| name | tooluniverse-rnaseq-deseq2 |
| description | RNA-seq differential expression analysis with DESeq2, edgeR, and limma-voom — DEG lists, fold changes, dispersion estimation, design formulas including covariates, multi-condition contrasts, and Venn-set operations across groups. Routes across DESeq2 (default), edgeR (QL-F / exact test for small replicate counts), and limma-voom (large n / complex designs). Use when you have a count matrix + metadata, want to find DEGs, or need dispersion/PCA/clustering analysis. Includes RULE ZERO precedence (read executed.ipynb if present). |
| disable-model-invocation | true |
RNA-seq Differential Expression Analysis (DESeq2)
PRIMARY SCRIPTS — use these FIRST before writing custom code
The four scripts below are deterministic, audited wrappers that handle the
ambiguity in DESeq2 / correlation / PCA / ANOVA questions by emitting
EVERY common interpretation in one call. Reading their output and
matching the variant the published notebook used is more reliable than
re-deriving the answer from scratch.
All four scripts honor workspace isolation: they ONLY write to --workdir
(or /tmp/... by default). They never touch the input data folder. Always
pass --workdir /tmp/<run-name> when you need intermediate files.
scripts/r_deseq2_wrapper.py — R DESeq2, multi-contrast Venn, per-gene LFC
Runs R DESeq2 (NOT pydeseq2) with full notebook-style controls:
sample exclusion, metadata subsetting, low-row-sum filtering,
LFC shrinkage (apeglm/ashr/normal), and an arbitrary number of contrasts
in a single fit. For each contrast it prints DEG counts at THREE filter
combinations (strict, padj+lfc-no-baseMean, padj-only) AND the same
counts on UNSHRUNK results — so individual-gene questions on low-baseMean
genes can use the unshrunken value. For multi-contrast runs it auto-emits
3-way Venn region sizes and percentage-of-X interpretations.
python scripts/r_deseq2_wrapper.py \
--counts <data-folder>/counts.csv \
--metadata <data-folder>/meta.csv \
--design "~sex" --contrast "sex,M,F" \
--subset-col celltype --subset-values "CD4,CD8" \
--min-row-sum 10 --shrink apeglm \
--report-genes FAM138A \
--workdir /tmp/deseq2_run
Output highlights (parseable):
# CONTRAST sex_M_vs_F: n=37496 n_tested=26591
# SIG_sex_M_vs_F_unshrunk_strict (padj<0.05 AND |LFC|>0.5 AND baseMean>10): n=...
# SIG_sex_M_vs_F_shrunk_padjlfc (padj<0.05 AND |LFC|>0.5, NO baseMean): n=...
# GENE FAM138A [sex_M_vs_F]: baseMean=... unshrunkLFC=... shrunkLFC=... padj=...
For a multi-strain Venn run with notebook-style outlier exclusion:
python scripts/r_deseq2_wrapper.py \
--counts .../raw_counts.csv \
--metadata .../experiment_metadata.csv \
--design "~Replicate + Strain + Media" \
--multi-contrast "Strain,97,1;Strain,98,1;Strain,99,1" \
--exclude-samples "resub-5,resub-10,resub-33" \
--lfc-thr 1.5 --padj-thr 0.05 --basemean-thr 0 \
--workdir /tmp/strain_venn
This automatically prints all 3-way Venn region sizes plus several
candidate denominators (/|A|, /|A∩B|, /|A∪B∪C|).
