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Found 5 Skills
Analyze mass spectrometry proteomics data including protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein interactions. Processes MaxQuant, Spectronaut, DIA-NN, and other MS platform outputs. Performs normalization, statistical analysis, pathway enrichment, and integration with transcriptomics. Use when analyzing proteomics data, comparing protein abundance between conditions, identifying PTM changes, studying protein complexes, integrating protein and RNA data, discovering protein biomarkers, or conducting quantitative proteomics experiments.
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.
Reason about quantitative proteomics experiment design and data analysis strategy. Use when the user asks to choose between LFQ, TMT, and DIA quantification; select an imputation method for missing values; pick a normalization strategy; interpret differential expression results from proteomics data; evaluate ratio compression; or design a proteomics study for biomarker discovery or validation. Triggers include "LFQ vs TMT", "DIA quantification", "proteomics normalization", "missing value imputation", "MNAR", "MinProb", "QRILC", "kNN imputation", "VSN normalization", "median centering", "quantile normalization", "limma proteomics", "ratio compression", "proteomics study design", "label-free quantification", "tandem mass tag", "data-independent acquisition", "DIA-NN", "Spectronaut", "MaxQuant LFQ", "proteomics differential expression", "empirical Bayes proteomics", "proteinGroups.txt", "MSFragger output", "TMT normalization code", "LFQ analysis", "proteomics pipeline R", "MaxQuant output".
Bulk RNA-seq analysis pipeline covering alignment (STAR), quantification (Salmon, featureCounts), and differential expression (DESeq2). Triggers on RNA-seq, STAR, Salmon, DESeq2, differential expression, gene expression, tximport, featureCounts, transcriptomics, "bulk RNA-seq", "STAR alignment", "Salmon quantification", "gene counts", "DESeq2 results", "shrinkage estimator", "volcano plot RNA-seq".
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.