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 predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R).
Install Sc In Silico Perturbation
$ npx skills add TianGzlab/OmicsClaw --skill sc-in-silico-perturbation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-in-silico-perturbation --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/singlecell/scrna/sc-in-silico-perturbation .claude/skills/sc-in-silico-perturbation && 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 "sc-in-silico-perturbation" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-in-silico-perturbation into .claude/skills/sc-in-silico-perturbation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-in-silico-perturbation", 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/singlecell/scrna/sc-in-silico-perturbationType 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 sc-in-silico-perturbation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-in-silico-perturbation --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/singlecell/scrna/sc-in-silico-perturbation .agents/skills/sc-in-silico-perturbation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sc-in-silico-perturbation" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-in-silico-perturbation into .agents/skills/sc-in-silico-perturbation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-in-silico-perturbation", 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 sc-in-silico-perturbation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-in-silico-perturbation --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/singlecell/scrna/sc-in-silico-perturbation .cursor/skills/sc-in-silico-perturbation && 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 "sc-in-silico-perturbation" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-in-silico-perturbation into .cursor/skills/sc-in-silico-perturbation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-in-silico-perturbation", 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/singlecell/scrna/sc-in-silico-perturbation--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 sc-in-silico-perturbation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-in-silico-perturbation --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/singlecell/scrna/sc-in-silico-perturbation .gemini/skills/sc-in-silico-perturbation && 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 "sc-in-silico-perturbation" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-in-silico-perturbation into .gemini/skills/sc-in-silico-perturbation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-in-silico-perturbation", 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 sc-in-silico-perturbationInstalls 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 sc-in-silico-perturbation -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/singlecell/scrna/sc-in-silico-perturbation .github/skills/sc-in-silico-perturbation && 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 "sc-in-silico-perturbation" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-in-silico-perturbation into .github/skills/sc-in-silico-perturbation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-in-silico-perturbation", 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 sc-in-silico-perturbation -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 sc-in-silico-perturbation --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/singlecell/scrna/sc-in-silico-perturbation .opencode/skills/sc-in-silico-perturbation && 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 "sc-in-silico-perturbation" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-in-silico-perturbation into .opencode/skills/sc-in-silico-perturbation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-in-silico-perturbation", 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
sc-in-silico-perturbation- GitHub stars
- 161
- Token cost
- ~1.3k tokens
- SKILL.md length
- 487 words
- Files
- 11 (incl. references)
- Skills in repo
- 88
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Load when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R).
- Tasks that involve Bioinformatics
- SKILL.md covers Method Selection Table, Key CLI, Workflow and Matrix Contract, plus 4 more sections
- Runs Python and R scripts from its folder; calls python
What it does
Sc In Silico Perturbation is an agent skill from TianGzlab/OmicsClaw. Load when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R). Skip when you have a real Perturb-seq / CRISPR screen (use sc-perturb); predicting drug sensitivity (use sc-drug-response).
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `references/methodology.md`).
It sits in Research & Science, covering Bioinformatics. It works with Python and AnnData. 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
Example prompts
- “/sc-in-silico-perturbation”
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 and R), 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
Sc In Silico Perturbation loads about 1.3k tokens when it runs, and up to ~1.9k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 487 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). 487 words, ~1,268 tokens.
Download SKILL.mdSave it as
.claude/skills/sc-in-silico-perturbation/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.- name
- sc-in-silico-perturbation
- description
- Load when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R). Skip when you have a real Perturb-seq / CRISPR screen (use sc-perturb); predicting drug sensitivity (use sc-drug-response).
- tags
- singlecell, scrna, in-silico-perturbation, knockout, grn, sctenifoldknk
sc-in-silico-perturbation
Explore descriptive gene associations or run the optional scTenifoldKnk R
method. The default grn_ko name is retained for CLI compatibility; its Pearson
edge-removal score is not a causal knockout simulation or a significance test.
Method Selection Table
| Method | What it returns | Requirement |
|---|---|---|
grn_ko | Absolute correlation edge-removal scores; no p-values | Python science stack |
sctenifoldknk | R scTenifoldKnk differential-regulation table | Rscript + scTenifoldKnk |
Key CLI
python skills/singlecell/scrna/sc-in-silico-perturbation/sc_in_silico_perturbation.py --demo --output /tmp/sc_iko_demo
python skills/singlecell/scrna/sc-in-silico-perturbation/sc_in_silico_perturbation.py --input expression.h5ad --ko-gene TP53 --n-top-genes 2000 --output results/associations
python skills/singlecell/scrna/sc-in-silico-perturbation/sc_in_silico_perturbation.py --input expression.h5ad --method sctenifoldknk --ko-gene TP53 --seed 0 --n-cores 1 --output results/tenifold--corr-threshold is an inactive legacy argument; it never thresholded the
correlation matrix. The API intentionally does not expose it.
Workflow
Preflight checks the target gene. grn_ko selects high-variance genes plus the
target, computes Pearson correlations, zeros the target row/column and averages
absolute changes by gene. The target's own score summarizes all its removed
edges; other genes lose one edge. R uses the existing temporary CSV bridge and
sets set.seed before fitting. No backend is silently substituted.
Matrix Contract
Reads raw_counts from layers['counts'] when present, otherwise X. Inputs are
unchanged by the API; returned table metadata records the chosen source.
CLI processed.h5ad carries omicsclaw_input_contract and
omicsclaw_matrix_contract. Use sc-perturb for an actual perturbation screen.
Inputs & Outputs
Input: expression .h5ad with --ko-gene in var_names.
The CLI writes processed.h5ad, tables/diff_regulation.csv, report.md,
result.json, figures/top_perturbed_genes.png, and figure/plot-data manifests.
