PyDESeq2 Differential Expression
davila7/claude-code-templates
Runs differential gene expression analysis on bulk RNA-seq counts with PyDESeq2: design formulas, Wald tests, FDR correction and volcano or MA plots.
Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein ×…
Install Proteomics De
$ npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TianGzlab/OmicsClaw proteomics-de --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/proteomics/proteomics-de .claude/skills/proteomics-de && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "proteomics-de" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-de into .claude/skills/proteomics-de/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-de", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-deType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TianGzlab/OmicsClaw proteomics-de --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/proteomics/proteomics-de .agents/skills/proteomics-de && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "proteomics-de" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-de into .agents/skills/proteomics-de/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-de", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TianGzlab/OmicsClaw proteomics-de --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/proteomics/proteomics-de .cursor/skills/proteomics-de && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "proteomics-de" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-de into .cursor/skills/proteomics-de/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-de", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/TianGzlab/OmicsClaw.git --path skills/proteomics/proteomics-de--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TianGzlab/OmicsClaw proteomics-de --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/proteomics/proteomics-de .gemini/skills/proteomics-de && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "proteomics-de" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-de into .gemini/skills/proteomics-de/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-de", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install TianGzlab/OmicsClaw proteomics-deInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/proteomics/proteomics-de .github/skills/proteomics-de && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "proteomics-de" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-de into .github/skills/proteomics-de/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-de", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install TianGzlab/OmicsClaw proteomics-de --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/proteomics/proteomics-de .opencode/skills/proteomics-de && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "proteomics-de" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-de into .opencode/skills/proteomics-de/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-de", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
proteomics-de- GitHub stars
- 161
- Token cost
- ~1.2k tokens
- SKILL.md length
- 469 words
- Files
- 8 (incl. references)
- Skills in repo
- 88
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein ×…
- Tasks that involve Bioinformatics
- SKILL.md covers When to use, Use from a step, API and Methods and parameters, plus 5 more sections
- Runs Python scripts from its folder; calls python
- Tasks that involve Statistics
What it does
Proteomics De is an agent skill from TianGzlab/OmicsClaw. Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually); label-based TMT linear-mixed models.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `proteomics_de.py`).
It sits in Research & Science, covering Bioinformatics and Statistics. The repository describes itself as: Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Bioinformatics
- Tasks that involve Statistics
Example prompts
- “/proteomics-de”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 90a3bec. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Proteomics De loads about 1.2k tokens when it runs, and up to ~1.4k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 469 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from TianGzlab/OmicsClaw at commit 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 469 words, ~1,195 tokens.
Download SKILL.mdSave it as
.claude/skills/proteomics-de/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- proteomics-de
- description
- Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually); label-based TMT linear-mixed models.
- trigger
- differential abundance, protein expression, MSstats, limma, volcano
- tags
- proteomics, differential-expression, ttest, welch, mann-whitney, bh-fdr
proteomics-de
When to use
ttest is the CLI default; welch and mann_whitney are alternatives. Groups default to the first and second half of columns. Use existing search-engine tables; this skill does not search raw spectra.
Use from a step
from skills._sdk.notebook import load_skill, write_output
library = load_skill('proteomics-de')
data = library.demo_data(random_state=42)
result = library.differential_abundance(data)
write_output(result, 'tables/differential_abundance.csv')For real data, use read_input and pass any read_table helper as reader=.
The executable examples/example_step.py also checks the result and writes a Figure.
API
<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->differential_abundance(data: pd.DataFrame, *, group1: list | None=None, group2: list | None=None, method: str='ttest') -> pd.DataFrame
Compare groups and return a new table with group2-minus-group1 log2 fold changes.
:param data: Protein-indexed, sample-column linear intensities; nonpositive values are missing. :param group1: First group columns; None uses the first half as in the CLI. :param group2: Second group columns; None uses the second half as in the CLI. :param method: CLI default ttest; welch uses unequal variance and mann_whitney tests ranks. :returns: Per-protein statistics and BH-adjusted p values. :raises ValueError: Groups overlap, are empty, or the protein index is not unique.
significant(results: pd.DataFrame, *, alpha: float=0.05, log2fc_threshold: float=0.0) -> pd.DataFrame
Select significant proteins from an existing comparison.
:param results: Differential abundance results with padj and log2fc. :param alpha: CLI default 0.05; strict upper bound on BH-adjusted p values. :param log2fc_threshold: CLI default 0 disables the absolute fold-change filter. :returns: A new table retaining selected rows. :raises ValueError: Thresholds are outside their allowed ranges.
run_info(table: pd.DataFrame, *, keep: bool=True) -> dict
Read comparison diagnostics.
