scarches
importedsoftware/scarches
Reference mapping for single-cell genomics
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- BSD-3-Clause(osi)
- Status
- active
- Maturity
- deployed
- Organization
- theislab
- Country
- unknown
- Homepage
- docs.scarches.org/en/latest/
- Repository
- github.com/theislab/scarches
- Documentation
- unknown
- Tags
- batch-correction · data-integration · deep-learning · human-cell-atlas · multimodal-deep-learning · multiomics · rna-seq-analysis · scrna-seq
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- Genes2Geneshuman-cell-atlas · scrna-seq
Aligning gene expression trajectories of single-cell reference and query systems
- scAlignhuman-cell-atlas · scrna-seq
A deep learning-based tool for alignment and integration of single cell genomic data across multiple datasets, species, conditions, batches
- cellhintdata-integration · scrna-seq
A tool for semi-automatic cell type harmonization and integration
- sccompbatch-correction
Bayesian mixed-effect model to test differences in cell type proportions from single-cell data, in R
- BiModNeuroCNNmultimodal-deep-learning
Package for bimodal training of deep neural networks on neurological data. Pypi: https://pypi.org/project/BiModNeuroCNN/
- MCATmultimodal-deep-learning
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images - ICCV 2021
- api.github.com/repos/theislab/scarchesretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-06-26, 407 stars, license reported as BSD-3-Clause. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/55.json→ .entries["scarches"]
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