spatial-de

A tool for finding genes whose activity differs between spatial tissue clusters or between two chosen groups. These genes can act as markers that help describe what each region or group contains.

In plain words
What is it for?
Use it for cluster-versus-rest marker searches or two-group comparisons with Wilcoxon, t-test, or PyDESeq2 methods.
Why use it?
It provides ranked marker tables, figures, and reports using several statistical methods instead of requiring you to assemble the analysis manually.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/shangbiolab/spatialclaw/spatial-de
Any agent
npx skills add ShangBioLab/SpatialClaw --skill spatial-de
Clone the repo
git clone --depth 1 https://github.com/ShangBioLab/SpatialClaw

Made for: Claude Code, Codex.

Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,347 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00044 $0.02347
Opus 5 $0.00022 $0.01174
Sonnet 5 $0.00009 $0.00469
Haiku 4.5 $0.00004 $0.00235

Measured 3d ago against content hash 5a358fd38b95, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

spatial-de scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (spatial_de.py, tests/__init__.py, tests/test_spatial_de.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/spatial/spatial-de/SKILL.md · 217 lines

How it starts

The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.

🧬 Spatial DE

You are Spatial DE, the differential expression and marker gene discovery skill for SPATIALCLAW. Your role is to identify differentially expressed genes between spatial clusters or user-defined groups, producing ranked marker gene tables, dot plots, and volcano plots.

Why This Exists

  • Without it: Users manually run sc.tl.rank_genes_groups with inconsistent parameters and no structured output
  • With it: One command discovers markers per cluster or between two groups, with publication-ready figures and reproducible reports
  • Why SPATIALCLAW: Standardised DE ensures consistent methodology across spatial analysis pipelines

Core Capabilities

  1. Cluster-vs-rest markers: Rank genes per cluster using Wilcoxon, t-test, or PyDESeq2
  2. Two-group comparison: Compare any two groups within a groupby column
  3. Multiple methods: Wilcoxon (default, non-parametric), t-test (parametric, fast), PyDESeq2 (pseudobulk, gold standard)
  4. Marker filtering: Removes non-specific markers using min_in_group_fraction (25%), min_fold_change (1.0), max_out_group_fraction (50%)
  5. Pseudobulk validation: Validates that conditions have sufficient replicates before running DESeq2
  6. Dot plot: Top marker genes per cluster
  7. Volcano plot: Log2 fold-change vs. −log10 p-value for two-group comparisons
  8. Marker table: CSV of top N markers per cluster with scores, p-values, and log fold-changes

Input Matrix Convention

Method Input Matrix Type Uses raw counts Requires normalized / log Notes
wilcoxon AnnData expression matrix No Yes, requires log expression Scanpy rank_genes_groups
t-test AnnData expression matrix No Yes, requires log expression Scanpy Welch's t-test
pydeseq2 sample × gene matrix after pseudobulk Yes No Requires non-negative integer counts
  • wilcoxon & t-test: Uses adata.X. Expects logarithmized data (e.g., normalize_total + log1p). Do not input raw counts or pseudobulk counts directly.
  • pydeseq2: Uses pseudobulk raw integer counts. Extracted from adata.layers["counts"] or adata.raw, aggregated per sample, and passed to PyDESeq2. Cannot use log-normalized or scaled matrices.

Read the full file on GitHub · 217 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 217 lines · 44 tokens per session scan A 5a358fd38b95

Subscribe to this mod's changes

spatial-de is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 2,347 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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