Hi! Welcome to our site. I hope if you’re here, it means that you enjoyed the paper, and want to hear some more about our work. In this brief blog post, we would like to tell you a little bit about our thoughts on how to use RNA-seq to identify internal states in multicellular organisms.

RNA-seq, as you will know by now, is a fantastic tool with which to explore transcriptome-wide relationships. A frustration that comes with these tools, however, is the feeling that they are purely a descriptive apparatus.

Luckily, this is changing. Recently, many labs, including us, have begun to use RNA-seq as a quantitative tool to discover not gene targets, but rather genetics interactions (see Aviv Regev’s wonderful paper on T-cells or maybe our paper on reconstructing the hypoxia pathway). As you may have realized, though, the focus of this particular paper was not to identify genetic interactions. Rather, we tried to identify a novel state of the C. elegans life cycle. Somewhat amazingly, the statistical method that we used to identify this state is exactly the same method that Aviv’s team used to reconstruct genetic interactions in T-cells!

I think there’s something really interesting in that. Maybe I can explain.

First, though, a bit of history. Usually, when people do RNA-seq, they make what is called a PCA plot. In few words, Principal Component Analysis takes an enormous matrix of data and understands it to find the multidimensional lines that your data lies on most compactly. Then, we plot the two lines that most compactify your data. That gives you a compact description of the data in a way that is easy to visualize. This is great, because plotting N-dimensional data is a real problem. On the other hand, what do these coordinates mean? The new coordinates are composite coordinates – they are a number made by adding very many components together in a weighted manner. Well, it seems we’ve just traded one problem for another! Indeed, the problem of how to interpret PC coordinates is rather complicated, but certain papers (for an example, see this one) have begun to make progress in using PCA to understand how cellular states are related to gene expression.

Well then.

Another way to try to understand this data is to give up on trying to predict the function of every single gene in this scenario, and instead try to say something about the relationship between the variables that we played with in this study. Ah! Fortunately, this problem is very old, and it has been tackled very aggressively.

Whenever we are trying to assess the effect of a variable on an output, the first thing to try if the trend looks linear (or if the variable is dichotomous) is to fit a linear model. Simply put, a linear model works as follows:

Imagine that you are walking on a straight line. Suppose we are measuring the effect that mutations on two genes $G_1$ and $G_2$ have on you. We find that knocking out $G_1$ makes you walk two steps forward; whereas knocking out $G_2$ makes you walk three steps forward. Both genes affect the same phenotype (your movement), and therefore they are not entirely independent. How independent are they? In genetics, there are three kinds of genetic interactions: non-interaction (or what I call true independence), additive interaction (most people still call this independence), and non-additive interaction. To test whether these two genes interact, we should make a double mutant. If the genes are interacting additively, then you should walk two steps + three steps = five steps. If the genes are not interacting additively, we expect that you will walk less than five steps if the genes are in the same pathway, or more than five steps if they share a synthetic interaction.

People have been doing exactly this kind of analysis with qPCR for a very long time. More recently, people have begun to do exactly this kind of analysis with RNA-seq and long story short (if you are seriously interested, you should look out for our other paper coming out soon) it works well. It’s exactly like doing qPCR, except 20,000 times.

But there’s something else that is wonderful about this idea. So far, I’ve been talking about interactions between genes. But there’s nothing, absolutely nothing in the book that says that the model couldn’t mix interactions between genes and the environment or time or internal states. And that is what makes, in my opinion, this paper so special. This is the first time that we have observed a developmental stage in C. elegans using transcriptomes, and the way in which we observed it was exactly by using a linear model that measures deviations from additivity.

So what do you think? Is this cool? Let us know!

—David Angeles