Dados e Análise

Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC. Use when handling 10X scATAC or 10X Multiome (paired RNA+ATAC) data, performing per-cell QC, choosing between ArchR/Signac/SnapATAC2 ecosystems, building per-cluster consensus peaksets, integrating with paired scRNA-seq, doublet detection (AMULET vs ArchR vs scDblFinder), or running pseudobulk differe

Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.

Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

Run omicverse's CellPhoneDB v5 wrapper on annotated single-cell data to infer ligand-receptor networks and produce CellChat-style visualisations.

Analyze datasets to extract insights, identify patterns, and generate reports. Use when exploring data, creating visualizations, or performing statistical analysis. Handles CSV, JSON, SQL queries, and Python pandas operations.

macOS development environment setup wizard. Interactive Q&A to collect preferences, auto-detect installed software, generate customized installation plan. Supports presets: fullstack, frontend, backend, data, devops. Triggers: /new-macos-setup, macos setup, dev environment, new mac

Transformar dados brutos e problemas abstratos em soluções criativas inovadoras, viáveis e de alto impacto, utilizando "mastigação" analógica e refinamento em múltiplas etapas.

MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.

Structured EDA audit for tabular competitions — target distribution, missing values, leakage scan, class imbalance, feature types, outliers, train/test shift. Use when the user says "do EDA", "audit the data", "explore the dataset", "check for leakage", or when starting a new tabular competition.