t-SNE, or t-distributed Stochastic Neighbor Embedding, is a visualization technique used to take complex, high-dimensional data and represent it in a more understandable two or three-dimensional map. Its primary purpose is to reveal underlying patterns, groupings, and relationships within data that are otherwise difficult to see.
Unveiling Patterns in Complex Data
Many datasets, especially in fields like biology or machine learning, are high-dimensional, containing a large number of variables for each data point. Humans are good at spotting patterns in two or three dimensions, but it’s nearly impossible to conceptualize relationships when data points are defined by dozens of features.
Dimensionality reduction techniques like t-SNE address this by transforming high-dimensional information into a low-dimensional plot. This process uncovers the inherent structure of the data, such as natural clusters of similar points that might represent distinct categories.
The Process of t-SNE Visualization
The t-SNE algorithm is designed to preserve the local structure of data, meaning that points close to each other in the original high-dimensional space should remain close in the final map. It starts by converting high-dimensional distances between data points into conditional probabilities, which represent the similarity between them. If two points have similar features, they are assigned a high probability of being “neighbors.”
The algorithm then creates a random arrangement of these points in a low-dimensional space and calculates a similar set of probabilities for this new arrangement. Its goal is to make the low-dimensional probabilities match the high-dimensional ones as closely as possible. It does this through an iterative process, adjusting the positions of the points on the map to minimize the difference between the two probability distributions.
Two parameters guide this process. “Perplexity” is a setting that relates to the number of nearest neighbors the algorithm considers for each point; typical values range between 5 and 50. The number of “iterations” determines how many times the algorithm refines the plot, and finding the right combination often requires experimentation.
Making Sense of t-SNE Plots
When interpreting a t-SNE plot, the most apparent features are the clusters of points. These distinct, well-separated groups represent meaningful categories within the original data. For example, in a biological study, each dot might represent a single cell, and clusters could correspond to different cell types based on their gene expression profiles.
Within a single, clearly defined cluster, the relative distances between points are meaningful. Points that are packed closely together are more similar to one another than points that are farther apart within that same cluster. The density of points within a cluster can also offer some insight, though it should be interpreted with care.
A common pitfall is to misinterpret the distances between separate clusters, as this space does not represent how dissimilar those groups are. Likewise, the relative size of the clusters on the plot does not necessarily reflect the actual size of those groups. The algorithm is designed to expand dense clusters and contract sparse ones to make them visible.
Where t-SNE Shines
In biology, t-SNE is frequently used to visualize single-cell RNA sequencing (scRNA-seq) data. Researchers can identify distinct cell populations from a tissue sample by seeing how cells group together based on thousands of gene expression measurements. It serves a similar purpose in flow cytometry to distinguish different types of cells.
In machine learning, t-SNE helps researchers understand what complex models are learning. For instance, it can visualize the internal representations of a neural network to see if it is successfully learning to separate different categories of images. By plotting these representations, developers get a visual confirmation of their model’s performance.
Natural Language Processing (NLP) also benefits from t-SNE. It is used to visualize word embeddings, which are numerical representations of words. By applying t-SNE, researchers can create maps where words with similar meanings, like “king” and “queen,” appear close to each other.
Important Nuances of t-SNE
To avoid over-interpreting results, be aware of several nuances of t-SNE:
- It is a visualization technique, not a clustering algorithm. While it excels at revealing potential clusters, it does not formally define them or assign data points to specific groups.
- The appearance of a t-SNE plot can be sensitive to its parameters, such as perplexity and the number of iterations. Different settings can produce visually distinct maps from the same data.
- The algorithm has a random component, so running it multiple times with identical parameters can yield slightly different results, although the main groupings should remain consistent.
- It can be computationally slow, especially for datasets containing millions of data points, making it best suited for exploratory analysis.