Gretchen Huizinga: Welcome to Abstracts, a Microsoft Research Podcast that puts the
spotlight on world-class research in brief. I’m Gretchen Huizinga. In this series,
members of the research community at Microsoft give us a quick
snapshot – or a podcast abstract – of their new and noteworthy papers.
On today's episode, I'm talking to Alex Lu, a senior researcher at Microsoft Research
and co-author of a paper called Assessing the Limits of Zero Shot Foundation Models
in Single-cell Biology. Alex Lu, wonderful to have you on the podcast. Welcome to Abstracts!
Alex Lu: Yeah, I'm really excited to be joining you today.
Huizinga: So let's start with a little background of your work. In just a few
sentences, tell us about your study and more importantly, why it matters.
Lu: Absolutely. And before I dive in, I want to give a shout out to the MSR research intern
who actually did this work. This was led by Kasia Kedzierska, who interned with us
two summers ago in 2023, and she's the lead author on the study. But basically, in this research,
we study single-cell foundation models, which have really recently rocked the world of biology,
because they basically claim to be able to use AI to unlock understanding about single-cell biology.
Biologists for a myriad of applications, everything from understanding how single
cells differentiate into different kinds of cells, to discovering new drugs for cancer,
will conduct experiments where they measure how much of every gene is expressed inside of just
one single cell. So these experiments give us a powerful view into the cell's internal
state. But measurements from these experiments are incredibly complex. There are about 20,000
different human genes. So you get this really long chain of numbers that measure how much there is
of 20,000 different genes. So deriving meaning from this really long chain of numbers is really
difficult. And single-cell foundation models claim to be capable of unraveling deeper insights than
ever before. So that's the claim that these works have made. And in our recent paper,
we showed that these models may actually not live up to these claims. Basically,
we showed that single-cell foundation models perform worse in settings that are fundamental
to biological discovery than much simpler machine learning and statistical methods
that were used in the field before single-cell foundation models emerged and are the go-to
standard for unpacking meaning from these complicated experiments. So in a nutshell,
we should care about these results because it has implications on the toolkits that biologists
use to understand their experiments. Our work suggests that single-cell foundation models may
not be appropriate for practical use just yet, at least in the discovery applications that we cover.
Huizinga: Well, let's go a little deeper there. Generative pre-trained transformer models,
GPTs, are relatively new on the research scene in terms of how they're being used
in novel applications, which is what you're interested in,
like single-cell biology. So I'm curious, just sort of as a foundation, what other research
has already been done in this area, and how does this study illuminate or build on it?
Lu: Absolutely. Okay, so we were the first to notice and document this issue in single-cell
foundation models, specifically. And this is because that we have proposed evaluation methods
that, while are common in other areas of AI, have yet to be commonly used to evaluate single-cell
foundation models. We performed something called zero-shot evaluation on these models. Prior to our
work, most works evaluated single-cell foundation models with fine tuning. And the way to understand
this is because single-cell foundation models are trained in a way that tries to expose these models
to millions of single-cells. But because you’re exposing them to a large amount of data, you can't
really rely upon this data being annotated or like labeled in any particular fashion
then. So in order for them to actually do the specialized tasks that are useful for biologists,
you typically have to add on a second training phase. We call this the fine-tuning phase,
where you have a smaller number of single cells, but now they are actually labeled
with the specialized tasks that you want the model to perform. So most people, they typically
evaluate the performance of single-cell models after they fine-tune these models.
However, what we noticed is that this evaluating these fine-tuned models has several problems.
First, it might not actually align with how these models are actually going to be used by biologists
then. A critical distinction in biology is that we're not just trying to interact with
an agent that has access to knowledge through its pre-training, we're trying to extend these
models to discover new biology beyond the sphere of influence then. And so in many cases, the point
of using these models, the point of analysis, is to explore the data with the goal of potentially
discovering something new about the single cell that the biologists worked with that they weren't
aware of before. So in these kinds of cases, it is really tough to fine-tune a model. There's
a bit of a chicken and egg problem going on. If you don't know, for example, there's a new kind
of cell in the data, you can't really instruct the model to help us identify these kinds of new
cells. So in other words, fine-tuning these models for those tasks essentially becomes impossible
then. So the second issue is that evaluations on fine-tuned models can sometimes mislead us
in our ability to understand how these models are working. So for example, the claim behind
single-cell foundation model papers is that these models learn a foundation of biological knowledge
by being exposed to millions of single cells in its first training phase, right? But it's possible
when you fine-tune a model, it may just be that any performance increases that you see using the
model is simply because that you're using a massive model that is really sophisticated,
really large. And even if there's any exposure to any cells at all then, that model is going
to do perfectly fine then. So going back to our paper, what's really different about this
paper is that we propose zero-shot evaluation for these models. What that means is that we do not
fine-tune the model at all, and instead we keep the model frozen during the analysis step. So how
we specialize it to be a downstream task instead is that we extract the model's internal embedding
of single-cell data, which is essentially a numerical vector that contains information that
the model is extracting and organizing from input data. So it's essentially how the model perceives
single-cell data and how it's organizing in its own internal state. So basically,
this is the better way for us to test the claim that single-cell foundation models are learning
foundational biological insights. Because if they actually are learning these insights,
they should be present in the models embedding space even before we fine-tune the model.
