tooluniverse-rnaseq-deseq2

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).

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.

# Single-factor sex DE on a CD4/CD8 subset, with FAM138A LFC
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

1. 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); " DE" = intersection. Re-running DESeq2 yourself — especially on the counts shipped in these files (DESeq2 needs RAW integer counts) — gives a materially different, wrong number.

---

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 — is materially smaller than total-tested and gives a different number.

---

Core Principles

1. Data-first - Load and validate count data and metadata BEFORE any analysis
2. Statistical rigor - Proper normalization, dispersion estimation, multiple testing correction
3. Flexible design - Single-factor, multi-factor, and interaction designs
4. Threshold awareness - Apply user-specified thresholds exactly (padj, log2FC, baseMean)
5. Reproducible - Set random seeds, document all parameters
6. Question-driven - Parse what the user is actually asking; extract the specific answer
7. 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          # enrichment (optional)
from tooluniverse import ToolUniverse  # annotation (optional)

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

1. How many DEGs and in which direction? (up vs down ratio indicates biological response type)
2. What pathways are enriched? (GO/KEGG enrichment of DEGs reveals mechanism)
3. Are the top DEGs biologically plausible? (known markers for the condition?)
4. Is the fold change magnitude realistic? (LFC > 5 is unusual; check for outlier-driven effects)
5. 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

• question_parsing.md - Extract parameters from questions
• data_loading.md - Data loading and validation
• design_formula_guide.md - Multi-factor design decision tree
• pydeseq2_workflow.md - Complete PyDESeq2 code examples
• result_filtering.md - Advanced filtering and extraction
• dispersion_analysis.md - Dispersion diagnostics
• enrichment_analysis.md - GO/KEGG/Reactome workflows
• output_formatting.md - Format answers correctly
• worked_examples.md - All 10 question patterns
• troubleshooting.md - Common issues and debugging
• r_clusterprofiler_guide.md - R clusterProfiler via rpy2
• edger_limma_voom.md - edgeR / limma-voom DE routes: command sequences, contracts, column crosswalk

Utility Scripts

Primary deterministic scripts (covered above):

• r_deseq2_wrapper.py - R DESeq2 multi-contrast + Venn + per-gene LFC
• r_edger_limma_wrapper.py - edgeR / limma-voom DE (run-if-available; preflights Rscript + edgeR/limma)
• multi_strain_venn.py - Venn-style overlap percentages from DEG CSVs
• gene_length_correlation.py - Length vs expression Pearson r (all variants)
• pca_variance.py - % variance per PC across all common PCA variants
• one_way_anova_f.py - ANOVA F-stat + p-value (long/wide/LFC-frame)

Helper utilities:

• format_deseq2_output.py - Output formatters
• load_count_matrix.py - Data loading utilities
• convert_rds_to_csv.py - Convert .rds DESeq2 results to CSV

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.

1. cat the script to see its exact parameters — every kwarg.
2. If the script already prints the quantity you need, cd DATASET_DIR && python3 run_*.py and take its answer.
3. 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:

# Basic DESeq2
tu run run_deseq2_analysis '{"operation":"deseq2","counts_file":"raw_counts.csv","metadata_file":"metadata.csv","design":"~ condition","ref_level":"condition, Control"}'

# With contrast + LFC shrinkage + refit_cooks
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}'

# enrichGO + simplify (after saving DEG list to file)
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)
# Pre-shrinkage (genewise) dispersions:
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.