The default table has gene, dr_score, wt_ko_corr; only perturbation_dr_score
is added to var. R additionally writes tables/tenifold_diff_regulation.csv
and may produce a p-value histogram and statistical var columns. R-enhanced
volcano plots require R statistical output; they are not made for correlation scores.
Gotchas
tables/diff_regulation.csvno longer contains fabricatedp_value,p.adj,z_scoreor anFCalias for the default method. Scores are sorted descending; they do not measure treatment effects.result.json→summary.n_significantexists only for R results with adjusted p-values. No significant-gene count is inferred from correlation scores.--ko-gene G10is a synthetic-demo default; the API raises when the chosen gene is absent.tables/tenifold_diff_regulation.csvis produced only if scTenifoldKnk succeeds. The R package is not installed by this skill.
Show full SKILL.md (188 more words)Show less
API
<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->knockout_correlation(adata, *, ko_gene, n_top_genes=2000)
Return descriptive edge-removal scores, not a causal knockout simulation.
Select the most variable genes plus ko_gene, compute Pearson correlations from layers['counts'] (or X), and average absolute changes after zeroing the target row and column. Returns gene, dr_score and wt_ko_corr, with no p-values. The target's own score includes all its removed edges and is not comparable to another gene's single removed edge. The input AnnData is unchanged.
sctenifoldknk(adata, *, ko_gene, qc=False, qc_min_lib_size=0, qc_min_cells=10, n_net=2, n_cells=100, n_comp=3, q=0.8, td_k=2, ma_dim=2, n_cores=1, random_state=0)
Return scTenifoldKnk diffRegulation through a temporary CSV bridge.
Uses layers['counts'] or X without changing the input. Requires Rscript and the scTenifoldKnk R package; missing packages and R failures propagate. random_state is passed to R set.seed before fitting the network.
run_info(table, *, keep: bool=True)
Return method and interpretation; keep=False removes the table's run record.
top_perturbed_genes(table, *, n_top=15)
Return top correlation scores, or lowest adjusted p-values for scTenifoldKnk.
perturbed_genes_figure(table, *, n_top=15)
Return a Figure of descriptive edge scores or R differential-regulation FC.
<!-- api:end -->Dependencies
anndata, matplotlib, numpy, pandas, scanpy, scipy
The R method additionally requires the scTenifoldKnk R package.
© 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 10 other files (references) in skills/singlecell/scrna/sc-in-silico-perturbation of TianGzlab/OmicsClaw.
- SKILL.md
- _api.py
- examples/example_step.py
- references/methodology.md
- references/output_contract.md
- references/parameters.md
- references/r_visualization.md
- rscripts/sc_sctenifoldknk.R
- sc_in_silico_perturbation.py
- tests/test_knockout_api.py
- tests/test_sc_in_silico_perturbation_methods.py
Open the folder on GitHubat commit 90a3bec
Compare with similar skills
Sc In Silico Perturbation 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 |
|---|---|---|---|---|---|---|
| Sc In Silico Perturbation this skillTianGzlab/OmicsClaw | 161 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| PyDESeq2 Differential Expressiondavila7/claude-code-templates | 33k | 11 repos | ~4k | Automated safety check: Pass | MIT | |
| Anndatadavila7/claude-code-templates | 33k | 11 repos | ~2.5k | Automated safety check: Pass | MIT | |
| ScanpyK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~5.1k | Automated safety check: Pass | BSD-3-Clause | |
| GenimlK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~4k | Automated safety check: Notes | MIT | |
| AnndataK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.9k | Automated safety check: Notes | BSD-3-Clause |
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Related
Categories
Questions about Sc In Silico Perturbation
What does Sc In Silico Perturbation do?
Load when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R). Sc In Silico Perturbation is an agent skill from TianGzlab/OmicsClaw. Load when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R).
When should I use Sc In Silico Perturbation?
Sc In Silico Perturbation fits situations like: tasks that involve Bioinformatics.
How do I install Sc In Silico Perturbation in Claude Code?
Run `npx skills add TianGzlab/OmicsClaw --skill sc-in-silico-perturbation -a claude-code`. Or copy the skill folder (skills/singlecell/scrna/sc-in-silico-perturbation in TianGzlab/OmicsClaw) into .claude/skills/sc-in-silico-perturbation in your project. Claude Code loads it when a task matches its description.
How do I install Sc In Silico Perturbation in Codex?
Run `npx skills add TianGzlab/OmicsClaw --skill sc-in-silico-perturbation -a codex`. Or copy the skill folder (skills/singlecell/scrna/sc-in-silico-perturbation in TianGzlab/OmicsClaw) into .agents/skills/sc-in-silico-perturbation in your project. Codex loads it when a task matches its description.
Can I use Sc In Silico Perturbation 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 sc-in-silico-perturbation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sc-in-silico-perturbation, .gemini/skills/sc-in-silico-perturbation, .github/skills/sc-in-silico-perturbation and .opencode/skills/sc-in-silico-perturbation in your project.
What does Sc In Silico Perturbation need to run?
Going by SKILL.md and its folder, Sc In Silico Perturbation needs Python and R for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Sc In Silico Perturbation 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 Sc In Silico Perturbation 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 Sc In Silico Perturbation use?
Sc In Silico Perturbation 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 Sc In Silico Perturbation use?
About 1.3k tokens (SKILL.md is roughly 5.1k 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 636 tokens, read only when the agent opens those files.
What are the alternatives to Sc In Silico Perturbation?
Skills that share tags, products or a category with Sc In Silico Perturbation: PyDESeq2 Differential Expression (davila7/claude-code-templates, 33k stars), Anndata (davila7/claude-code-templates, 33k stars), Scanpy (K-Dense-AI/scientific-agent-skills, 48k stars) and Geniml (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sc In Silico Perturbation?
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.