Skill OpenCode
Multi-Omics Factor Analysis v2 (MOFA+) with mofapy2. Jointly decompose omics layers (scRNA, ATAC, proteomics, methylation) into latent factors capturing major variation. Multi-group designs. AnnData views → MOFA object → train → variance explained → correlate factors with metadata → visualize/cluster → enrich top…
Skill OpenCode
Multi-modal single-cell analysis with muon/MuData. Joint RNA+ATAC (10x Multiome), CITE-seq (RNA+protein), other multi-omics. MuData holds per-modality AnnData with shared obs. WNN joint embedding, per-modality preprocessing, MOFA factor analysis. Use scanpy-scrna-seq for single-modality RNA; use muon when combining 2+…
Skill OpenCode
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 is insufficient or…
Skill OpenCode
Query ReMap 2022 TF ChIP-seq peak database via REST API and BED downloads. Retrieve TF peaks overlapping a region (chr:start-end), peaks near a gene, TFs by species, peaks filtered by biotype (promoter, enhancer), and BED files for a TF-cell type pair. Use for TF co-occupancy, regulatory annotation, and TF binding…
Skill OpenCode
End-to-end scRNA-seq analysis with Scanpy: QC, normalization, dimensionality reduction, Leiden clustering, marker gene identification, cell type annotation, and export, plus optional batch correction, trajectory inference, and publication figures.
Skill OpenCode
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 correction without…
Skill OpenCode
Decision framework for manual marker-based, automated (CellTypist), and reference-based (popV) cell type annotation in scRNA-seq. Three-tier strategy: Tier 1 manual markers, Tier 2 CellTypist, Tier 3 popV ensemble transfer. Use when planning or troubleshooting annotation.
Skill OpenCode
Best practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.
Skill OpenCode
Automated and marker-guided single-cell cell type annotation using CellTypist reference transfer, marker review, confidence-aware label curation, and export of both raw and curated labels.
Skill OpenCode
Cell-free protein synthesis (CFPS) optimization workflow: system selection, template design, expression prediction, disulfide engineering, solubility enhancement, and troubleshooting for in vitro protein expression.
Skill OpenCode
Verify cNMF module outputs.
Skill OpenCode
Verify inferCNVpy outputs and malignancy calls.
Skill OpenCode
Verify scRNA-seq QC outputs and AnnData integrity.
Skill OpenCode
Verify Palantir trajectory outputs.
Skill OpenCode
Design manuscript figures panel by panel for a computational biology paper: framework overview (Figure 1), task-driven application figures (2-N), supplementary figures, and caption plans, producing a structured markdown figure-design document.
Skill OpenCode
End-to-end pipeline from structured research input to a full journal-style manuscript planning package (proposal, figures, manuscript text, presentation) with iterative reviewer-style refinement.
Skill OpenCode
Full single-cell RNA-seq pipeline from raw counts to biological interpretation. Covers QC, normalization, batch integration, clustering, annotation, pseudobulk DE, trajectory inference, cell-cell communication, and TF activity. Dual-language: Seurat v5 (R) and scanpy (Python).
Skill OpenCode
Work with Effect v4 / effect-smol TypeScript code in this repo.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: