Reverse Dependencies of scikit-misc

The following projects have a declared dependency on scikit-misc:

• actxps — Create Actuarial Experience Studies: Prepare Data, Summarize Results, and Create Reports
• awgan — code for model AWGAN
• Cellist — Cellist (Cell identification in high-resolution Spatial Transcriptomics) is a cell segmentation tool for high-resolution spatial transcriptomics.
• cellxgene-census — API to facilitate the use of the CZ CELLxGENE Discover Census. For more information about the API and the project visit https://github.com/chanzuckerberg/cellxgene-census/
• ComSeg — single cell RNA profiling analysis of imaging-based spatial transcriptomics data
• convexgating — ConvexGating is a Python tool to infer optimal gating strategies for flow cytometry and cyTOF data.
• cospar — CoSpar: integrating state and lineage information for dynamic inference
• CSNet — short description
• dpi-sc — An end-to-end single-cell multimodal analysis model with deep parameter inference.
• ehrapy — Electronic Health Record Analysis with Python.
• erlab-coat — COAT: COVID-19 Statistical Analytics
• gene-trajectory — Compute gene trajectories
• genevector — Single Cell GeneVector Library
• gfpa — Gene function and cell surface protein association analysis based on single-cell multiomics data.
• grmpy — grmpy is a Python package for the simulation and estimation of the generalized Roy model.
• lantsa — Landmark-based transferable subspace analysis for single-cell and spatial transcriptomics
• mrvi — Multi-resolution analysis of single-cell data.
• nichecompass — End-to-end analysis of spatial multi-omics data
• omicverse — OmicVerse: A single pipeline for exploring the entire transcriptome universe
• osgpy — Python utilities of the Operating System Group
• panpipes — Panpipes - multimodal single cell pipelines
• pegasuspy — Pegasus is a Python package for analyzing sc/snRNA-seq data of millions of cells
• pertpy — Perturbation Analysis in the scverse ecosystem.
• plotnine — A Grammar of Graphics for Python
• popv — Consensus prediction of cell type labels with popV
• pydance — Deep Learning for Single-cell Analysis
• pyrovelocity — A multivariate RNA Velocity model to estimate future cell states with uncertainty using probabilistic modeling with pyro.
• rapids-singlecell — running single cell analysis on Nvidia GPUs
• scanpy — Single-Cell Analysis in Python.
• scCloud — scRNA-Seq analysis tools that scale to millions of cells
• scdataloader — a dataloader for single cell data in lamindb
• scdrs — Single-cell disease-relevance score
• scFates — scanpy compatible python suite for fast tree inference and advanced pseudotime downstream analysis
• scgen — ScGen - Predicting single cell perturbations.
• scgpt — Large-scale generative pretrain of single cell using transformer.
• scib — Evaluating single-cell data integration methods
• scmvae — a comprehensive single-cell multimodal analysis python package based on mixed variational autoencoder
• scnym — Semi supervised adversarial network networks for single cell classification
• scrnatools — Tools for single cell RNA sequencing pipelines
• scslat — A graph deep learning based tool to align single cell spatial omics data
• sctdl — no summary
• scTM — A toolbox for single cell topic models
• scvega — VEGA: a VAE Enhanced by Gene Annotations for interpretable scRNA-seq deep learning
• scvi — Single-cell Variational Inference
• scvi-tools — Deep probabilistic analysis of single-cell omics data.
• sdeper — Spatial Deconvolution method with Platform Effect Removal
• SOAPy-st — Spatial Omics Analysis in Python
• spametric — Metric learning for Spatial transcriptomics
• sparcl — Relational Contrastive Learning for Spatial Transcriptomics
• spasrl — Spatially aware self-representation learning
• STACCI — STACCI for STCase
• step-kit — STEP, an acronym for Spatial Transcriptomics Embedding Procedure, is a deep learning-based tool for the analysis of single-cell RNA (scRNA-seq) and spatially resolved transcriptomics (SRT) data. STEP introduces a unified approach to process and analyze multiple samples of scRNA-seq data as well as align several sections of SRT data, disregarding location relationships. Furthermore, STEP conducts integrative analysis across different modalities like scRNA-seq and SRT.
• STpipe-sc — STpipe is designed to analyze spatial transcriptomic data.
• vivs — Calibrated Variational Inference for single-cell omics.