differential-expression

name	differential-expression
description	Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE figures.
tool_type	python
primary_tool	PyDESeq2

Differential Expression

Version Compatibility

Reference examples assume:

• pydeseq2 0.4+
• pandas 2.2+
• numpy 1.26+
• matplotlib 3.8+

Verify before use:

• Python: python -c "import pydeseq2, pandas; print(pydeseq2.__version__, pandas.__version__)"

Overview

Use this skill for count-based DE from bulk RNA-seq or similar count matrices when the user needs:

• robust model fitting
• explicit contrasts
• ranked gene tables
• volcano and MA plots
• pathway-ready output tables

When To Use This Skill

• raw count matrix and sample metadata are available
• the task is condition, treatment, or genotype comparison
• batch or pairing terms may need explicit modeling

Quick Route

• no replicates: do not pretend formal DE is robust
• 2 replicates per group: possible but conservative interpretation
• 3 or more replicates per group: standard starting point

Progressive Disclosure

• Read technical_reference.md for design formulas, confounding checks, and contrast logic.
• Read commands_and_thresholds.md for PyDESeq2 code, recommended filters, and output file conventions.

Prerequisites

Requirement	Recommendation
minimum replicates per group	>= 2
preferred replicates per group	>= 3
input values	raw integer counts

Expected Inputs

• raw count matrix
• sample metadata
• explicit contrast such as treated vs control

Expected Outputs

• results/de_results.tsv
• results/de_ranked_genes.tsv
• figures/volcano.pdf
• figures/ma_plot.pdf
• qc/sample_pca.pdf

Starter Pattern

from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata_df,
    design_factors=["condition", "batch"],
)
dds.deseq2()
stats = DeseqStats(dds, contrast=("condition", "treated", "control"))
stats.summary()
res = stats.results_df.sort_values("padj")
res.to_csv("results/de_results.tsv", sep="\t")

Workflow

1. Validate the design

Check:

• replicate counts
• factor levels
• batch balance
• paired structure
• confounded variables

2. Fit a count-aware model

Use raw counts, not TPM or log-normalized expression, for count-based DE frameworks.

3. Apply explicit filtering and ranking

Common reporting thresholds:

• padj < 0.05
• abs(log2FoldChange) >= 1

Export both the full table and a thresholded table.

4. Visualize results

At minimum:

• sample PCA
• volcano plot
• MA plot

5. Export pathway-ready artifacts

Produce a ranked gene list sorted by signed effect or Wald statistic for enrichment workflows.

Output Artifacts

results/
├── de_results.tsv
├── de_significant.tsv
└── de_ranked_genes.tsv
figures/
├── sample_pca.pdf
├── volcano.pdf
└── ma_plot.pdf
qc/
└── design_check.tsv

Quality Review

• raw counts only for model fitting
• no fully confounded batch and condition
• outlier samples reviewed before publication claims
• all final tables should include baseMean, log2FoldChange, pvalue, and padj

Anti-Patterns

• running DE on TPM as if it were count-based
• omitting batch or pairing terms that clearly exist
• showing only thresholded genes and hiding the full table
• using p-value alone without effect size

Related Skills

• Bulk RNA Expression
• RNA Quantification
• Pathway Analysis

Optional Supplements

• pydeseq2