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Differential Gene Expression Analysis (RNA-seq)
by
kenthompson2088
· GitHub ↗
· v1.0.2
· MIT-0
156
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Install in OpenClaw
/install differential-gene-analysis-rna-seq
Description
Performs differential gene expression analysis on RNA-seq count data using DESeq2, generating significant gene lists and visualizations.
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 what it says (DESeq2 analysis) but has several practical inconsistencies you should address before running: (1) The manifest (skill.json) references main.R and an R runtime while the bundle only contains SKILL.md with embedded R — update the manifest or provide the entry file so execution is deterministic. (2) input/count_matrix.csv in the package is empty; provide a properly formatted count matrix (rows=genes, columns=samples) that matches the hard-coded group factor (3 Control / 3 Treat) or modify the code to accept arbitrary sample groups. (3) The skill installs R/Bioconductor packages at runtime; that requires network access and will execute code downloaded from CRAN/Bioconductor — run in a sandboxed environment or pre-install/verify packages if you have security concerns. (4) If you need provenance, request the author/source (homepage) because the skill's source is unknown. If you plan to run this in a production environment, verify the input format, update the metadata, and consider pinning package versions to reduce variability.
Capability Analysis
Type: OpenClaw Skill
Name: differential-gene-analysis-rna-seq
Version: 1.0.2
The skill performs standard bioinformatics differential gene expression analysis using the DESeq2 R package. The code in skill.md reads a local input file (count_matrix.csv), processes it using well-known libraries (DESeq2, ggplot2, pheatmap), and saves the resulting plots and data to an output directory. There are no indicators of data exfiltration, malicious command execution, or prompt injection.
Capability Assessment
Purpose & Capability
The R code in SKILL.md implements differential gene expression using DESeq2, PCA, volcano, and heatmap which aligns with the skill's description. However, skill.json claims an entryFile (main.R) and runtime (R-4.2) while no such file exists in the bundle and the registry metadata earlier listed no required binaries—this metadata mismatch is inconsistent and may cause runtime confusion.
Instruction Scope
The instructions are narrowly scoped to reading input/count_matrix.csv, running DESeq2-based analysis, creating plots, and writing outputs to output/. The code does not read unrelated system files, environment variables, or call external endpoints except to install R packages. The analysis uses a hard-coded sample grouping (3 Control / 3 Treat), which may not match user data.
Install Mechanism
The SKILL.md performs runtime package installation via install.packages('BiocManager', ...) and BiocManager::install(...). This installs from CRAN/Bioconductor (cloud.r-project.org and Bioconductor), which is a standard source for R packages but implies network access and the ability to execute newly downloaded code at runtime. There is no separate install spec in the manifest to declare or prepare these dependencies.
Credentials
The skill requests no environment variables, no credentials, and no config paths. The lack of requested secrets is appropriate for its purpose.
Persistence & Privilege
The skill is not always-enabled and uses normal, user-invocable settings. It does not request persistent agent privileges or modify other skills' configurations.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install differential-gene-analysis-rna-seq - After installation, invoke the skill by name or use
/differential-gene-analysis-rna-seq - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
- Removed unnecessary file: plain.txt.
- No changes to input, output, or core functionality.
v1.0.0
Initial release of the Differential Gene Analysis Skill for RNA-seq data.
- Reads gene expression count matrices from CSV files.
- Performs differential expression analysis using DESeq2.
- Generates volcano, PCA, and heatmap plots.
- Outputs a list of significant differentially expressed genes.
Metadata
Frequently Asked Questions
What is Differential Gene Expression Analysis (RNA-seq)?
Performs differential gene expression analysis on RNA-seq count data using DESeq2, generating significant gene lists and visualizations. It is an AI Agent Skill for Claude Code / OpenClaw, with 156 downloads so far.
How do I install Differential Gene Expression Analysis (RNA-seq)?
Run "/install differential-gene-analysis-rna-seq" 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 v1.0.2.
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