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:
- 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.
- 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.
- 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/
