We’re so excited to announce the result of our work with the AWS Industry Solutions Team today: a “one-stop” approach that lets GATK users take advantage of more AWS features and optimize for low cost, high speed — or both! Check out our open-source solution and an accompanying writeup at AWS for Industries.

Michele Busby wrote a blog post we wish we’d written. (Then again, if we had been the ones to write it, the shout out to DA at the end might have sounded a bit shameless….) We do have one change we’d make to her biotech stack: Python is great and we agree it’s the place to start for most coding. But we find R works just fine for non-production systems.

We don’t usually give away our advice for free (!) — but recently, we sat down with 10x Genomics to talk about common pitfalls in single-cell analysis and how to avoid them. Check out their blog post to learn what we think are the five most important things to know about working with single-cell data.

Michael DeRan is one of Diamond Age’s experienced scRNAseq analysts. He’ll be participating in a panel discussion on scRNAseq data analysis at 10X Genomics’ Americas 2020 User Group Meeting. It’s virtual, of course, so you can join in and watch the conversation led by Anushka Brownly of 10X. 4:30 EST, 2020/10/09.

Lots of folks in the life sciences are finding themselves suddenly thrown into an involuntary work-from-home situation. Here at Diamond Age we’ve been working remotely from our homes for some time, so we’d like to say “Welcome!” and offer a few hard-won tips to make the transition (hopefully) easier. A few of these are common to all of us:

• Put together an ergonomic desk setup. It’s entirely too easy to damage yourself, sometimes permanently, hunching over a laptop keyboard. Get a real office chair, a monitor at the correct height, an ergonomic keyboard if that suits you, etc. This work-from-home situation isn’t temporary, and even a week with bad posture can hurt you.
• Don’t rely on email alone to communicate with your coworkers. If you find yourself trying to write an email of more than three sentences, it probably needs to move to a phone call or a video meeting. That goes double for an email sent to more than one person.
• Keep in contact with your professional network. You’re not meeting in the halls or at the coffee shop anymore, so you need to take action to keep up those contacts. Schedule them explicitly for video and bring your own coffee. You need to keep your social and professional network alive. 
• Take breaks during the day. Walk around your neighborhood if you can. It’s important to get away from your desk and reset your brain. Sitting in one room all day is suffocating.

And then we each cope with the home work environment a little differently. I asked around the team for their favorite less-common tips:

• Katie: “I don’t like to sit in one chair all day so I have four or five options to rotate through. Also when I work from home I tend to endlessly snack, but if I force myself to drink water I eat less of my kids’ Valentine’s day candy.”
• Mike: “Check what’s in view of your camera during video calls. You may need to move your cat’s litterbox.”
• Eleanor: “Don’t keep unhealthy snacks in the house – you will eat them all. Stock up on carrot sticks instead. And be sure to feed the cats before your 4pm meeting: they will harass you on video if you don’t.”
• Erica: “Sometimes I plan calls with friends for my breaks ahead of time — if I know I’ll take lunch around noon, I’ll reach out to a few people in the morning before I start working to see if they want to connect around that time.”

Hang in there, everyone. We’re all adjusting to this new normal.

Reach out if you’d like to talk to us about either the joys of home office work or bioinformatics. We’re always happy to chat.

–Eleanor

We’ll be hosting the venerable Boston Computational Biology and Bioinformatics Meetup in Cambridge, at the Asgard, December 10th. Please come join us for food and drinks and much discussion of all things bioinformatics.

This meetup is a fantastic place to meet folks who are in the field, whether they’re just starting out or long-time veterans. I’ve made tremendously valuable connections there, and I hope to keep doing so. I’ll be there on the 10th, as will several of my Diamond Age colleagues. Please come find us, and let us talk your ear off about what consulting is like.

–Eleanor

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.

The problem

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.

Experimental design

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.

Analyzing the 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.

Getting the most from the data

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!