Oct 8, 2024 · Automatic tools have been created for analysis of these big data sets, and one of the most successful and widely used tools in flow cytometry today is called t-SNE, or t-Stochastic Neighbor Embedding.

Sep 22, 2022 · tSNE-CUDA is a state-of-art implementation of the t-SNE algorithm. It utilizes GPU to significantly reduce the computational time of the t-SNE algorithm. This 2019 publication shows that tSNE-CUDA significantly outperformed many current t-SNE implementations.

Furthermore Cytosplore provides Approximated-tSNE (up to 100x faster than standard t-SNE without loss in precision) and a custom implementation of the SPADE clustering algorithm.

In essence, t-SNE transforms each high-dimensional object (in this case, each cell which has many genes) to a two-dimensional point (sometimes three), in such a way, that when we plot it, cells with similar gene profiles will be assigned to nearby points, and cells with very different gene profiles will have distant coordinates.

Mar 25, 2024 · tSNE is a very robust method that provides great preservation of the relationships between similar cells. It tells us next to nothing about points that are far away from each other. Generally, if there's white space between the points, the distance is meaningless.

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 called using the default settings, but can be altered by passing relvant arguments through cyto_map.

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 similarity of these events or observations in the high-dimensional space of the dataset.

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 dimensionality of your data is very high.

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 demonstrates how to combine dimensionality reduction with manual gating to classify populations.

Cytosplore Transcriptomics is a stand-alonesoftware application for fast and interactive exploration of cell types from single cell sequencing data. It scale to millions of cells at still interactive speeds, and runs on PC (windows 10) and linux (Ubuntu).