# PCA provides principal directions, what does tSNE provide?

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.

I can't comment since I don't have enough reputation, but I will try to give a small answer.

Essentially, the reduced dimensions of t-SNE are not intended to carry meaning (although they might be correlated to something meaningful by chance). t-SNE is mostly used as a visualization technique, and its use as a dimensionality reduction technique is muddled.

In addition, since t-SNE is non-convex and depends on initialization, your final dimensions might have different "meaning" every time you re-run it. It is entirely dependent on the run and your full dataset (part of the reason why sklearn's tSNE does not provide a transform function, only fit_transform).

The original t-SNE paper discusses this and how t-SNE dimensionality reduction is not clear for reduced components of dimensionality d>3.

In addition, distances in the reduced space do not mean much as t-SNE is trying to minimize KL-divergence.

See this question and this one too

• Thanks for the leads. – MadmanLee Nov 14 '19 at 2:03