Hey guys, in this video I wanted to go over one aspect of doing single cell rna-seq analysis
which is cluster labeling. When you're doing single cell RNA-seq analysis--
and I'm doing this using the seurat package in R--you can run and produce what are called t-SNE plots, which look like this.
They're kind of similar to PCA plots.
And what this will produce automatically in seurat is clusters that are labeled with just numbers.
In this particular example, I have seven clusters produced and that's numbered starting with the number zero.
That's numbered zero to six. So I want to figure out, what do these clusters mean?
What type of cell types are each of these clusters? And to do that we have to use
genetic markers to figure out how to label these different clusters. That's what I'm going to go over in this video.
I'll try to put all this code that I have in the description box below.
And also I'm using a publicly available dataset that I'll also put a link to in the description box.
I'm using this publicly available dataset from the Allen Institute and in particular I'm using some thalamic
nuclei from the human. I have nuclei from the human lateral geniculate nucleus.
I'm looking at neuronal cell types, and I want to be able to label the different cell types from this
thalamic nuclei. Once you have the t-SNE plots that are unlabeled, then what you can do is go through
using known genetic markers to test what the different clusters are
showing you for the different cell types. I have some examples written down in my notes. So I have
2571, which is the gene ID for GAD1, which is a genetic marker for
GABAergic neurons.
I'm going to save that as a feature and then seurat has four plots that you can use to
figure out how to label your clusters.
It has ridge plots, violin plots, feature plots, and dot plots and usually these will all show you kind of similar
conclusions. Hopefully these four different plots will give you the same answer about where these different neuronal markers are being expressed.
I'm just going to run those four plots and then go through them.
The first one was a ridge plot, and it will basically show you a distribution of the genetic markers. Again, I'm looking at
2571, which is for GABAergic neuron.
And this should show you the distribution of that particular gene in these seven clusters. So this is zero,
so a lot of the clusters are showing high zero expression and then you're seeing
high-level expression for two clusters: cluster five and cluster two.
It's possible that cluster five and cluster two are the cell type of GABAergic neuron.
And then if I go to the violin plots, it's showing me a similar conclusion.
So again,
it's looking at high expression level at cluster two and cluster five for this one genetic marker.
Then the feature plot is showing high expression for this area of the t-SNE plot and this area is
cluster two and cluster five.
So again,
we're seeing high expression in those two clusters and less expression in the other clusters.
And finally the dot plot again shows a similar idea.
You see high expression and high percent expressed in cluster two and cluster five.
In the original t-SNE plot, the algorithm was separating cluster 2 and cluster 5 as different clusters
but this analysis is showing that you might have a reason to merge these two clusters labels together into one cluster.
The next and final part of my code shows how you can actually change the
cluster labeling into the different cell type IDs and add that to the t-SNE plot and your seurat object.
The first thing I have here is a vector that has the original default
cluster IDs, which are just numbers.
In my particular case, my t-SNE plot currently produces seven clusters that are numbered 0 through 6.
The next vector that I have in the code is
what I want my cell type labels to be for each cluster.
And so this is going to be a vector that has the same size as the current cluster IDs.
So in my particular case, I have seven cell type
IDs labeled here. And the zero is going to be replaced in this case by glutamatergic neuron.
The one label is going to be replaced by oligodendrocyte.
Two is going to be replaced by GABAergic neuron, and so on. And it's in this step that you can also merge different clusters.
Like we showed before, cluster two and cluster five were both considered to be GABAergic neuron.
It will merge the two different clusters into one cluster
as long as you give them the same ID name. Here I've labeled cluster two and cluster five as GABAergic neuron.
So since they have the same ID,
they're going to be merged in the code after I run this. And then I just add the code names and add it to my
seurat object using those two lines. And then when I re-plot my t-SNE plot,
instead of having the clusters be labeled zero through six
we see that now these have the cell type IDs
added to the t-SNE plot. And that's all I wanted to go over in this video.
I think I'll make another video on single-cell RNA seq analysis--
although I don't know how much of an audience there is for that--but I do also know how to integrate across multiple
seurat objects to do an integrated analysis.
I'm thinking I'll make another video on that, but that's it for now and thank you guys for watching :)
*chiptune music*