The inclusion of HSNE allows the exploration of millions of cells without the need for downsampling. Furthermore Cytosplore provides Approximated-tSNE (up to 100x faster than …

cyto_map is a convenient wrapper to produce dimension-reduced maps of cytometry data using PCA, tSNE, FIt-SNE, UMAP and EmbedSOM. These dimensionality reduction functions are …

A - The Hierarchy Viewer is designed to view a cellular hierarchy of tSNE maps, enabling navigation through submaps in the hierarchy. All tSNE maps are ordered in a ring …

Using GPU accelarated tSNE and HSNE Cytosplore Transcriptomics is scalable to large datasets having millions of cells, and can interactively produce low-dimensional visualizations of the …

Sep 22, 2022 · Clustering on DR channels (e.g. viSNE /opt-SNE/ tSNE-CUDA/UMAP channels) can be a useful approach for defining groups of cells or groups of samples when the …

Apr 23, 2020 · CytoExploreR has full support for PCA, tSNE, FIt-SNE, UMAP and EmbedSOM dimensionality reduction algorithms through the cyto_map() function. The demo below …

Mar 25, 2024 · The tSNE here is being run in a manner that replicates most of the modifications of OptSNE. This includes massively increasing the learning rate (also used in OpenTSNE), as …

Sep 9, 2024 · tSNE-CUDA (as the rest of the tSNE analysis) works by repeatedly adjusting the placement of events or observations in a two-dimensional space in order to best reflect the …

#' cytometry data using PCA, tSNE, FIt-SNE, UMAP and EmbedSOM. These ... #' If you use \code{cyto_map} to map your cytometry data, be sure to cite the #' publication that describes …

Jun 21, 2019 · James also made an even more in-depth analysis of the influence of the parameters of tSNE and UMAP. He has written the smallvis package …