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Differential Gene Expression Analysis (RNA-seq)
by
kenthompson2088
· GitHub ↗
· v1.0.1
· MIT-0
219
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0
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Install in OpenClaw
/install differential-gene-analysis
Description
Performs differential gene expression analysis on RNA-seq count data using DESeq2 and outputs significant genes, volcano plot, PCA, and heatmap.
README (SKILL.md)
Differential Gene Expression Analysis (RNA-seq)\r
OpenCLAW Skill for bioinformatics data analysis.\r \r
License\r
MIT-0\r \r
Description\r
This skill performs differential gene analysis using DESeq2 with simulated expression data.\r \r
Input\r
input/count_matrix.csv\r \r
Output\r
- output/volcano.png\r
- output/pca.png\r
- output/heatmap.png\r
- output/diff_genes_significant.csv\r \r
Code\r
# ==============================\r
# OpenCLAW Skill Run Code\r
# ==============================\r
\r
if (!require("BiocManager", quietly = TRUE)) {\r
install.packages("BiocManager", repos = "https://cloud.r-project.org/")\r
}\r
\r
BiocManager::install(c("DESeq2", "ggplot2", "pheatmap"), update = FALSE, ask = FALSE)\r
\r
library(DESeq2)\r
library(ggplot2)\r
library(pheatmap)\r
\r
if (!dir.exists("output")) dir.create("output")\r
\r
# Read input\r
count_df \x3C- read.csv("input/count_matrix.csv", row.names = 1)\r
count_matrix \x3C- as.matrix(count_df)\r
group \x3C- factor(c("Control","Control","Control","Treat","Treat","Treat"))\r
colData \x3C- data.frame(group = group)\r
\r
# DESeq2\r
dds \x3C- DESeqDataSetFromMatrix(round(count_matrix), colData, ~ group)\r
dds \x3C- dds[rowSums(counts(dds)) > 3, ]\r
dds \x3C- DESeq(dds)\r
res \x3C- results(dds, contrast = c("group", "Treat", "Control"))\r
res_sig \x3C- subset(res, padj \x3C 0.05 & abs(log2FoldChange) > 1)\r
write.csv(as.data.frame(res_sig), "output/diff_genes_significant.csv")\r
\r
# Volcano\r
res_df \x3C- as.data.frame(res)\r
res_df$sig \x3C- ifelse(res_df$padj \x3C 0.05 & abs(res_df$log2FoldChange) > 1, "Sig", "NS")\r
p \x3C- ggplot(res_df, aes(log2FoldChange, -log10(padj))) + geom_point(aes(color=sig)) + theme_bw()\r
ggsave("output/volcano.png", p, dpi=300)\r
\r
# PCA\r
vsd \x3C- vst(dds, blind=FALSE)\r
p_pca \x3C- plotPCA(vsd, intgroup="group") + theme_bw()\r
ggsave("output/pca.png", p_pca)\r
\r
# Heatmap\r
if(nrow(res_sig) > 0) {\r
top \x3C- head(rownames(res_sig), 10)\r
mat \x3C- t(scale(t(assay(vsd)[top,])))\r
png("output/heatmap.png", width=800, height=600)\r
pheatmap(mat, annotation_col=data.frame(group))\r
dev.off()\r
}\r
\r
cat("✅ Skill run successfully\
")
Usage Guidance
This skill appears to do exactly what it says: it runs R/DESeq2 on an input count matrix and writes plots and a CSV. Before running, ensure: (1) you provide a correctly formatted input/count_matrix.csv (the code expects six samples arranged as 3 Control then 3 Treat), (2) you run it in a sandbox or environment where installing R/Bioconductor packages is safe (installing packages downloads and executes package installation code), and (3) you accept that the skill will install packages at runtime (network access and write permissions required). Also note the packaging mismatch (skill.json references main.R which is not present); consider asking the publisher for a proper entry file or confirm that executing the code embedded in SKILL.md is the intended workflow.
Capability Analysis
Type: OpenClaw Skill
Name: differential-gene-analysis
Version: 1.0.1
The skill performs standard bioinformatics differential gene expression analysis using the DESeq2 R package. The code in skill.md follows typical data science workflows, including installing necessary dependencies from official repositories (CRAN/Bioconductor), processing local CSV input, and generating visualization plots (Volcano, PCA, Heatmap). No evidence of data exfiltration, malicious execution, or prompt injection was found.
Capability Assessment
Purpose & Capability
Name/description (DE analysis with DESeq2) matches the actions in SKILL.md: installing DESeq2, plotting, and writing result files. Required resources (R packages) are appropriate. Minor inconsistency: skill.json claims an entryFile main.R and runtime R-4.2, but no main.R is present and the runnable R code lives in SKILL.md; this is likely bookkeeping/packaging sloppiness rather than suspicious behavior.
Instruction Scope
The SKILL.md contains full R code that installs BiocManager and Bioconductor/CRAN packages, reads input/count_matrix.csv, performs DESeq2 analysis with a hard-coded group vector (3 Control, 3 Treat), and writes output images/CSV. Instructions do not read unrelated files or environment variables and do not transmit data externally. Note: installing R packages requires network access to CRAN/Bioconductor and will fetch and run package installation scripts — normal but worth noting.
Install Mechanism
No platform install spec; runtime R code calls install.packages() and BiocManager::install() to pull DESeq2, ggplot2, pheatmap from standard repositories (CRAN/Bioconductor). These are standard/public package sources, not arbitrary URLs or archive downloads.
Credentials
The skill requests no environment variables, no credentials, and accesses only the declared input path input/count_matrix.csv and writes to output/. The requested resources are proportional to the stated purpose.
Persistence & Privilege
always is false and the skill does not request persistent system-wide privileges or modify other skills/config. It runs ad-hoc R code at runtime only.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install differential-gene-analysis - After installation, invoke the skill by name or use
/differential-gene-analysis - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Adds initial support for differential gene expression analysis using DESeq2 on RNA-seq count data.
- Generates key output files: volcano plot, PCA plot, heatmap of significant genes, and CSV with significant differential genes.
- Uses simulated group assignments (Control vs Treat) for analysis.
- Outputs visualizations and results to an output directory.
Metadata
Frequently Asked Questions
What is Differential Gene Expression Analysis (RNA-seq)?
Performs differential gene expression analysis on RNA-seq count data using DESeq2 and outputs significant genes, volcano plot, PCA, and heatmap. It is an AI Agent Skill for Claude Code / OpenClaw, with 219 downloads so far.
How do I install Differential Gene Expression Analysis (RNA-seq)?
Run "/install differential-gene-analysis" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Differential Gene Expression Analysis (RNA-seq) free?
Yes, Differential Gene Expression Analysis (RNA-seq) is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Differential Gene Expression Analysis (RNA-seq) support?
Differential Gene Expression Analysis (RNA-seq) is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Differential Gene Expression Analysis (RNA-seq)?
It is built and maintained by kenthompson2088 (@kenthompson2088); the current version is v1.0.1.
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