X Openai Ngs Bulk Rnaseq Differential Expression 12c68f9

---
name: x-openai-ngs-bulk-rnaseq-differential-expression-12c68f9
description: Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks.
---

### 导出副本边界与技能别名

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跨技能名称可能沿用源库别名。先查 [技能别名表](../../skill-aliases.json)，优先选择同来源、同插件的条目，再使用其 `id` 调用或读取对应入口；不得假定旧 `$skill`、`superpowers:skill` 名称在新宿主直接注册。不能确定目标时先核对，不替换真实产品命令或 Shell 环境变量。

## 跨宿主使用约定

本副本供支持 Agent Skills 的客户端使用。先检查当前宿主实际提供的工具、权限和已连接服务，再执行正文工作流。原文中的宿主工具标识是能力参考；只有实际发现该工具时才可调用，其他宿主应使用经核验的等价 MCP、CLI 或 API。没有等价能力的步骤必须报告不可用，不能虚构调用或完成状态。

技能资源以本技能目录为基准；输出写入当前项目中用户指定的位置。通过技能名称或自然语言触发，具体快捷命令由宿主决定。涉及真实产品、模型 API、协议字段和官方网址的名称保持原意。授权、审批、隐私和工具范围限制在迁移后仍然有效；不得为兼容而扩大权限。具体依赖与迁移说明见同目录 `PORTABILITY.md`。

# Bulk RNA-seq Differential Expression

Use this skill when the user has raw counts or a count-generation output and wants differential expression, contrasts, QC plots, or ranked gene tables.

## Essential Inputs

Confirm:

- raw count matrix path and sample metadata path
- gene ID type and annotation mapping requirement
- biological conditions, replicates, batch variables, donor pairing, covariates, and exclusions
- exact contrasts and baseline levels
- preferred statistical framework: DESeq2, edgeR, limma-voom, or existing lab standard
- output needs: normalized counts, PCA, sample distance, volcano plots, heatmaps, ranked tables, GSEA-ready lists

## Preconditions

Do not start differential expression until:

- raw counts are preserved
- each requested contrast has enough biological replication
- sample metadata row names match count matrix columns
- batch/covariate choices are explicit
- exploratory PCA/sample-distance plots do not reveal obvious swaps or failed libraries

## Route

For most count matrices, use DESeq2 or edgeR. Use limma-voom when the study design or lab standard favors it. Keep the analysis in R when using Bioconductor unless the user specifically asks for a Python-only workflow.

The plugin-owned local runner is:

```bash
python plugins/ngs-analysis/scripts/run_bulk_rnaseq_de.py \
  --count-matrix count_matrix.tsv \
  --sample-metadata sample_metadata.tsv \
  --contrasts contrasts.tsv \
  --execute
```

Use `--method auto` unless the user or lab standard specifies `DESeq2`, `edgeR`, or `limma_log2`. Auto mode uses DESeq2 when integer-like counts and the package are available, falls back to edgeR for integer-like counts, and uses `limma_log2` for non-integer expression matrices.

Use `--input-mode` to declare whether the matrix is `raw_counts`, `normalized_expression`, or `log_expression`. When `--input-mode auto` is used, the runner infers the mode and records a warning if normalization is skipped because the matrix is already transformed.

Preflight command:

```bash
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_differential_expression --emit-install-plan
```

## Decision Points

- Never compare groups without stating the design formula and contrast.
- Treat batch correction in modeling separately from visual batch removal.
- Do not filter genes using post-hoc knowledge of the contrast.
- For paired or repeated-measures designs, model subject/donor explicitly.
- Report genes with effect size, uncertainty, adjusted p-value, and filtering status.

## Outputs

Produce:

- design formula and contrast manifest
- QC plots: library size, detected genes, PCA/sample distance, mean-variance trend, and outlier review
- input-mode-aware matrix exports plus the modeling/log-scale matrix used for DE
- differential-expression tables per contrast
- explicit `.not_tested.tsv` stubs for contrasts blocked by insufficient replication or confounding
- auto-launched localhost Marimo review app recorded in `notebooks/marimo_server.json`
- caveats for small n, confounded designs, failed samples, or batch variables that cannot be estimated
- standard run envelope: `run_manifest.json`, `config.json`, `validation/`, `logs/`, `versions/`, `visualizations/`, `notebooks/`, `artifact_index.json`, and `summary.md`