openmedical/registry
← registry

scnym

imported

software/scnym

Semi-supervised adversarial neural networks for classification of single cell transcriptomics data

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
Apache-2.0(osi)
Status
active
Maturity
deployed
Organization
calico
Country
unknown
Documentation
unknown
Tags
adversarial-training · rna-seq · semi-supervised · single-cell · single-cell-genomics
Regulatory
unknown
built by · 5

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.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • scCustomizesingle-cell · single-cell-genomics

    R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.

  • scgensingle-cell · single-cell-genomics

    Single cell perturbation prediction

  • SCpubrsingle-cell · single-cell-genomics

    Generate high quality, publication ready visualizations for single cell transcriptomics data.

  • Seurat.utilssingle-cell · single-cell-genomics

    Various utility functions for Seurat v5 single-cell analysis

  • TileDB-SOMAsingle-cell · single-cell-genomics

    Python and R SOMA APIs using TileDB’s cloud-native format. Ideal for single-cell data at any scale.

  • SCALEXsingle-cell · single-cell-genomics

    Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

sources
  1. api.github.com/repos/calico/scnym
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-06-10, 78 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/53.json→ .entries["scnym"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.