Chooses a bulk multi-omics integration strategy before any tool runs by mapping the biological question (subtype discovery, shared axis of variation, predictive signature, pairwise correlation) to a method class, naming the sample correspondence (paired-vertical, horizontal, mosaic, diagonal), enforcing the n<<p discipline that makes a held-out cohort the endpoint instead of in-cohort cross-validation, and running the per-view variance-imbalance diagnostic. Covers the early/mixed/intermediate/late taxonomy, why vertical and horizontal integration are different problems, and why a shared factor dominated by one omic is not integration. Use when deciding which integration method fits a question, whether data is paired or mosaic, supervised or unsupervised, or how to validate an integrated result. For unsupervised factors see mofa-integration; for supervised signatures see mixomics-analysis; for stratification see similarity-network; for single-cell see single-cell/multimodal-integration.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryGeneralStars0
About this skill
Chooses a bulk multi-omics integration strategy before any tool runs by mapping the biological question (subtype discovery, shared axis of variation, predictive signature, pairwise correlation) to a method class, naming the sample correspondence (paired-vertical, horizontal, mosaic, diagonal), enforcing the n<<p discipline that makes a held-out cohort the endpoint instead of in-cohort cross-validation, and running the per-view variance-imbalance diagnostic. Covers the early/mixed/intermediate/late taxonomy, why vertical and horizontal integration are different problems, and why a shared factor dominated by one omic is not integration. Use when deciding which integration method fits a question, whether data is paired or mosaic, supervised or unsupervised, or how to validate an integrated result. For unsupervised factors see mofa-integration; for supervised signatures see mixomics-analysis; for stratification see similarity-network; for single-cell see single-cell/multimodal-integration.
Quick start
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Chooses a bulk multi-omics integration strategy before any tool runs by mapping the biological question (subtype discovery, shared axis of variation, predictive signature, pairwise correlation) to a method class, naming the sample correspondence (paired-vertical, horizontal, mosaic, diagonal), enforcing the n<<p discipline that makes a held-out cohort the endpoint instead of in-cohort cross-validation, and running the per-view variance-imbalance diagnostic. Covers the early/mixed/intermediate/late taxonomy, why vertical and horizontal integration are different problems, and why a shared factor dominated by one omic is not integration. Use when deciding which integration method fits a question, whether data is paired or mosaic, supervised or unsupervised, or how to validate an integrated result. For unsupervised factors see mofa-integration; for supervised signatures see mixomics-analysis; for stratification see similarity-network; for single-cell see single-cell/multimodal-integration.
About this listing
Category
General
Reported license
Not specified
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI