SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/swaruplab/operon --skill statistical-data-analysisコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Install and run the BD Rhapsody™ Sequence Analysis Pipeline (v3.0) on a shared cluster or remote Linux server with no root and no container runtime. Covers the self-contained install bundle, reference archives, FASTQ manifests, per-library YML generation, SLURM array execution, outputs, sample-tag demultiplexing, and the failure modes that cost hours — wrong Sample_Tags_Version on nuclei runs, uncapped Maximum_Threads, node-local scratch, and pinning a stale `latest` bundle.
Advanced single-cell multi-omics analysis including scRNA-seq, scCITE-seq, scATAC-seq, and TARGET-seq. Use when analyzing single-cell data, cell type identification, trajectory analysis, differential expression, UMAP/clustering, integrating protein and RNA modalities (TotalVI), or working with Scanpy, Seurat, scvi-tools. Includes workflows for MPN, hematologic malignancies, megakaryocyte biology.
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
| name | statistical-data-analysis |
| description | Omics data forge |
| keywords | ["pandas","R-tidyverse","SQL","visualization","reproducible"] |
| measurable_outcome | Deliver a cleaned dataset + statistical summary + at least one visualization or dashboard spec for each request within 1 working session (≤30 minutes). |
| license | MIT |
| metadata | {"author":"BioSkills Team","version":"1.0.0"} |
| compatibility | [{"system":"Python 3.9+ / R 4.0+"}] |
| allowed-tools | ["run_shell_command","read_file","python_repl"] |
Run the cross-language data analysis workflows (Python, R, SQL, Tableau/Power BI) described in this module to clean, analyze, and visualize biomedical datasets end-to-end.
exploratory, statistical, predictive, visualization) and required language/tooling.README.md.README.md (plus tutorials/README.md for step-by-step lessons).