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Found 8 Skills
Guide Claude through omicverse's single-cell clustering workflow, covering preprocessing, QC, multimethod clustering, topic modeling, cNMF, and cross-batch integration as demonstrated in t_cluster.ipynb and t_single_batch.ipynb.
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
Reasoning skill for multi-omics data integration strategy selection. Use when the user asks to integrate transcriptomics with proteomics, combine multi-omic layers, choose between early intermediate or late integration, apply batch correction across omics, handle partial sample overlap, run MOFA+ or iCluster, interpret multi-omic factors, select enrichment methods for multi-omic signatures, or decide how to merge genomics epigenomics transcriptomics proteomics and metabolomics data. Triggers include "multi-omics integration", "combine omics layers", "early vs late fusion", "MOFA+", "iCluster", "batch correction across omics", "partial overlap", "multi-omic enrichment", "kernel integration", "concatenation vs stacking", "SNF", "similarity network fusion", "intermediate integration", "multi-omic factor analysis".
Pipeline skill for multi-omics data processing and integration. Use when the user asks to map gene IDs between HGNC Ensembl and UniProt, convert between omic data formats, run batch correction with ComBat or ComBat-seq, perform GSEA or over-representation analysis on multi-omic results, run consensus clustering on integrated data, execute MOFA2 in R or mofapy2 in Python, build a multi-omics ETL pipeline, harmonize feature identifiers across omics layers, or run clusterProfiler enrichment. Triggers include "map Ensembl to HGNC", "ID mapping omics", "ComBat code", "run MOFA2", "mofapy2", "GSEA Python", "fgsea R", "consensus clustering", "multi-omics pipeline", "gseapy", "biomaRt", "clusterProfiler", "mixOmics DIABLO".
Scanpy-based single-cell RNA-seq analysis pipeline covering loading (10X, h5ad), QC, normalization, HVG selection, PCA/UMAP, Leiden clustering, differential expression, and batch correction (Harmony, scVI). Use when the user mentions single-cell, scRNA-seq, Scanpy, AnnData, UMAP, clustering, 10X, h5ad, leiden, highly variable genes, Harmony, scVI, cluster cells, find marker genes, filter low-quality cells, gene expression matrix, doublet removal, normalize counts, dimensionality reduction, cell clustering, QC single-cell, filtered_feature_bc_matrix, cellranger output, 10X h5, scrublet, cell ranger, count matrix, find cell types, batch effect, integration, neighborhood graph, or differential expression genes.
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
Issue/PR の状態とプロジェクト Status の自動同期を設定する。モード A は `Fandhe-AI/actions/project-sync` Composite Action を使う `.github/workflows/project-sync.yml` を生成 (PAT または GitHub App トークン)。モード B は既存の不整合を一括補正。「自動同期セットアップ」「PR レビュー時に In Review 化」「ボードと Issue の状態を同期」などで使用。
This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.