Reverse Dependencies of umap-learn

The following projects have a declared dependency on umap-learn:

• MICTI — Feature extraction approach in single-cell gene expression profiling for cell-type marker identification.
• MiScan — MiScan: mutation-inferred screening model of cancer
• ml2json — A safe, transparent way to share and deploy scikit-learn models.
• mngs — For lazy python users (monogusa people in Japanse), especially in ML/DSP fields
• MOBiceps — Python tools for Mass Spectrometry and Omics data.
• modelscope — ModelScope: bring the notion of Model-as-a-Service to life.
• molmap — MolMap: An Efficient Convolutional Neural Network Based Molecular Deep Learning Tool
• Morphomics — morphOMICs: a python package for the topological and statistical analysis of microglia morphology (appliable to any cell structure)
• mrqy — MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.
• multi_mst — Minimum spanning tree based manifold approximations.
• multivelo — Multi-omic extension of single-cell RNA velocity
• muon — Multimodal omics analysis framework
• mvtcr — mvTCR: A multimodal generative model to learn a unified representation across TCR sequences and scRNAseq data for joint analysis of single-cell immune profiling data
• n2d — (Not too) deep clustering
• nabo — Python library to perform memory efficient cross-sample cell mapping using single cell transciptomics (scRNA-Seq) data
• napari-clusters-plotter — A plugin to use with napari for clustering objects according to their properties
• napatrackmater — Import Trackmate XML files for Track Visualization and analysis in Napari.
• NEExT — Unsupervised Graph Analysis Framework.
• nemi-learn — Code to accompany paper
• neurocode — 🧠 EEG/MEG self-supervised learning toolbox.
• nhssynth — Synthetic data generation pipeline leveraging a Differentially Private Variational Auto Encoder assessed using a variety of metrics
• nimble-raccoon — A slim and fast implementation of the RACCOON clustering library.
• njab — not Just Another Biomarker
• nkululeko — Machine learning audio prediction experiments based on templates
• nlpsig — Path signatures for Natural Language Processing.
• nrtk-explorer — Model Visualizer
• obsinthe — no summary
• OCAT — A new single-cell analytics framework
• octid — One-Class learning-based tool for Tumor Image Detection
• oggmap — extract orthologous maps (short: orthomap) from OrthoFinder output for query species
• omniplot — To draw scientific plots easily
• OnClass — Single Cell Annotation based on the Cell Ontology
• openlabcluster — OpenLabCluster
• opsci-toolbox — a complete toolbox
• oreum-core — Core tools for use on projects by Oreum Industries
• orthomap — extract orthomap from OrthoFinder output for query species
• panpiper — Panpiper: snakemake workflow for bacterial isolate analysis
• parc — no summary
• pathogen-embed — Reduced dimension embeddings for pathogen sequences
• peelml — Peel away the pain of ml deployment
• pegasuspy — Pegasus is a Python package for analyzing sc/snRNA-seq data of millions of cells
• phenonaut — A toolkit for multiomic phenotypic space exploration.
• phylics — Single-cell CNV data analysis toolkit
• picturedrocks — Single Cell RNA Sequencing Marker Selection Package
• pipeGEM — Processing and integrating data with genome-scale metabolic models (GEM)
• planktonspace — A point process analysis package
• plateletanalysis — plateletanalysis
• pmap3.0.7 — TEST PMAP
• pmap3.0.8 — TEST PMAP
• ponyo — Install functions to simulate gene expression compendia
• popari — Popari: a probabilistic graphical model for integrated spatial transcriptomics analysis
• prenigma-automl — prenigma_automl - An open source, low-code machine learning library.
• prenigmaautoml — prenigma_automl - An open source, low-code machine learning library.
• protein-design — Python tools for protein design
• proteinworkshop — no summary
• psort — Graphical application for identifying simple and complex purkinje spikes
• pt-datasets — Library for loading PyTorch datasets and data loaders.
• pyBibX — A Bibliometric and Scientometric Library Powered with Artificial Intelligence Tools
• pycaret — PyCaret - An open source, low-code machine learning library in Python.
• pycaret-nightly — Nightly version of PyCaret - An open source, low-code machine learning library in Python.
• pycaret-ts-alpha — PyCaret - An open source, low-code machine learning library in Python.
• pyInfinityFlow — Impute Flow Cytometry values between overlapping panels with XGBoost regression.
• pyliger — The Python version of LIGER package.
• pypesto — python-based Parameter EStimation TOolbox
• pySPROUT — SPROUT: spectral sparsification helps restore the spatial structure at single-cell resolution.
• pyturbseq — no summary
• pyVIA — no summary
• pyvitae — Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior
• q2-umap — Sample Embedding with UMAP
• quartic-sdk — QuarticSDK is the SDK package which exposes the APIs to the user
• qubitai-dltk — Python Client for DLTK.
• QuickClus — UMAP + HDBSCAN for numeric and/or categorical variables
• qwgc — Graph classifier based on quantum walk
• raccoon-cluster — Scale-adaptive clustering in Python
• ragmap — RAGmap is a simple RAG visualization package for exploring document chunks and queries in embedding space
• ragxplorer — A open-source tool to to visualise your RAG documents 🔮.
• Recursive-Symmetry-Aware-Materials-Microstructure-Explorer — Tool for recursive symmetry aware searching of materials microstructure images
• relatio — A Python package to extract narrative statements from text
• RelevanceAI-dev — no summary
• renumics-spotlight — Visualize and maintain datasets to develop and understand data-driven algorithms.
• ressac — Resnet based single-cell ATAC-seq clustering
• retentioneering — Retentioneering is a Python library that makes analyzing clickstreams, user paths (trajectories), and event logs much easier, and yields much broader and deeper insights than funnel analysis. You can use Retentioneering to explore user behavior, segment users, and form hypotheses about what drives users to desirable actions or to churning away from a product.
• reval — Relative clustering validation to select best number of clusters
• ride — Training wheels, side rails, and helicopter parent for your Deep Learning projects using Pytorch
• roicat — A library for classifying and tracking ROIs.
• roodmus — Synthetic SP micrograph creation and analysis
• rostspace — Protein Embedding Visualization Tool.
• rusty-axe-bbrener1 — Random Forest Latent Structure (Biology)
• Sagittarius-api-test — no summary
• salamander-learn — Salamander is a non-negative matrix factorization framework for signature analysis
• samba-metric — no summary
• SC-search — Single-cell search tool
• scab — Single cell analysis of B cells
• scale — Single-Cell ATAC-seq Analysis via Latent feature Extraciton
• scale-atac — Single-Cell ATAC-seq Analysis via Latent feature Extraciton
• scanpy — Single-Cell Analysis in Python.
• scarf — Scarf
• scAtlasVAE — scAtlasVAE: a deep learning framework for atlas-scale scRNA-seq integration and analysis
• scCloud — scRNA-Seq analysis tools that scale to millions of cells
• scdna-replication-tools — Code for analyzing single-cell replication dynamics