Reverse Dependencies of leidenalg

The following projects have a declared dependency on leidenalg:

• acdc-py — A package to quickly identify unbiased graph-based clusterings via parameter optimization in Python
• aliby-post — Post-processing tools for aliby pipeline.
• allcools — Toolkit for single-cell DNA methylome and multiomic data analysis.
• ARBOLpy — python implementation of ARBOL scRNAseq iterative tiered clustering
• bento-tools — A toolkit for subcellular analysis of RNA organization
• Bering — Bering: Transfer Learning of Cell Segmentation and Annotation for Spatial Omics
• besca — Collection of BEDA internal python functions for analysing single cell RNAseq data.
• bio-present — Cross-modality representation and multi-sample integration of spatially resolved omics data
• biobookshelf — a collection of python scripts and functions for exploratory analysis of bioinformatic data in Python
• biocomet — A brief description of the biocomet package
• booleabayes — A suite for network inference from transcriptomics data
• brokerscore — Meso level network measure pbroker score as defined by Paquet-Clouston and Bouchard
• cajal — A library for multi-modal cell morphology analyses using Gromov-Wasserstein (GW) distance.
• cansig — Discovering de novo shared transcriptional programs in single cancer cells
• capital — Single-Cell Analysis, comparing pseudotime trajectories with tree alignment
• ccsf — Leveraging cell-cell similarity from gene expression data for high-performance spatial and temporal cellular mappings.
• cdlib — Community Discovery Library
• celescope — Single Cell Analysis Pipelines
• Cell-BLAST — Single-cell transcriptome querying tool
• cell-decipher — Spatial-omics data embedding and analysis
• cell2location — cell2location: High-throughput spatial mapping of cell types
• CellClear — Estimating and removing ambient expression in scRNA-seq data
• cellhint — A tool for semi-automatic cell type harmonization and integration
• Cellist — Cellist (Cell identification in high-resolution Spatial Transcriptomics) is a cell segmentation tool for high-resolution spatial transcriptomics.
• cellmaps-generate-hierarchy — A tool to generate hierarchies from protein to protein interaction networks
• cellpath — CellPath, multiple trajectories inference in single cell RNA-Seq data from RNA velocity
• cellrank — CellRank: dynamics from multi-view single-cell data
• CellSNAP — A package for enhancing single-cell population delineation by integrating cross-domain information.
• celltypist — A tool for semi-automatic cell type annotation
• champ — Modularity based networks partition selection tool
• CIA-python — Cluster Independent Annotation
• ClustAssessPy — Python package for systematic assessment of clustering results stability on single-cell data.
• commot — Cell-cell communications inference for spatial transcriptomics data via optimal transport.
• compcate — Identify and categorise corporate competitors
• ComSeg — single cell RNA profiling analysis of imaging-based spatial transcriptomics data
• connectivity-modifier — connectivity modifier
• constclust — no summary
• cospar — A toolkit for dynamic inference of cell fate by integrating state and lineage information.
• cytobench — Benchmarking library for generative algorithms
• CytoBulk — Integrating transcriptional data to decipher the tumor microenvironment with the graph frequency domain model
• DBRetina — DBRetina Python Package
• derep-genomes — A simple genome de-replication tool with fastANI
• descartes-rpa — descartes_rpa: Extract pathway features from Single-Cell
• DEWAKSS — Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision.
• doubletdetection — Method to detect and enable removal of doublets from single-cell RNA-sequencing.
• dpi-sc — An end-to-end single-cell multimodal analysis model with deep parameter inference.
• drvi-py — Disentangled Generative Representation of Single Cell Omics
• dspin — Regulatory network models from single-cell perturbation profiling
• dynamo-release — Mapping Vector Field of Single Cells
• echidna-sc — Mapping genotype to phenotype through joint probabilistic modeling of single-cell gene expression and chromosomal copy number variation.
• EmitGCL — MarsGT: A Python library for rare cell identification (Internal testing only)
• epiaster — ASTER: accurate estimation of cell-type numbers in single-cell chromatin accessibility data
• epicarousel — EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data
• eschergraph — The library that uses AI agents to enable building and searching in generalized knowledge graphs.
• eschr — A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets
• exdyn — Analyze cell state dynamics dependent on extrinsic factors
• fusemap — no summary
• garfield — Garfield: Graph-based Contrastive Learning enable Fast Single-Cell Embedding
• GeneClust — Cofunctional grouping-based feature gene selection for unsupervised scRNA-seq clustering
• genepioneer — A Python package for identifying essential genes in cancer.
• genevector — Single Cell GeneVector Library
• genomap — Genomap converts tabular gene expression data into spatially meaningful images.
• geospace-st — GeoSpace method for identifying multiscale structure in spatial transcriptomic data
• gfpa — Gene function and cell surface protein association analysis based on single-cell multiomics data.
• graph-express — Python package for the analysis and visualization of network graphs.
• graph-sc — Graph-sc
• graphcompass — Spatial metrics for differential analyses of cell organization across conditions
• graphragzen — no summary
• grnndata — Awesome gene regulatory network enhanced anndata
• harpy-analysis — single-cell spatial proteomics analysis that makes you happy
• hiconet — Hierachical Community Network, data driven omics integration
• hidef — A package for building a hierarchy based on multiple partitions on graph nodes.
• hypercluster — A package for automatic clustering hyperparameter optmization
• imagegrains — A software library for segmenting and measuring of sedimentary particles in images
• IMC — A package for processing and analysis of imaging mass cytometry (IMC) data.
• ImputeHiFI — no summary
• infercnvpy — Infercnv is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired by InferCNV, but plays nicely with scanpy and is much more scalable.
• inferelator-velocity — Inferelator-Velocity Calcualtes Dynamic Latent Parameters
• jMF2D — jMF2D: Enhancing and accelerating cell type deconvolution of spatial transcriptomics with dual network model
• kailin — KAILINss - TOGGLE(KAILIN): Single Cell Fate Tracing tools
• keypartx — A Graph-based Perception(Text) Representation
• kglab — A simple abstraction layer in Python for building knowledge graphs
• kropp — Test repository.
• lantsa — Landmark-based transferable subspace analysis for single-cell and spatial transcriptomics
• LittleSnowFox — KAILINss - TOGGLE(KAILIN): Single Cell Fate Tracing tools
• louvainvsleiden — For sampling the louvain method and leiden method on different networks
• mainframe-orchestra — Mainframe-Orchestra is a lightweight, open-source agentic framework for building LLM based pipelines and self-orchestrating multi-agent teams
• MarsGT — MarsGT: A Python library for rare cell identification (Internal testing only)
• maxfuse — Cross-modality matching of single cells via iterative fuzzy smoothed embedding
• mazebox — A suite of tools for analyzing single-cell transcriptomics data
• metachat — Spatial metabolic communication flow of single cells.
• metatime — Beta MetaTiME: annotate TME scRNA cell states
• methyltree — High-resolution, noninvasive single-cell lineage tracing based on DNA methylation epimutations.
• mipathway — MIPath pathway analysis method
• mist-vae — MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis
• mnmstpy — The initial package of MNMST
• modisco-lite — Transcription Factor MOtif Discovery from Importance SCOres - lite
• modularitypruning — Pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference.
• MorphLink — MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics
• morphometrics — A plugin for quantifying shape and neighborhoods from images.