Connor Meehan, Jonathan Ebrahimian, Wayne Moore, and Stephen Meehan (2022). Uniform Manifold Approximation and Projection (UMAP) (https://www.mathworks.com/matlabcentral/fileexchange/71902), MATLAB Central File Exchange.
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Fixes and improvements based on feedback from CYTO 2023 conference.
Corrected documentation in run_umap for examples 4 & 5 which use FlowJo.
1. Integration with FlowJO
1) Improved documentation and examples for using MLP train/predict independently of UMAP
-mlp_train combines neural network and supervised template classification
1. Fast approximation now accelerates both matching and reduction processing.
2. Prediction table now:
V3.0 improves speed, classification assessment and ROI functionality. For details see the last section of the FileExchange description and/or search the run_umap.m file for fast_approximation, run_epp and match_predictions.
-New table showing density distribution & KLD of unreduced data associated with groupings of the reduced data
Fix edge case where running template fails IF the metric is a user defined function.
-Added parameters to run_umap "wrapper" that reach more capabilities within the UMAP.m core; search "v2.1.2" in run_umap.m to see these additions.
-Maximized UMAP parallelism speed by using all MATLAB’s assigned logical CPU cores
-Stochastic gradient descent (SGD) is now parallelized by default with our MEX method. See 'sgd_tasks' in the documentation.
-Improved documentation for some arguments and removed all popups when "verbose" is false
-Removed .exe and .MEX files to comply with File Exchange requirements. Users are now encouraged to download these from our Google Drive if they wish to significantly speed up run_umap.
-Fixed a bug in SGD in Java where data was unintentionally stored as two distinct objects
-Fixed some minor cosmetic issues such as suboptimal plot scaling
-If applying a UMAP template on data that appears to have new populations, a warning occurs and the option is given to perform a re-supervised reduction
-Fixed a GUI bug that would occur for users with MATLAB R2018b or earlier
-Data can now be reduced to any number of dimensions by changing the 'n_components' parameter; if reducing to more than 2 dimensions, a 3D plot is shown
-Added precomputed parameter values for users without the Curve Fitting Toolbox
-Added 2 examples (run_umap.m) showing how to perform supervised dimension reduction with UMAP