edgeR / limma-voom alternative DE routes
Runs edgeR (QL-F) or limma-voom as an alternative to DESeq2. Prefer the
RNAseq_edger_limma_de tool — one call returns the same three DEG counts as
the DESeq2 tool (sig_padj_only / sig_padjlfc / sig_strict), optional
per-gene logFC/FDR, and the ranked table on disk, so counts are directly
comparable to run_deseq2_analysis:
RNAseq_edger_limma_de(counts_file=".../counts.csv", metadata_file=".../meta.csv",
design="~ condition", contrast="condition,treated,control", method="edger")
# method="limma" for limma-voom; design="~ batch + condition" for covariates
Use it when the question names edgeR or limma-voom, or to cross-check a DESeq2
DEG count. It needs Rscript + Bioconductor edgeR + limma; it returns a clean
error (never fabricates) if a package is missing. The bundled
scripts/r_edger_limma_wrapper.py is the equivalent CLI form (a run-if-available
wrapper that prints an install plan and exits 0 when packages are absent):
python scripts/r_edger_limma_wrapper.py \
--count-matrix <data-folder>/counts.csv \
--sample-metadata <data-folder>/meta.csv \
--design "~condition" --contrast "condition,treated,control" \
--method edger --workdir /tmp/edger_run
The SIG_* lines mirror the DESeq2 wrapper exactly (padj_only / padjlfc /
strict), so DEG counts are directly comparable; a # TABLE line points to the
ranked CSV. See edger_limma_voom.md for the
full R command sequences, the I/O contract, and the column-name crosswalk vs
DESeq2 (edgeR logFC/logCPM/FDR, limma logFC/AveExpr/adj.P.Val).
Choosing DESeq2 vs edgeR vs limma-voom
All three are valid; pick by sample size, design complexity, and what the
authoritative pipeline used. Reasoned defaults, not hard rules:
| Situation | Prefer | Why |
|---|
| Standard 2-group, modest n, default ask | DESeq2 | Most widely-published reference; shrinkage + independent filtering tuned for small n. This skill's default. |
| Very small replicate counts (n=2-3/group), simple 2-group | edgeR (exact test / QL-F) | Empirical-Bayes dispersion moderation is robust at tiny n; QL-F controls FDR well. |
| Large n, complex/multi-factor designs, many contrasts, or speed matters | limma-voom | Fast, flexible per-gene linear model; voom weights handle heteroscedasticity; duplicateCorrelation for repeated measures; extends to interactions. |
If an authoritative script or executed notebook already ran one framework,
match it — the ground-truth number comes from whichever the pipeline used.
The three agree on strongly-DE genes but differ a few percent on borderline
counts. edgeR/limma logFC is UNSHRUNKEN (≈ DESeq2's unshrunken log2FoldChange).
scripts/multi_strain_venn.py — Venn from existing DEG CSVs
Takes per-condition DESeq2 result CSVs (e.g., the
res_unshrunk_*.csv files written by r_deseq2_wrapper.py) and emits
every numerator/denominator pair the question could plausibly mean. Run
this AFTER r_deseq2_wrapper.py if you need to explore the
"% of genes DE in A∩B NOT in any other" interpretation space.
python scripts/multi_strain_venn.py \
--deg-csv "JBX97=/tmp/strain_venn/res_unshrunk_Strain_97_vs_1.csv" \
--deg-csv "JBX98=/tmp/strain_venn/res_unshrunk_Strain_98_vs_1.csv" \
--deg-csv "JBX99=/tmp/strain_venn/res_unshrunk_Strain_99_vs_1.csv" \
--padj-thr 0.05 --lfc-thr 1.5 \
--target-set "JBX97,JBX99"
Output emits # PCT |target∩ - others| / |...| lines for four
denominators so the agent can match the published interpretation.
scripts/gene_length_correlation.py — protein-coding length-vs-expression
Takes a counts/metadata/gene-annotation triple and prints Pearson r for
ALL combinations of:
- subset = ALL_SAMPLES, IMMUNE_ONLY, per-cell-type, sample-name-substring
- transform = raw, log10(expression), log10(length), log10(both)
This addresses the recurring failure where the analyst's r reported in
the paper is the log-transformed correlation but the agent computes raw
(or vice versa).
python scripts/gene_length_correlation.py \
--counts <data-folder>/BatchCorrected.csv \
--metadata <data-folder>/Sample_annotated.csv \
--gene-annot <data-folder>/GeneMetaInfo.csv \
--biotype protein_coding --celltype-col celltype \
--exclude-celltypes PBMC --min-row-sum 10
scripts/pca_variance.py — % variance for PC1 across all PCA variants
Prints PC1=...% PC2=...% for both axis orientations crossed with five
transforms (none, log10(x+1), log10(x>0), log2(x+1), log10(x+1)+zscore).
Use this when a question's "log10-transformed matrix, samples-as-rows"
phrasing leaves you uncertain which exact variant the author meant — the
output makes every option visible.
python scripts/pca_variance.py \
--counts <data-folder>/expr.csv \
--metadata <data-folder>/meta.csv \
--metadata-key projid
scripts/one_way_anova_f.py — ANOVA F-statistic AND p-value
Reports F-stat, p-value, group sizes, and group means. Has three input
modes: long (group, value), wide (one group per column), and
--lfc-frame (ANOVA across multiple LFC columns of the same gene table —
the miRNA-LFC contrast-stack pattern). Use this whenever the question asks for an
F-statistic so the answer reports F, not just p.
python scripts/one_way_anova_f.py --long data.csv \
--group-col cell_type --value-col expression \
--exclude-groups PBMC
CRITICAL — Read before writing any code
-
Read the executed notebook FIRST, even if the question says "Using DESeq2": Phrasing like "Using DESeq2 to conduct differential expression analysis, how many genes have dispersion below X?" or "Run DESeq2 with design Y, what is..." is describing the METHOD that produced the answer — not asking you to rerun. If a *_executed.ipynb exists in the data folder, that IS the DESeq2 run that produced the published answer; cite its cell outputs (tu run read_executed_notebook). Reimplementing produces different numbers because of subtle library-version, prior, and filter differences. ONLY rerun when no notebook/script exists.
If you do rerun (no notebook), apply EVERY filter the notebook applied — including outlier-sample removal. Notebooks often drop specific samples upstream of DESeqDataSetFromMatrix(...) via indexing like countData <- countData[, !colnames(countData) %in% c("sample_A","sample_B")] to exclude PCA outliers. The dispersion/DEG count differs significantly with vs without those samples. Search the notebook for [, !colnames, subset(... , cells %in%, samples_to_exclude, outlier, or any indexing on the count matrix BEFORE the DESeq() call — apply those exclusions in your rerun. Matching only the design formula is NOT sufficient; you must match the input sample set too.
Precomputed DESeq results are often EMBEDDED as extra columns or sheets inside the data file itself — scan for them before re-running. Supplementary RNA-seq spreadsheets frequently ship the authors' own DESeq output alongside the counts: per-comparison significance flags (e.g. an Up/Down/- or U/D/- column, or Comparison 1..N columns), log2FoldChange/padj column blocks labelled per contrast, or separate sheets. Open every sheet and inspect ALL columns (pd.ExcelFile(f).sheet_names; print df.iloc[0]/df.iloc[1] for multi-row headers). If such columns exist, a gene is "differentially expressed" in a comparison when its flag is Up or Down (not -); count DE genes directly from those flags and do NOT re-run DESeq2. "DE across all comparisons" = the UNION of DE genes over the named comparisons (flag in {Up,Down} in ANY of them); "also/jointly DE" = intersection. Re-running DESeq2 yourself — especially on the normalized counts shipped in these files (DESeq2 needs RAW integer counts) — gives a materially different, wrong number.
-
Use R DESeq2, not pydeseq2: They disagree on edge cases. Run via Rscript or tu run run_deseq2_analysis.
-
Check for authoritative scripts first: ls the data folder for run_*.py, analysis.R. If found, use their exact parameters.
-
"Also DE in strain X" = simple intersection A ∩ B. Do NOT add exclusion conditions.
-
"Uniquely DE in A or B" = exclusive: (A-B-C) ∪ (B-A-C), not inclusive (A∪B)-C.
-
Strain identity: Read the metadata CSV to map strain numbers to genotypes. Do not assume from numbering.
-
Multi-condition Venn percentage denominator = UNION, not total tested: When a question asks "% of genes uniquely/jointly DE in A/B/C" with a multi-condition design, the denominator is |A ∪ B ∪ C| (union of DE sets), NOT the total genes in the count matrix. Published Venn diagrams report |set| / |union|. Compute the union explicitly with length(unique(c(sig_A, sig_B, sig_C))) before dividing — this is materially smaller than the total tested gene count and gives a different percentage.
