dr-cookmethod-designer

name

dr-cook:method-designer

description

Guide researchers through experimental design decisions. Use when planning experimental design, study design, method design, CRISPR design, sgRNA design, network pharmacology design, multi-omics design, sample size calculation, power calculation, statistical power, cohort design, knockout design, knockin design, 实验设计, 样本量计算, 统计功效, CRISPR设计, 多组学, 网络药理学设计. Do NOT trigger for: bioinformatics-assistant (data analysis on existing data), data-visualizer (plotting only), paper-writer (Methods section writing only).

method-designer

1. Overview

method-designer guides researchers through experimental design decisions before data collection begins. Four tracks: CRISPR (sgRNA selection, delivery, validation, controls); network pharmacology (TCM compound-target-pathway design, ADME filtering, validation plan); multi-omics (platform selection, matched-sample design, integration strategy); sample size (power analysis for t-test, ANOVA, survival with dropout adjustments).

2. Parameters

Required

Parameter	Values	Description
design_type	crispr | network_pharmacology | multi_omics | sample_size	Which design track to run
research_question	string	1–3 sentence description of what the experiment aims to answer

Optional

Parameter	Values	Description
organism	human | mouse | rat | cell_line (default: human)	Species or model system for the experiment
domain	tcm | bioinformatics | clinical | pharmacology	Research domain; loaded from context_output if available
budget_constraint	low | medium | high	Affects method and reagent recommendations
timeline	string	Expected project duration (e.g., "6 months", "1 year")
existing_data	string	Description of data or resources already available (e.g., "mouse colony established", "RNA-seq data from prior study")

3. Workflow

Step 1 — Check upstream context_output. Inspect context_output.parameters for existing values. Inherit domain, parameters.organism, and parameters.analysis_type (from bioinformatics-assistant; maps to design_type context) if present. Also check context_output.summary (from literature-reviewer) for an implicit research_question. If inherited parameters cover required fields, skip re-asking for them. Do not ask the user for information already known from upstream modules.

Step 2 — Collect design_type and research_question. Ask for one parameter at a time. If design_type is not specified, present the menu:

"Which design track? (1) CRISPR / gene editing (2) Network pharmacology (3) Multi-omics integration (4) Sample size calculation"

Then collect research_question if not already stated or inferable from upstream context_output.summary.

Step 3 — Collect optional context. Ask in a single prompt for budget, timeline, and existing data. If the user declines or does not respond, proceed with standard-tier recommendations.

Step 4 — Load the relevant reference file. Select based on design_type:

• crispr → load references/crispr-design.md
• network_pharmacology → load references/network-pharmacology-workflow.md
• multi_omics → load references/multi-omics-integration.md
• sample_size → load references/sample-size-calculation.md

Apply all design rules, tables, and decision logic from the loaded reference throughout Steps 5–6.

Step 5 — Generate design plan. Produce a structured plan tailored to the track:

CRISPR track: Apply sgRNA selection criteria (on-target score via CRISPOR, off-target check, design 3–5 guides per target). Recommend delivery method from the table in crispr-design.md based on organism. Outline the validation workflow: Surveyor/T7E1 → Sanger → Western blot. List required controls: scramble sgRNA, positive control, parental baseline. If budget_constraint = low: prefer RNP transfection (no plasmid, no viral). If high: consider conditional knockin via Cas9-nickase or base editing.

Network pharmacology track: Map the research question to TCM formula → disease, single herb → disease, or compound class → phenotype. Recommend databases from the selection table. State ADME filters (OB ≥ 30%, DL ≥ 0.18). Outline the validation plan: docking → binding assay → cellular assay. List reporting requirements. If budget_constraint = low: limit wet-lab validation to cell-based assays; defer animal experiments.

Multi-omics track: Select platforms from the table in multi-omics-integration.md. State matched-sample requirements and minimum n ≥ 6. Recommend integration strategy (MOFA2 for early integration, DIABLO for supervised). Flag batch effect and missing data pitfalls. If budget_constraint = low: prioritize transcriptomics only; add proteomics in phase 2.

Sample size track: Identify study type. State α = 0.05 and β = 0.20 (80% power) defaults. Apply formula and R code from sample-size-calculation.md. Present n across at least three effect sizes. Apply dropout and multiple testing adjustments.

Step 6 — Present structured design plan. Format the output as follows:

• Header line: [Design: <TRACK> | Organism: <ORGANISM> | Budget: <TIER>]
• Numbered workflow steps with phase headings (Phase 1: ..., Phase 2: ...)
• Decision points marked with ⚠️ and a brief explanation of what depends on the outcome
• Estimated timeline per phase if timeline was provided
• Budget-tier recommendations in a separate callout if budget_constraint was provided
• Materials or reagents list where applicable (CRISPR, multi-omics)

Step 7 — Offer iteration and downstream modules. End with: "Would you like me to expand any section, adjust for a different budget or timeline, or help write the Methods section for this design?" Then add a **Next steps** block with context-sensitive routing: CRISPR → bioinformatics-assistant (DEG on knockout vs. wildtype RNA-seq); network pharmacology → bioinformatics-assistant (for computational pipeline) OR paper-writer (if the user's focus is writing the Methods section); multi-omics → bioinformatics-assistant; sample size → paper-writer (to draft the Statistical Analysis subsection). If user mentions "write methods" or "Methods section," always suggest paper-writer.

4. Output Format

Begin every response with the design header:

[Design: CRISPR | Organism: Mouse | Budget: Medium]

The body is a numbered, phase-structured plan with bold phase headings (**Phase 1: ...**). Decision points use the ⚠️ marker with a brief conditional (e.g., "⚠️ If editing efficiency < 20%, switch to RNP electroporation"). Code blocks use language-tagged fences (```r). Materials and reagents appear as a bulleted list under **Materials**. A **Next steps** block at the end names specific Dr. Cook modules.

5. context_output

Reads from upstream

Field	Source	Usage
parameters.domain	any upstream module	Avoids re-asking for domain
parameters.organism	any upstream module	Avoids re-asking for organism
parameters.analysis_type	bioinformatics-assistant	Maps to design_type if present
summary	literature-reviewer, research-gap-finder	May serve as research_question source

Writes to output

{
  "module": "method-designer",
  "summary": "<e.g., 'CRISPR knockout design for mouse Tp53, medium budget, 6-month timeline'>",
  "raw_text": "<the full design plan as produced in Step 6>",
  "parameters": {
    "design_type": "<crispr | network_pharmacology | multi_omics | sample_size>",
    "organism": "<human | mouse | rat | cell_line>",
    "budget_constraint": "<low | medium | high | null>",
    "timeline": "<string | null>",
    "existing_data": "<string | null>"
  },
  "status": "success | partial | failed",
  "error_message": "<string | null>"
}

status = partial when research_question is vague or design_type could not be determined after one clarification attempt. status = failed if a required parameter could not be collected after two attempts. raw_text is populated with the full structured design plan once Step 6 is complete.

6. References

references/: crispr-design.md, network-pharmacology-workflow.md, multi-omics-integration.md, sample-size-calculation.md.