Dimensionality Reduction in SeqGeq

Learn how to use dimensionality reduction in this advanced SeqGeq tutorial.  In SeqGeq the dimensionality reduction platform helps to perform certain complex algorithms in just a few clicks. 

Currently, SeqGeq gives three ways to reduce dimensionality. Each one of them performs a different calculation which can be combined to obtain better results:

1. t-SNE (t-distributed stochastic neighbor embedding) is a machine learning unsupervised nonlinear dimensionality reduction algorithm useful for visualizing high dimensional data sets in a two-parameter dimension-reduced data space.
2. PCA (principal component analysis) creates a reduced dimensionality projection by multiplying the data by a vector that transforms it into the rotated version of itself to provides the best view of the differences, for as many principal components as required.
3. LDA (linear discriminant analysis) is a similar kind of projection in the data but it explicitly attempts to model the difference between the classes of data rather than similarities.

 For more information visit the SeqGeq documentation: https://docs.flowjo.com/seqgeq/dimensionality-reduction/