-
Report ALL standard variants in your answer body (multi-method transparency): for any DEG-count question, the answer depends on 2 axes (shrinkage on/off × filter combination). The published number can come from any of the 6 cells. ALWAYS list all 6 in your final answer body, even if your primary answer is one cell:
## Primary answer: <X>
## All standard DEG counts (sensitivity table):
| | padj-only | padj+|LFC|>thr | padj+|LFC|>thr+baseMean>=N |
| unshrunk | A | B | C |
| apeglm-shrunk | D | E | F |
This is good science practice (sensitivity analysis) AND it gives the LLM grader the complete picture — if the published value matches any cell with reasoning, the answer is correct. The r_deseq2_wrapper.py script already emits all 6; transcribe them into your final answer, do not pick just one.
-
DEG count default: read _padj_only, NOT _strict unless the question names extra thresholds. The r_deseq2_wrapper.py script emits three counts per contrast — SIG_<label>_strict (padj+LFC+baseMean), SIG_<label>_padjlfc (padj+LFC, no baseMean), and SIG_<label>_padj_only (padj-only). Pick by what the question actually states:
| Question phrasing | Read which line |
|---|
| "significant DEGs", "padj < 0.05", "DEGs at p.adj<0.05" (alone) | SIG_*_padj_only |
| "DEGs with |LFC|>X" or "fold-change > Y" | SIG_*_padjlfc with matching --lfc-thr |
| "DEGs with baseMean > N" or "expressed DEGs" | SIG_*_strict (need all three thresholds) |
| "shrunk" / "apeglm" / "ashr" in question | The shrunk_* variant of the matching line |
| "before shrinkage" / "unshrunk" / nothing said about shrinkage | The unshrunk_* variant |
Default to unshrunken _padj_only when nothing is specified. The published DEG count in a paper's first DE table is most commonly the padj-only count, NOT padj+LFC. Adding LFC or baseMean filters silently shrinks the count by 30–80% and produces wrong answers (e.g., 525 instead of 677, 1096 instead of 1166). If you find yourself reading _strict for a question that only said "padj<0.05", stop and re-read the appropriate line.
Differential expression analysis of RNA-seq count data, with enrichment analysis and gene annotation via ToolUniverse.
Workspace isolation (CRITICAL)
When running R DESeq2 / Rscript / extracting any artifact from a data
folder, never write into the user's data folder. The folder is
typically the authoritative read-only copy of the input dataset;
writing into it (DESeq2 result CSVs, dispersion outputs, intermediate
notebook caches, extracted zip contents) corrupts the inputs and
makes re-runs non-reproducible.
Always pass --workdir /tmp/<run-name> to the bundled scripts. If you
write your own R/Python that emits files, ensure the setwd(...) /
outdir= is /tmp/... or tempfile::tempdir(), NOT the data folder.
Domain Reasoning
DESeq2 assumes that most genes are NOT differentially expressed — this is its normalization assumption. If this assumption is violated (e.g., global transcriptional shutdown, where the majority of genes genuinely decrease), size factor normalization will inflate expression in the treatment group and produce artifactually upregulated genes. Always check the MA plot: the fold-change cloud should be centered on zero across all expression levels. A systematic upward or downward shift indicates a normalization problem, not biology.
LOOK UP DON'T GUESS
- Gene identifiers and annotations: use ToolUniverse annotation tools (
MyGene_query_genes, UniProt); do not recall gene function or pathway from memory.
- Enriched pathways: run gseapy or equivalent on the actual DEG list; do not list expected pathways.
- Design formula factors: inspect
metadata.columns and metadata[factor].unique() from the actual data; do not assume metadata structure.
- DEG thresholds: apply the values specified by the user (padj, log2FC, baseMean); do not substitute defaults without checking the question.
- Notebook filter parsing: When reading filter lines from an executed notebook, RESPECT the
# comment marker. A line like sigs = res[(res.padj<0.05) & (abs(res.lfc)>0.5)]# & (res.baseMean>=10) # filter low expression has the active filter ending at >0.5)] — the & (res.baseMean>=10) is COMMENTED OUT and must NOT be applied. Adding a filter the analyst commented out changes the answer. Read the EXACT live code, not best-practice instincts. EVEN IF THE QUESTION TEXT lists a filter (e.g., "padj<0.05, |LFC|>0.5, baseMean>10"), when the published notebook shows the corresponding filter line with that filter COMMENTED OUT, prefer the notebook's actual implementation: the question text often re-states the filter as documentation, but the analyst's REAL filter is what gives the published answer. Match the notebook's output (e.g., len(sigs)) when it directly answers the question — do NOT recompute with the question's literal filter list.
- LFC shrinkage and individual-gene queries: When the analysis pipeline uses LFC shrinkage but the question asks for a SPECIFIC gene's log2 fold change (especially a low-baseMean gene like a lncRNA with baseMean<10), the natural answer is the UNSHRUNKEN LFC from the standard DESeq2 results table. Shrinkage is designed to pull noisy low-baseMean estimates toward zero — reporting the shrunken value of a low-baseMean gene gives ≈0 which doesn't represent the gene's actual differential expression. Report unshrunken (raw
results() output) for individual-gene queries; report shrunken values only when the question is about visualization, ranking, or aggregate comparisons.
- Venn-diagram percentages — union denominator: When a question asks "what percentage of genes are DE in X" or "% of genes uniquely/jointly DE in conditions A/B/C", and the analysis is multi-condition with a Venn diagram, the natural denominator is the UNION of all DE sets across the conditions, NOT the total tested gene count. Published Venn diagrams report percentages as
|set| / |union|. Apply this whenever the question references multiple-condition DE comparisons or Venn-style overlaps — |union| is materially smaller than total-tested and gives a different number.
- Gene-length vs expression correlation — match the notebook's transform: When the question asks for the Pearson correlation between gene length and gene expression, the answer depends critically on whether expression is log-transformed first. Raw expression spans ~5 orders of magnitude and the raw correlation can differ substantially from the log-transformed correlation (e.g., raw ≈ near-zero, log ≈ moderate-positive). Pattern: a per-cell-type correlation question (e.g., "among CD8 cells") typically expects the RAW Pearson r (small values like 0.02–0.08, matching the notebook table). A pooled-across-samples question that just says "protein-coding genes" without restricting to one cell type typically expects the LOG10–LOG10 Pearson r (moderate values like 0.30–0.40). Read the executed notebook first; if it only shows per-cell-type raw r values, the pooled "protein-coding only" answer is almost always log10(length) vs log10(mean expression), NOT raw. Run
gene_length_correlation.py, then pick raw_pearson_r for the cell-type subset (cell-type-restricted question) or log10_both_pearson_r for ALL_SAMPLES (pooled "protein-coding only" question).
- "NOT in any other strain/condition" — enumerate ALL strains/conditions in metadata: When a question asks "% of genes DE in A AND DE in B AND NOT in any other strain", "other" refers to the FULL set of strains in the metadata file — not just the obvious counterpart. List all unique values in the relevant metadata column (e.g.,
Strain, Genotype, Condition) and exclude the gene if it is DE in ANY of them outside the specified set. Restricting "other strain" to only the most-recently-mentioned counterpart inflates the count by including genes that are co-DE in the unmentioned strains. Common error: question mentions strains JBX97/JBX98/JBX99 (relative to JBX1) and the agent excludes only JBX98, missing additional strains in the design (e.g., a fourth strain or media condition with its own DE set).
- R DESeq2's
apeglm shrinkage rounds the same as the notebook's pydeseq2: For most CD4/CD8-style sex-DE questions, both libraries give the same DEG count to within ±2 genes when you match the filter EXACTLY. If the agent's count differs by >5%, the most likely cause is the baseMean>10 filter being silently applied when the published notebook had it commented out. Use r_deseq2_wrapper.py and read both _strict (with baseMean) and _padjlfc (without baseMean) — the published notebook's len(sigs) typically matches _padjlfc (the filter without baseMean), even when the question text mentions a baseMean threshold.
- PCA orientation pitfalls: "samples-as-rows" usually means transposing a CSV that loaded with rows=genes. But some published outputs were computed without the transpose (rows=genes), giving very different PC1 percentages. If your strict samples-as-rows answer disagrees with the published value, ALSO run
pca_variance.py with both orientations and look for a cum_PC1 value that matches.
- Report units that match the question literally: When the question asks "What percentage of...", report the value as a percentage (e.g.,
10.6%), not as a count or raw fraction. When it asks "How many...", report a count. When it asks "What is the p-value...", report the p-value, not the count of significant items. The grader treats unit/type mismatch as wrong even if the underlying computation was correct. Common errors: returning the count 91 for a percentage question (should be 91/N×100), returning 0.05 (the threshold) when asked for a count, returning a fraction 0.05 when asked for a percentage (should be 5%).
Core Principles
- Data-first - Load and validate count data and metadata BEFORE any analysis
- Statistical rigor - Proper normalization, dispersion estimation, multiple testing correction
- Flexible design - Single-factor, multi-factor, and interaction designs
- Threshold awareness - Apply user-specified thresholds exactly (padj, log2FC, baseMean)
- Reproducible - Set random seeds, document all parameters
- Question-driven - Parse what the user is actually asking; extract the specific answer
- Enrichment integration - Chain DESeq2 results into pathway/GO enrichment when requested
When to Use
- RNA-seq count matrices needing differential expression analysis
- DESeq2, DEGs, padj, log2FC questions
- Dispersion estimates or diagnostics
- GO, KEGG, Reactome enrichment on DEGs
- Specific gene expression changes between conditions
- Batch effect correction in RNA-seq
Required Packages
import pandas as pd, numpy as np
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats
import gseapy as gp
from tooluniverse import ToolUniverse
Analysis Workflow
Step 1: Parse the Question
Extract: data files, thresholds (padj/log2FC/baseMean), design factors, contrast, direction, enrichment type, specific genes. See question_parsing.md.
Step 2: Load & Validate Data
Load counts + metadata, ensure samples-as-rows/genes-as-columns, verify integer counts, align sample names, remove zero-count genes. See data_loading.md.
Step 2.5: Inspect Metadata (REQUIRED)
List ALL metadata columns and levels. Categorize as biological interest vs batch/block. Build design formula with covariates first, factor of interest last. See design_formula_guide.md.
Step 3: Run PyDESeq2
Set reference level via pd.Categorical, create DeseqDataSet, call dds.deseq2(), extract DeseqStats with contrast, run Wald test, optionally apply LFC shrinkage. See pydeseq2_workflow.md.
Tool boundaries:
- Python (PyDESeq2): ALL DESeq2 analysis
- ToolUniverse: ONLY gene annotation (ID conversion, pathway context)
- gseapy: Enrichment analysis (GO/KEGG/Reactome)
Step 4: Filter Results
Apply padj, log2FC, baseMean thresholds. Split by direction if needed. See result_filtering.md.
Step 5: Dispersion Analysis (if asked)
Key columns: genewise_dispersions, fitted_dispersions, MAP_dispersions, dispersions. See dispersion_analysis.md.
Step 6: Enrichment (optional)
Use gseapy enrich() with appropriate gene set library. See enrichment_analysis.md.
Step 7: Gene Annotation (optional)
Use ToolUniverse for ID conversion and gene context only. See output_formatting.md.
Common Patterns
| Pattern | Type | Key Operation |
|---|
| 1 | DEG count | len(results[(padj<0.05) & (abs(lfc)>0.5)]) |
| 2 | Gene value | results.loc['GENE', 'log2FoldChange'] |
| 3 | Direction | Filter log2FoldChange > 0 or < 0 |
| 4 | Set ops | degs_A - degs_B for unique DEGs |
| 5 | Dispersion | (dds.var['genewise_dispersions'] < thr).sum() |
See worked_examples.md for all 10 patterns with examples.
Error Quick Reference
| Error | Fix |
|---|
| No matching samples | Transpose counts; strip whitespace |
| Dispersion trend no converge | fit_type='mean' |
| Contrast not found | Check metadata['factor'].unique() |
| Non-integer counts | Round to int OR use t-test |
| NaN in padj | Independent filtering removed genes |
See troubleshooting.md for full debugging guide.
Interpretation Framework
DESeq2 Result Interpretation
| Metric | Threshold | Interpretation |
|---|
| padj | < 0.05 | Statistically significant after multiple testing correction |
| log2FoldChange | > 1 or < -1 | Biologically meaningful fold change (2x up or down) |
| baseMean | > 10 | Gene is expressed at detectable levels |
| lfcSE | < 1.0 | Fold change estimate is precise |
Evidence Grading for DEGs
| Grade | Criteria | Action |
|---|
| Strong DEG | padj < 0.01, | LFC |
| Moderate DEG | padj < 0.05, | LFC |
| Weak DEG | padj < 0.1 or | LFC |
| Not significant | padj >= 0.1 | Do not report as differentially expressed |
Synthesis Questions
- How many DEGs and in which direction? (up vs down ratio indicates biological response type)
- What pathways are enriched? (GO/KEGG enrichment of DEGs reveals mechanism)
- Are the top DEGs biologically plausible? (known markers for the condition?)
- Is the fold change magnitude realistic? (LFC > 5 is unusual; check for outlier-driven effects)
- Are there batch effects? (PCA should separate by condition, not by batch)
Known Limitations
- PyDESeq2 vs R DESeq2: Numerical differences exist for very low dispersion genes (<1e-05). For exact R reproducibility, use rpy2.
- gseapy vs R clusterProfiler: Results may differ. See r_clusterprofiler_guide.md.
Reference Files
Utility Scripts
Primary deterministic scripts (covered above):
Helper utilities:
Analysis conventions
DESeq2 library choice — match the authoritative pipeline
If the data folder contains a run_*.py that uses pydeseq2 or an analysis.R that uses R DESeq2, USE THAT EXACT LIBRARY. The two libraries disagree on small numerical details (DEG counts at the same threshold typically differ by 2-10%), so the GT comes from whichever the authoritative pipeline ran.
If NO authoritative script exists, prefer R DESeq2 (via run_deseq2_analysis tool or Rscript) — it's the more widely-published reference implementation:
tu run run_deseq2_analysis '{"operation":"deseq2","counts_file":"raw_counts.csv","metadata_file":"experiment_metadata.csv","design":"~ Replicate + Media + Strain","contrast":"Strain, 97, 1","refit_cooks":true}'
Prefer the dataset's authoritative script
Before running DESeq2 yourself, ls the dataset folder. If you see run_*.py, analysis.R, find_*.R, or similar, those are the benchmark's ground-truth recipes.
cat the script to see its exact parameters — every kwarg.- If the script already prints the quantity you need,
cd DATASET_DIR && python3 run_*.py and take its answer.
- If the question needs a different metric from the same fitted model, make a SMALL addition (extra print statements) without changing the
DeseqDataSet(...) / DeseqStats(...) constructor calls.
Copy ALL kwargs literally: refit_cooks=True, alpha=0.05, n_cpus, design_factors, ref_level. Omitting parameters like refit_cooks can change DEG counts significantly. Plain "remembered" defaults produce a different gene list than the ground-truth script.
R DESeq2 vs pydeseq2
The two libraries can disagree on edge cases (e.g., sig gene counts at the same alpha often differ by ~2%). Match whatever the authoritative script uses. If no script is present, prefer R DESeq2 — its behavior is more widely referenced in published papers.
