facebookresearch / PoincareMapsLinks
The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry which is not an optimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation …
☆150Updated 4 years ago
Alternatives and similar repositories for PoincareMaps
Users that are interested in PoincareMaps are comparing it to the libraries listed below
Sorting:
- Latent spaces for single cells☆21Updated last year
- Density-preserving data visualization tools den-SNE and densMAP☆114Updated 4 years ago
- The art of using t-SNE for single-cell transcriptomics☆129Updated 10 months ago
- This Python package will allow you to replicate the experiments from our research on applying Optimal Transport as a similarity metric in…☆42Updated 2 years ago
- Archetypal Analysis network (AAnet)☆37Updated 8 months ago
- Tools for building and manipulating graphs in Python☆43Updated last month
- ☆100Updated last year
- generalized principal component analysis (GLM-PCA) implemented in python☆59Updated 4 years ago
- Deep learning for single-cell transcript counts☆89Updated 6 months ago
- Towards Gene Expression Convolutions using Gene Interaction Graphs☆75Updated 3 years ago
- ☆63Updated 5 years ago
- Attraction-Repulsion Spectrum in Neighbor Embeddings☆31Updated 3 years ago
- DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning☆121Updated 2 years ago
- Parallel opt-SNE implementation with Python wrapper☆37Updated 3 years ago
- Systematically learn and evaluate manifolds from high-dimensional data☆101Updated 3 weeks ago
- ☆26Updated 3 years ago
- Conditional out-of-distribution prediction☆63Updated last year
- An SKLearn-style toolbox for estimating and analyzing models, distributions, and functions with context-specific parameters.☆75Updated 3 weeks ago
- Gromov-Wasserstein based optimal transport for aligning single-cell multi-omics data☆76Updated last year
- Training and evaluating a variational autoencoder for pan-cancer gene expression data☆172Updated 6 years ago
- Here we address the global structure preservation by tSNE and UMAP☆47Updated 5 years ago
- Code for Nature Scientific Reports 2020 paper: "Unsupervised generative and graph neural methods for modelling cell differentiation" by I…☆18Updated 5 years ago
- Deep Graph Mapper: Seeing Graphs through the Neural Lens☆58Updated 2 years ago
- An object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data☆52Updated 7 years ago
- Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep…☆152Updated 4 years ago
- A collection of scripts and tools for loading, processing, and handling single cell data.☆74Updated 2 weeks ago
- Contrastive neighbor embeddings☆55Updated last month
- Kipoi's model zoo API☆238Updated last year
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell lev…☆180Updated 2 years ago
- PyTorch implementation of BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders☆42Updated 5 years ago