Uniform Manifold Approximation and Projection

JavaScript implementation of UMAP

An R package implementing the UMAP dimensionality reduction method.

Visualization for a Retrieval-Augmented Generation (RAG) Assistant 🤖❤️📚

TorchDR - PyTorch Dimensionality Reduction

Seurat meets tidyverse. The best of both worlds.

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

Manage map and features with Leaflet and expose them for backend storage through an API.

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia

PANDORA :computer:

Uniform Manifold Approximation and Projection - R package

iCellR is an interactive R package designed to facilitate the analysis and visualization of high-throughput single-cell sequencing data. It supports a variety of single-cell technologies, including scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq, and Spatial Transcriptomics (ST).

This repository contains R code, with which you can create 3D UMAP and tSNE plots of Seurat analyzed scRNAseq data

A JavaScript Library for Dimensionality Reduction

Uniform Manifold Approximation with Two-phase Optimization (IEEE VIS 2022 short)

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

:no_entry: ARCHIVED :no_entry: Wraps UMAP Algorithm for Dimension Reduction

Tera Online 32-bit and 64-bit client package(*.gpk, *.gmp, *.upk, *.umap, *.u) editor/viewer

Browse the top 10,000 packages on PyPI with the help of vector embeddings

sciBASIC# is a kind of dialect language which is derive from the native VB.NET language, and written for the data scientist.

R package for dimensionality reduction of small datasets

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

create "Karpathy's style" 2d images out of your image embeddings

Pure MLX implementations of UMAP, t-SNE, PaCMAP, TriMap, DREAMS, CNE, and NNDescent for Apple Silicon. Metal GPU for computation and video rendering.

A general purpose Snakemake workflow and MrBiomics module to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

[Communications Chemistry 2023] Highly accurate and large-scale collision cross section prediction with graph neural network for compound identification

C# library for fast embeddings projection using Uniform Manifold Approximation and Projection

A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).