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Found 27 Skills
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing tumor-immune communication, or answering questions about scRNA-seq datasets.
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characterization, and validation recommendations. Uses 70+ ToolUniverse tools across 9 analysis phases. Use when users ask about spatial transcriptomics analysis, spatial omics interpretation, tissue heterogeneity, spatial gene expression patterns, tumor microenvironment mapping, tissue zonation, or cell-cell communication from spatial data.