Stars
R package that allows for visualization of the KNN of an embedding compared to the KNN of the original high-dimensional data.
Fast embedding ot multidimensional datasets, great for cytometry data
The complete FlowSOM package known from R, now available in Python!
Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.
Interactive quality analysis for two-dimensional embeddings
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
Representation Learning for detection of phenotype-associated cell subsets
A modular active learning framework for Python
R package implementing fast multi-scale neighour embedding
Exploring Bayesian Optimization
Run your code in the cloud, with technology so advanced, it feels like magic!
PaCMAP: Large-scale Dimension Reduction Technique Preserving Both Global and Local Structure
Codes for our paper "Revisiting Multiple Instance Neural Networks".
Topological and Geometrical Tools for Single-cell Data
PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
implementation of the 'stochastic quartet descent MDS'
Self-Supervised Noise Embeddings (Self-SNE)
Pangea Software's Mighty Mike (Power Pete) for modern systems
Benchmarking framework based on Pareto front concept
Replication of "Auto-encoder Based Data Clustering" Song et al
A collection of 50+ trajectory inference methods within a common interface 📥📤
Experimental combination of various ideas from deep learning for dimensional reduction nad manifold analysis