arboreto
발현 데이터에서 유전자 조절 네트워크 추론
متاح أيضًا من: K-Dense-AI
전사체학 데이터 분석하여 유전자 조절 상호작용을 식별하는 것은 시간이 많이 걸리고 특수 알고리즘이 필요합니다. Arboreto는 대량 RNA-seq 또는 단일세포 RNA-seq 데이터에서 확장 가능한 유전자 조절 네트워크 추론을 위해 GRNBoost2 및 GENIE3 알고리즘을 제공합니다.
تنزيل ZIP المهارة
رفع في Claude
اذهب إلى Settings → Capabilities → Skills → Upload skill
فعّل وابدأ الاستخدام
اختبرها
استخدام "arboreto". Infer gene regulatory network from expression_data.tsv using arboreto, output to network.tsv
النتيجة المتوقعة:
- Network contains 15,420 regulatory links
- Top regulators: GATA1 (targets: 234), TAL1 (targets: 189), KLF1 (targets: 156)
- Results saved to network.tsv with columns: TF, target, importance
استخدام "arboreto". Run GENIE3 on bulk RNA-seq data with human transcription factors
النتيجة المتوقعة:
- Inferred 23,567 regulatory links from 500 genes x 48 samples
- Top TF-gene associations saved to rnaseq_grn.tsv
- Importance scores range from 0.15 to 0.92
استخدام "arboreto". Use distributed computing to analyze 50,000 cell scRNA-seq dataset
النتيجة المتوقعة:
- Connected to Dask cluster at tcp://scheduler:8786
- Distributed inference across 8 workers completed in 45 minutes
- Generated network with 156,892 regulatory interactions
التدقيق الأمني
مخاطر منخفضةArboreto is a legitimate bioinformatics tool for gene regulatory network inference. All 116 static findings are FALSE POSITIVES: the scanner misidentified markdown code blocks as shell commands, YAML frontmatter as cryptographic code, and standard Dask cluster IP addresses as suspicious network targets. The skill reads/writes user-provided data files, uses standard ML libraries (pandas, scikit-learn), and has no malicious patterns, credential access, or network exfiltration.
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단일세포 GRN 분석
scRNA-seq 데이터에서 세포 유형별 전사 인자-표적 유전자 관계 식별
대량 RNA-seq 네트워크 추론
여러 조건에 걸친 대량 전사체학 실험에서 조절 네트워크 구축
SCENIC 파이프라인 통합
SCENIC 조절자 발견 워크플로우에서 GRN 추론 단계로 arboreto 사용
جرّب هذه الموجهات
Use the arboreto skill to infer a gene regulatory network from my expression matrix at data/expression.tsv. Output results to results/network.tsv.
Run GRNBoost2 inference using arboreto on data/expression.tsv with transcription factors from data/tfs.txt. Save to results/tf_network.tsv.
Run both GRNBoost2 and GENIE3 using arboreto on data/expression.tsv. Compare the top regulatory links from each algorithm.
Use arboreto with a Dask distributed client to infer a GRN from large scRNA-seq data. Connect to tcp://cluster:8786 scheduler.
أفضل الممارسات
- Always use if __name__ == '__main__' guard when running scripts to prevent Dask process spawning issues
- Set a random seed for reproducible results across runs
- Filter to known transcription factors to reduce computation time and improve relevance
تجنب
- Running arboreto on raw read counts without normalization or filtering
- Interpreting importance scores as definitive causal relationships without validation
- Ignoring memory requirements for large datasets; use distributed computing for >10,000 cells