:param table: Output of differential_abundance. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate diagnostic dictionary. :raises TypeError: The input is not a DataFrame.
volcano_figure(results: pd.DataFrame)
Plot log2 fold change against adjusted significance.
:param results: Differential abundance results. :returns: A matplotlib Figure without writing files. :raises KeyError: log2fc or padj is absent.
Show full SKILL.md (191 more words)Show less
demo_data(*, random_state: int=42) -> pd.DataFrame
Generate synthetic intensities with two ordered groups.
:param random_state: CLI seed 42; change for another simulation. :returns: Protein rows and five control then five treatment columns. :raises ValueError: The seed is invalid.
<!-- api:end -->Methods and parameters
ttest is the CLI default; welch and mann_whitney are alternatives. Groups default to the first and second half of columns.
Functions return new DataFrames. run_info(result) reads diagnostic attrs;
use keep=False before serialization when those attrs are not needed.
Gotchas
- differential_abundance reports group2 minus group1. Nonpositive intensities do not enter the tests. significant uses BH-adjusted p values.
demo_datauses seed 42, matching the CLI; every demo is synthetic.run_infolives in DataFrame attrs and is not preserved by CSV serialization.
Inputs and outputs
The CLI reads CSV tables and writes:
- tables/differential_abundance.csv
- tables/significant.csv
- report.md
- result.json
reproducibility/commands.shrecords the CLI invocation template.
Functions return data and Figures without writing files. Steps own their outputs. Demo mode also writes its synthetic input when the original CLI used a file.
CLI
python skills/proteomics/proteomics-de/proteomics_de.py --demo --output /tmp/proteomics_deFor real input replace --demo with --input <table>.
See also
references/methodology.mdreferences/parameters.mdreferences/output_contract.mdproteomics-data-importfor protein-table normalization;proteomics-defor comparisons.
Dependencies
numpy, pandas, matplotlib, scipy
© TianGzlab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 7 other files (references) in skills/proteomics/proteomics-de of TianGzlab/OmicsClaw.
- SKILL.md
- _api.py
- examples/example_step.py
- proteomics_de.py
- references/methodology.md
- references/output_contract.md
- references/parameters.md
- tests/test_api.py
Open the folder on GitHubat commit 90a3bec
Compare with similar skills
Proteomics De next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Proteomics De this skillTianGzlab/OmicsClaw | 161 | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| PyDESeq2 Differential Expressiondavila7/claude-code-templates | 32k | 12 repos | ~4k | Automated safety check: Pass | MIT | |
| Ukb Ppp Region FetchClawBio/ClawBio | 1.2k | — | ~4.6k | Automated safety check: Pass | MIT | |
| Volcano Plot Scriptaipoch/medical-research-skills | 2k | — | ~2.5k | Automated safety check: Pass | MIT | |
| Tooluniverse Epigenomicswu-yc/LabClaw | 1.1k | 2 repos | ~14k | Automated safety check: Pass | None | |
| Tooluniverse Metabolomics Analysiswu-yc/LabClaw | 1.1k | 2 repos | ~5.9k | Automated safety check: Pass | None |
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Related
Categories
Questions about Proteomics De
What does Proteomics De do?
Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein ×…. Proteomics De is an agent skill from TianGzlab/OmicsClaw. Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV.
When should I use Proteomics De?
Proteomics De fits situations like: tasks that involve Bioinformatics; tasks that involve Statistics.
How do I install Proteomics De in Claude Code?
Run `npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a claude-code`. Or copy the skill folder (skills/proteomics/proteomics-de in TianGzlab/OmicsClaw) into .claude/skills/proteomics-de in your project. Claude Code loads it when a task matches its description.
How do I install Proteomics De in Codex?
Run `npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a codex`. Or copy the skill folder (skills/proteomics/proteomics-de in TianGzlab/OmicsClaw) into .agents/skills/proteomics-de in your project. Codex loads it when a task matches its description.
Can I use Proteomics De in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TianGzlab/OmicsClaw --skill proteomics-de -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/proteomics-de, .gemini/skills/proteomics-de, .github/skills/proteomics-de and .opencode/skills/proteomics-de in your project.
What does Proteomics De need to run?
Going by SKILL.md and its folder, Proteomics De needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Proteomics De access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Proteomics De safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Proteomics De use?
Proteomics De is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Proteomics De use?
About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 223 tokens, read only when the agent opens those files.
What are the alternatives to Proteomics De?
Skills that share tags, products or a category with Proteomics De: PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars), Ukb Ppp Region Fetch (ClawBio/ClawBio, 1.2k stars), Volcano Plot Script (aipoch/medical-research-skills, 2k stars) and Tooluniverse Epigenomics (wu-yc/LabClaw, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Proteomics De?
TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on October 7, 2026.
Source: TianGzlab/OmicsClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.