awesome-multi-omics
mikelove/awesome-multi-omics
A curated reference list of software packages and academic literature dedicated to multi-omics data analysis and multi-table statistical methods.
940 196MITUpdated 2026-08-04
73
GoodOverview
This resource provides researchers and bioinformaticians with a comprehensive directory of tools for multi-modal data integration. It covers various statistical and machine learning methodologies, including canonical correlation analysis, factor analysis, partial least squares, and matrix factorization tailored for multi-table datasets.
Capabilities
- ▸Listing multi-omics correlation and factor analysis packages
- ▸Providing references to chemometrics and ecology multi-table literature
- ▸Linking to original peer-reviewed papers and source code repositories
Best for
Discovering R packages and Python libraries for integrating multi-omics datasets, Finding academic papers and software implementations for multi-way data analysis, Selecting appropriate statistical methods for analyzing samples measured across multiple assays
Works with
Claude CodeCodexChatGPTGemini CLICursorGitHub CopilotOpenCode
Evoa Score breakdown= Σ (score × weight)
73Task usefulness20%65 → +13.0
実タスクにどれだけ役立つか（機能の豊富さ・用途の明確さ）。 — AIによるcapabilities/use-cases解析
Code quality15%70 → +10.5
実装・指示の品質。 — AIによるSKILL.md/README解析
Maintenance15%100 → +15.0
リポジトリがどれだけ活発に保守されているか。 — GitHub 最終push日時の新しさ
Documentation12%30 → +3.6
ドキュメントの充実度・分かりやすさ。 — README/独自要約の情報量
Security15%100 → +15.0
危険・不審な挙動が無いか。 — AIによるセキュリティレビュー
Originality10%45 → +4.5
ありふれたラッパーではない独自性。 — AIによる独自性判定
Popularity8%78 → +6.2
コミュニティの採用度。 — GitHub Stars/Forks（対数スケール）
Compatibility5%100 → +5.0
対応AIエージェントの広さ。 — AIによる対応エージェント判定
Weighted total72.8 / 100
ライセンス不明/制限あり（Red）のSkillは総合スコアに0.85倍の補正を適用します。 ランキングはこのScoreのみで決まり、広告で変わりません。 算出方法の詳細 →
Security considerations
The skill provides only informational references and software repository links without executing any external code.
Categories
Summary and analysis are original content generated by AI Skills Rank. The skill's source text is not reproduced here — view it on the linked repository.