scanpy

Plugin scanpy from marketplace helmholtz-agent-skills in HelmholtzAI-Consultants-Munich/helmholtz-agent-skills. Commands read from the repo's manifest — 18 days ago.

Skills (8)

• anndata-data-structure

  Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.

• cell-type-annotation

  scRNA-seq cell type annotation - read before any annotation skills. Chooses and runs the route: name clusters manually from canonical markers (scanpy, runnable code here), transfer labels with CellTypist or a popV/scANVI on a reference, or do both and validate one against other. Covers package avail

• celltypist-cell-annotation

  Automated scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without m

• cellxgene-census

  Query CELLxGENE Census (61M+ cells). Search by cell type/tissue/disease/organism; get AnnData, stream out-of-core, train PyTorch models. For your own data use scanpy; for annotated data use anndata.

• harmony-batch-correction

  Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).

• popv-cell-annotation

  Consensus cell type annotation: runs 10+ algorithms (KNN-Harmony/BBKNN/Scanorama/scVI, CellTypist, ONCLASS, Random Forest, SCANVI, SVM, XGBoost) on a labeled reference and transfers labels via majority voting. Outputs per-method labels, consensus, agreement score. Use when single-method annotation i

• scanpy-scrna-seq

  scRNA-seq with Scanpy: QC, normalization, HVG selection, PCA, neighborhood graph, UMAP/t-SNE, Leiden clustering, markers, cell annotation, trajectory inference. Standard scRNA-seq exploration.

• scvi-tools-single-cell

  Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear co