Execute R code in the isolated analysis container. The R session is PERSISTENT: objects defined in earlier calls remain available.

OUTPUT RULES (enforced server-side — follow them in your code):

• Write to /output ONLY aggregated results: DE tables (log2FC, pvalue, padj per gene), pathway scores, group-level summary statistics, model coefficients.

• Do NOT write per-sample matrices (normalized counts, raw counts, PCA coordinates) as CSV/TSV to /output. Keep them in R memory for intermediate computation.

• Plots (PNG) showing sample-level data are permitted — use anonymous labels (S1, S2, ...).

• ggsave() and png() write to /output/; use descriptive names.

• If the server blocks a CSV file, revise your code to export an aggregated version.

Available R packages: DESeq2, edgeR, limma, ggplot2, pheatmap, ComplexHeatmap, EnhancedVolcano, ggrepel, patchwork, clusterProfiler, fgsea, msigdbr, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi. No internet access — use org.Hs.eg.db instead of biomaRt for gene annotation.

Execute Python code in the isolated analysis container. The Python namespace is PERSISTENT across calls.

OUTPUT RULES (same as execute_r):

• Write to /output ONLY aggregated results (gene/pathway level).

• Do NOT write per-sample matrices as CSV.

• Plots saved with matplotlib/seaborn: plt.savefig("/output/.png").

• If the server blocks a CSV, produce an aggregated equivalent instead.

Available packages: pydeseq2, pandas, numpy, matplotlib, seaborn, scipy, statsmodels.

List files available in /data (anonymized counts and metadata).

Read the first n_rows of a CSV/TSV file from /data and return it as a markdown table. Useful for inspecting column names, sample aliases, and group labels before writing analysis code.

Retrieve all files written to /output during this session. CSV files containing per-sample data are blocked and replaced with an explanatory message. Images (PNG) are returned as base64-encoded strings.

Return R sessionInfo(), available Python symbols, and files already produced in /output. Useful at the start of a session to confirm what packages are available.

Destroy the current Docker container and start a fresh one with the same anonymized data. Use this to clear all in-memory R/Python state and /output files.