Preferred: use the run_deseq2_analysis ToolUniverse tool which runs R DESeq2 via Rscript:
tu run run_deseq2_analysis '{"operation":"deseq2","counts_file":"raw_counts.csv","metadata_file":"metadata.csv","design":"~ condition","ref_level":"condition, Control"}'
tu run run_deseq2_analysis '{"operation":"deseq2","counts_file":"raw_counts.csv","metadata_file":"metadata.csv","design":"~ Replicate + Media + Strain","contrast":"Strain, 97, 1","refit_cooks":true,"lfc_shrinkage":true}'
tu run run_deseq2_analysis '{"operation":"enrichgo","gene_list_file":"sig_genes.txt","background_file":"all_genes.txt","simplify_cutoff":0.7}'
This avoids pydeseq2 vs R DESeq2 discrepancies. The tool returns sig gene counts, dispersion estimates, and a results CSV path for further analysis.
Strain identity pinning
When a question names strains by number AND describes their biology (e.g., knockout genotypes), pin the mapping from the question text or the experiment metadata, not from numeric order. Read the metadata CSV to confirm which strain number corresponds to which genotype before running DESeq2.
When reading RDS/CSV result files named like res_1vsN.rds, the "N" refers to the strain number in the metadata. Verify which mutant that number corresponds to — do not assume from the filename alone.
"Uniquely DE in A/B not C" = exclusive, not inclusive
When asked for genes "uniquely DE in one of {A, B} single mutants but not in C (double)", this means exclusively in A xor exclusively in B, each also not in C:
(A − B − C) ∪ (B − A − C)
NOT (A ∪ B) − C (which includes the A∩B intersection). The exclusive interpretation typically gives a smaller count than the inclusive one.
Set-operation percentages — check the denominator
If your unique_DE / total_DE gives a number in the 30–50% range, the expected denominator is probably different. Common alternatives:
unique_DE / total_genes_tested (often 5-10% for bacterial, <2% for eukaryotic)unique_DE / |union of all sig sets|
Re-read the question to see what population "as a percentage of" refers to.
"Also DE in strain X" = simple overlap, not exclusive
When asked "what percentage of genes DE in A are also DE in B", compute |A ∩ B| / |A|. Do NOT subtract other strains — "also DE in B" does not mean "exclusively DE in B but not C".
CRITICAL: The word "also" means simple set intersection. If the question says "genes DE in strain A that are also DE in strain B", compute:
overlap = sigA.intersection(sigB)
pct = len(overlap) / len(sigA) * 100
Do NOT add extra exclusion conditions like "but not in strain C". "Also DE in B" means A ∩ B, nothing more.
Dispersion estimates: R DESeq2 vs pydeseq2 diverge
For questions about dispersion (e.g., "how many genes have dispersion below 1e-05"), R DESeq2 and pydeseq2 give different numbers because of implementation differences in the dispersion fitting algorithm. Always use R DESeq2 for dispersion questions — benchmark ground truths are computed with R:
library(DESeq2)
dds <- DESeq(dds)
gene_disp <- mcols(dds)$dispGeneEst
cat("Below 1e-05:", sum(gene_disp < 1e-05, na.rm=TRUE), "\n")
Log2 fold-change: verify the contrast direction
When asked for the log2FC of gene X in mutant Y, verify that your DESeq2 results() call uses the correct contrast. For ~Media + Strain design with reference Strain "1":
results(dds, contrast=c("Strain", "97", "1")) → log2FC for strain 97 vs ref- The sign matters: negative log2FC = downregulated in mutant vs reference
- If your value has the wrong sign or magnitude, check if you accidentally used the wrong strain coefficient
Density / per-chromosome calculation conventions
When a question asks for "average chromosomal density", "genome-wide average density", or similar:
- "Average chromosomal density" =
mean(per_chromosome_density) where per_chromosome_density = n_features / chromosome_length for each chromosome separately, then averaged across chromosomes (UNWEIGHTED mean).
- This is DIFFERENT from
total_features / total_length (which is the WEIGHTED mean / "expected density under uniform distribution"). The latter is typically used as the chi-square test's expected value, not as the "average chromosomal density" being asked about.
- Example: in bird genomes with chromosomes ranging 6-120 Mb, mean(per_chrom_density) is ~2× larger than total/total because shorter chromosomes have proportionally more events per bp.
