Bio Analysis System

bio-analysis-system

Step 5: Analysis system design (分析方法体系构建)

Build the analysis layer for the manuscript by identifying which analyses, tools, and biological validations should support each figure and each task.

Purpose

1. Extract analysis patterns from related work
2. Borrow useful analyses from adjacent domains when needed
3. Map analyses to BioClaw-compatible tools or fallback software
4. Explain why each analysis is included and what biological claim it supports
5. Connect analyses to figure panels

Input Format

topic: [research topic]
paper_count: [number of related papers]
task_system: [task system]
metric_system: [metric system]
dataset_catalog: [dataset catalog]

Workflow

Step 5.1: Extract analyses from existing work

If enough related papers exist, inspect their figures and extract:

• panel type
• analysis method
• software / package
• important parameters
• the scientific or biological conclusion the panel supports

Step 5.2: Borrow from adjacent fields

If the field is still thin, adapt common analyses from nearby areas such as:

• clustering
• marker visualization
• latent embedding visualization
• pathway enrichment
• cell-cell communication
• spatial statistics
• GRN analysis

Step 5.3: Categorize analyses

Use three broad groups:

• Quantitative analyses
• clustering
• metric computation
• statistical tests
• baseline comparisons
• Qualitative analyses
• spatial visualization
• feature / violin plots
• UMAP / t-SNE
• before / after alignment comparisons
• heatmaps
• Biological analyses
• cell annotation
• marker genes
• pathway enrichment
• GRN
• ligand-receptor communication
• spatial statistics
• trajectory analysis

Step 5.4: Map to BioClaw or fallback tools

Whenever possible, map analysis needs to BioClaw-compatible skills or established tools.

Examples:

• clustering -> Scanpy / Leiden
• annotation -> CellTypist / SingleR
• marker plots -> Scanpy
• enrichment -> gseapy
• spatial statistics -> squidpy
• GRN -> pySCENIC
• communication -> CellChat-like workflow

Step 5.5: Standardize analysis descriptions

For each analysis, define:

• category
• purpose
• biological claim supported
• preferred tool
• fallback tool
• key function
• recommended parameters
• inputs / outputs
• mapped task
• mapped figure / panel

Output Format

# Analysis System

## Analysis Sources
- Extracted from related papers:
- Borrowed from adjacent domains:

## Quantitative Analyses

### Clustering
- Category:
- Purpose:
- Biological claim supported:
- Preferred tool:
- Fallback tool:
- Key function:
- Recommended parameters:
- Inputs / outputs:
- Relevant tasks:
- Figure mapping:

### Metric computation
- Category:
- Purpose:
- Preferred tools:
- Relevant tasks:
- Figure mapping:

## Qualitative Analyses
- spatial plot
- marker / feature plot
- latent embedding plot
- heatmap
- before / after alignment visualization

## Biological Analyses
- annotation
- marker recovery
- pathway enrichment
- GRN
- communication
- trajectory

## Next Step
- Use the analysis system to design figures in Step 6

Usage

/bio-analysis-system "spatial multi-omics integration | paper_count: 5 | task_system: [...] | metric_system: [...] | dataset_catalog: [...]"

Notes

1. Prefer analyses that directly support paper claims.
2. Make the biological readouts visible early; they should not appear only at the very end.
3. Map each major analysis to a concrete figure panel.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: cheatthegod
• Source: cheatthegod/BioHermes
• License: MIT

Install and usage instructions live in the source repository linked above.