菜单
Build weighted gene co-expression networks (WGCNA) to detect co-regulated gene modules, correlate them with sample traits, and identify hub genes; includes CEMiTool, hdWGCNA (single-cell), and PyWGCNA alternatives.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Load BioMaster as one integrated skill-driven bioinformatics assistant.
BioMaster conversational bioinformatics assistant; starts workflows only after execution intent or empty activation.
Automated and marker-guided single-cell cell type annotation using CellTypist, marker review, reference transfer, and confidence-aware label curation.
Ligand-receptor communication inference in single-cell or spatial data with sender-receiver summaries and cautious interpretation.
Workflow for paired or integrated single-cell RNA and ATAC analysis with multimodal latent spaces and regulatory interpretation.
Workflow for spatial transcriptomics preprocessing, domain detection, deconvolution, neighborhood analysis, and publication-ready spatial maps.
| name | bioskills-coexpression-networks |
| description | Build weighted gene co-expression networks (WGCNA) to detect co-regulated gene modules, correlate them with sample traits, and identify hub genes; includes CEMiTool, hdWGCNA (single-cell), and PyWGCNA alternatives. |
| domain | single-cell |
| primary_tool | WGCNA |
| methods | ["WGCNA","CEMiTool","hdWGCNA","PyWGCNA"] |
| macro_tags | ["co-expression","wgcna","module-detection","hub-genes","network","trait-correlation"] |
| micro_tags | ["WGCNA","CEMiTool","hdWGCNA","PyWGCNA","blockwiseModules","pickSoftThreshold","soft-thresholding","module-detection","eigengenes","trait-correlation","hub-genes","module-membership","gene-significance","connectivity","cytoscape","metacells","single-cell"] |
| source | bioskills-coexpression-networks |
| status | active |
| enabled | true |
