Bulk Rna Expression

Bulk RNA Expression

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially pandas and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Python-first workflow for bulk RNA-seq expression intake, normalization, sample QC, and downstream-ready matrices.

When To Use This Skill

• use when the task is bulk RNA-seq expression profiling before or alongside differential analysis
• use when the user has count matrices, transcript abundances, or aligned RNA-seq reads and needs expression summaries
• use when sample-level QC, PCA, or normalized matrices are required

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• count matrix
• sample metadata
• optional BAM or quantification outputs

Expected Outputs

• normalized matrix
• sample QC plots
• PCA or clustering summaries

Preferred Tools

• pandas
• numpy
• seaborn
• matplotlib
• scanpy for matrix utilities when appropriate

Starter Pattern

Preferred starting point: pandas
Inputs: count matrix, sample metadata, optional BAM or quantification outputs
Outputs: normalized matrix, sample QC plots, PCA or clustering summaries

Workflow

1. Validate matrix orientation

Confirm rows and columns, unique sample IDs, and whether counts are raw integers or already normalized.

2. Join metadata

Check condition labels, replicate structure, batch columns, and missing covariates before analysis.

3. Compute sample QC

Summarize library size, detected genes, missingness, outliers, and replicate similarity.

4. Normalize for exploration

Apply count-aware normalization or variance stabilization appropriate to the downstream method.

5. Export analysis-ready data

Save normalized matrices and QC tables for DE, enrichment, or reporting.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• normalized matrix
• sample QC plots
• PCA or clustering summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Check replicate structure, outlier samples, and whether counts versus normalized values are being mixed.
• Export ranked or contrast-aware tables when downstream enrichment is likely.

Anti-Patterns

• mixing raw counts and normalized values in the same table
• running DE directly on TPM values unless the method explicitly supports it
• skipping sample metadata validation before modeling

Related Skills

• RNA Quantification
• Differential Expression
• Alternative Splicing
• Small RNA Seq

Optional Supplements

• pydeseq2
• pysam