. We’ve continued our work in using genomics to support discovery biology and drug development and expanded into building data processing pipelines, developing analysis methods and doing technology transfer. Beyond therapeutics, we have moved into biotechnology and diagnostics, assisting our clients with data analysis for process improvement and streamlining.
All of this work meant that we needed to expand the team. In July, Chris Friedline joined us, bringing with him extensive experience in sequencing, evolutionary biology, information technology and software engineering / infrastructure. In August, Somdutta Saha brought to the team chemistry, cheminformatics, microbiome analysis and drug discovery/development experience.
In December, we presented at the North Shore Technology Council’s First Friday seminar series. We talked about how to hire computational biology folks, how bioinformatics relates to data science, and using machine learning and AI in discovery biology. We co-presented with Huseyin Mehmet from Zafgen, Inc; he described how Diamond Age helps Zafgen get its data analysis needs met efficiently, and what makes a good collaboration with a bioinformatics team. Check out the slides if you’d like to learn more.
As for the year ahead, 2019 looks to me like another year of exciting work with great companies, and perhaps some even more interesting developments, besides. I want to say a personal thank you to everyone at Diamond Age, plus all of our clients and our supporters. There is certainly more to come.
–Eleanor
If you missed our presentation at the North Shore Technology Council’s First Friday event, you can check out the slides on SlideShare.
We co-presented with Huseyin Mehmet of Zafgen, Inc. Huseyin talked about what we do for them, from cross-dataset analysis to technology recommendations to app development. I talked about what you should look for in a bioinformatics team and how to decide whether a deep learning/AI approach is right for your computational questions.
Enjoy!
Want to learn more about computational biology in a therapeutics context? I’ll be co-presenting with Huseyin Mehmet of Zafgen next Friday, December 7th at the Woburn Trade Center. We’ll be talking about making effective use of computational biology data for drug discovery and development, and also describing how the collaboration between Diamond Age and Zafgen works.
This is event is the North Shore Technology Council’s usual First Friday event. Register here to attend and come say hello. Several Diamond Age folks will be there.
Hope to see you there!
–Eleanor
It’s Thanksgiving Day, and I’d like to take a minute to say “Thank you” to the people who have made Diamond Age possible, starting with our customers. We’ll have a proper ‘Customers’ page up here soon enough, but in the meantime, thank you to Decibel Therapeutics, Voyager Therapeutics, 1CellBio, Aquinnah Pharmaceuticals, and Zafgen, Inc. As for the rest of you who will not be named, you know who you are, and we appreciate the trust you’ve placed in us.
Also, thank you Chris and Somdutta, who took the risk of throwing in with me by joining Diamond Age full-time this year. And thank you to Dave, Max, Nick, Mike, Zarko, Bruce and Chris for making the time to work with us. Diamond Age would not exist without you.
Happy Thanksgiving!
Source control is not just for software engineers. Using the tools that coders have written to support their work can make a computational biologist’s life massively easier. You’ll find that having a versioned, trackable backup of your analytic scripts is a lifesaver, over and over again.
We’re probably preaching to the choir here, but I wanted to make sure everyone had at least heard the gospel.
-Eleanor
