One of my main frustrations with the current state of single cell transcriptome analysis is representations of cells within $tSNE$ plots.
These $tSNE$ plots provide amazing separation of the data and are championed as better than $PCA$ for revolutionary displays. But $PCA$ provides an easy understanding of what is separating the data.
So, by using $PCA$ for my dimensional reduction technique I also obtain the principal directions of the data. From a $n x m$ matrix where $n = cells$ and $m=genes$, the principal components show the $cells$ separation. This analysis also provides the principal directions for the understanding of the structure of the $cells$ separation due to the $genes$. This is nice because I can search and localize genes providing this separation in different quadrants of the graph.
What i'm struggling to understand with the $tSNE$ is how can I achieve something synonymous to the principal directions in these plots. I want to know what genes are guiding each cluster of cells in these $tSNE$ plots with the ultimate goal of providing context to these beautiful $tSNE$ graphs.