Reverse Dependencies of louvain

The following projects have a declared dependency on louvain:

• CarDEC — A deep learning method for joint batch correction, denoting, and clustering of single-cell rna-seq data.
• cardec-cite — A deep learning method for joint batch correction, denoting, and clustering of cite-seq data.
• celligner — A useful module for alligning cell lines to tumors
• champ — Modularity based networks partition selection tool
• COSCST — Single-cell RNA sequencing data excels in providing high sequencing depth and precision at the single-cell level, but lacks spatial information. Simultaneously, spatial transcriptomics technology visualizes gene expression patterns in their spatial context but has low resolution. Here, we present COSCST that combines these two datasets through autoencoder and supervised learning model to map single-cell RNA-seq data with spatial coordination and spatial transcriptomics with precise cell type annotation.
• cstreet — CStreet is a python script (python 3.6, 3.7 or 3.8) for cell state trajectory construction by using k-nearest neighbors graph algorithm for time-series single-cell RNA-seq data.
• desc — Deep Embedded Single-cell RNA-seq Clustering
• doubletdetection — Method to detect and enable removal of doublets from single-cell RNA-sequencing.
• dynamo-release — Mapping Vector Field of Single Cells
• 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
• evolocity — Evolutionary velocity with protein language models
• genomap — Genomap converts tabular gene expression data into spatially meaningful images.
• graph-sc — Graph-sc
• hidef — A package for building a hierarchy based on multiple partitions on graph nodes.
• hypercluster — A package for automatic clustering hyperparameter optmization
• icat-sc — Identify cell states across treatments in single-cell RNA sequencing experiments
• METIforST — METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics
• MultiGATE — MultiGATE single cell
• multigrate — Multigrate: multimodal data integration for single-cell genomics.
• ncem — ncem. Learning cell communication from spatial graphs of cells.
• ngs-toolkit — A toolkit for NGS analysis with Python.
• oggmap — extract orthologous maps (short: orthomap) from OrthoFinder output for query species
• orthomap — extract orthomap from OrthoFinder output for query species
• panpipes — Panpipes - multimodal single cell pipelines
• pegasuspy — Pegasus is a Python package for analyzing sc/snRNA-seq data of millions of cells
• popari — Popari: a probabilistic graphical model for integrated spatial transcriptomics analysis
• pydance — Deep Learning for Single-cell Analysis
• pyliger — The Python version of LIGER package.
• scanpy — Single-Cell Analysis in Python.
• scanpy-scripts — Scripts for using scanpy from the command line
• scbean — integration
• scbig — scBiG for representation learning of single-cell gene expression data based on bipartite graph embedding
• SCCAF — Single-Cell Clustering Assessment Framework
• scCASE — no summary
• sccastle — single-cell Chromatin Accessibility Sequencing data analysis via discreTe Latent Embedding
• scETM — Single cell embedded topic model for integrated scRNA-seq data analysis.
• scHiCPTR — An unsupervised pseudotime inference pipeline through dual graph refinement for single cell Hi-C data.
• scib — Evaluating single-cell data integration methods
• scnym — Semi supervised adversarial network networks for single cell classification
• scvelo — RNA velocity generalized through dynamical modeling
• scvi — Single-cell Variational Inference
• seekr — Count small kmer frequencies in nucleotide sequences.
• SpaGCN — SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks
• SpaGFT — SpaGFT is a python package to analyze spatial transcriptomics. It was designed to identify spatially variable genes, detect tissue modules, enhance gene expression.
• SpatialTools — spatial tools for S1000
• spider-st — Identifying spatially variable interactions
• spiderYa — Identifying spatially variable interactions
• STACCI — STACCI for STCase
• stereoAlign — A toolkit package of data integration
• stlearn — A downstream analysis toolkit for Spatial Transcriptomic data
• twitterexplorer — A Python tool for interactive network visualizations of Twitter data.
• VIPCCA — single cell integration