Question: Is it possible to interpret similarities between gene expression in clusters based on distance from one cluster center to another cluster center in the 2D t-SNE space? If not, is there another way to use the 10x single-cell data to say that two clusters are more similar to each other than they are to the rest of the clusters?
Answer: We do not recommend using the distances between the clusters in t-SNE space to interpret how similar any two particular clusters are. The distances between points/cells are relative - because the algorithm is non-linear and adapts to underlying data, the distances on the x and y-axis have no direct interpretation. As described in this t-SNE primer, "The algorithm is non-linear and adapts to the underlying data, performing different transformations on different regions." For details about how Cell Ranger constructs t-SNE, please refer to our support page: algorithms overview.
If you want to evaluate differences between any two clusters, you could run a differential expression analysis between the clusters to identify the differentially expressed genes. You can do this in Loupe Cell Browser. Here is some more information on how to compute differential expression between cell clusters.
It is possible to import other dimensionality reduction projections derived using other tools (such as Seurat, Scanpy, or PHATE) into Loupe. This page provides detailed instructions in the "Projections" section.
For more information on t-SNE, please refer to this publication: Maaten, L.V.D. and Hinton, G., 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, Vol 9(Nov), pp. 2579—2605.