Huizinga: Well, let's talk about methodology on this particular study.
You focused on assessing existing models in zero-shot learning for
single-cell biology. How did you go about evaluating these models?
Lu: Yes, so let's dive deeper into how zero-shot evaluations are conducted, okay? So the premise
here is that we're relying upon the fact that if these models are fully learning foundational
biological insights, if we take the model's internal representation of cells, then cells
that are biologically similar should be close in that internal representation, where cells that are
biologically distinct should be further apart. And that is exactly what we tested in our study. We
compared two popular single-cell foundation models and importantly, we compared these models against
older and reliable tools that biologists have used for exploratory analyses. So these include
simpler machine learning methods like scVI, statistical algorithms like Harmony, and
even basic data pre-processing steps, just like filtering your data down to a more robust subset
of genes, then. So basically, we tested embeddings from our two single-cell foundation models against
this baseline in a variety of settings. And we tested the hypothesis that biologically
similar cells should be similar across these distinct methods across these datasets.
Huizinga: Well, and as you as you did the testing, you obviously were aiming
towards research findings, which is my favorite part of a research paper,
so tell us what you did find and what you feel the most important takeaways of this paper are.
Lu: Absolutely. So in a nutshell, we found that these two newly proposed single-cell foundation
models substantially underperformed compared to older methods then. So to contextualize why
that is such a surprising result, there is a lot of hype around these methods.
So basically, I think that,yeah, it's a very surprising result,
given how hyped these models are and how people were already adopting them. But our
results basically caution that these shouldn't really be adopted for these use purposes.
Huizinga: Yeah, so this is serious real-world impact here in terms of if models are being
adopted and adapted in these applications, how reliable are they, et cetera? So given that,
who would you say benefits most from what you've discovered in this paper and why?
Lu: Okay, so two ways, right? So I think this has at least immediate implications on the way
that we do discovery in biology. And as I've discussed, these experiments are used for
cases that have practical impact, drug discovery applications, investigations into basic biology,
then. But let's also talk about the impact for methodologists, people who are trying to improve
these single-cell foundation models, right? I think at the base, they're really excited
proposals. Because if you look at what some of the prior and less sophisticated methods couldn’t do,
they tended to be more bespoke. So the excitement of single-cell foundation models is that you have
this general-purpose model that can be used for everything and while they're not
living up to that purpose just now, just currently, I think that it's important that
we continue to bank onto that vision, right? So if you look at our contributions in that area,
where single-cell foundation models are a really new proposal, so it makes sense that
we may not know how to fully evaluate them just yet then. So you can view our work as basically
being a step towards more rigorous evaluation of these models. Now that we did this experiment,
I think the methodologists know to use this as a signal on how to improve the models and if they're
going in the right direction. And in fact, you are seeing more and more papers adopt zero-shot
evaluations since we put out our paper then. And so this essentially helps future computer
scientists that are working on single-cell foundation models know how to train better models.
Huizinga: That said, Alex, finally, what are the outstanding challenges that you
identified for zero-shot learning research in biology, and what foundation might this paper
lay for future research agendas in the field?
Lu: Yeah, absolutely. So now that we've shown single-cell foundation models don't necessarily
perform well, I think the natural question on everyone's mind is how do we actually train
single-cell foundation models that live up to that vision, that can perform in helping us discover
new biology then? So I think in the short term, yeah, we're actively investigating
many hypotheses in this area. So for example, my colleagues, Lorin Crawford and Ava Amini,
who were co-authors in the paper, recently put out a pre-print understanding how training data
composition impacts model performance. And so one of the surprising findings that they had was that
many of the training data sets that people used to train single-cell foundation models are highly
redundant, to the point that you can even sample just a tiny fraction of the data and get basically
the same performance then. But you can also look forward to many other explorations in this area
as we continue to develop this research at the end of the day. But also zooming out into the bigger
picture, I think one major takeaway from this paper is that developing AI methods for biology
requires thought about the context of use, right? I mean, this is obvious for any AI method then,
but I think people have gotten just too used to taking methods that work out there for natural
vision or natural language maybe in the consumer domain and then extrapolating these methods
to biology and expecting that they will work in the same way then, right? So for example,
one reason why zero-shot evaluation was not routine practice for single-cell foundation models
prior to our work, I mean, we were the first to fully establish that as a practice for the field,
was because I think people who have been working in AI for biology have been looking to these more
mainstream AI domains to shape their work then. And so with single-cell foundation models, many
of these models are adopted from large language models with natural language processing, recycling
the exact same architecture, the exact same code, basically just recycling practices in that field
then. So when you look at like practices in like more mainstream domains, zero-shot evaluation is
definitely explored in those domains, but it's more of like a niche instead of being considered
central to model understanding. So again, because biology is different from mainstream
language processing, it's a scientific discipline, zero-shot evaluation becomes much more important,
and you have no choice but to use these models, zero-shot then. So in other words,
I think that we need to be thinking carefully about what it is that makes training a model
for biology different from training a model, for example, for consumer purposes.
Huizinga: Alex Lu, thanks for joining us today, and to our listeners,
thanks for tuning in. If you want to read this paper, you can find a link at aka.ms/Abstracts,
or you can read it on the Genome Biology website. See you next time on Abstracts!