gene-protein-expression-matrix-normalization

name	gene-protein-expression-matrix-normalization
description	Use when normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Gene Protein Expression Matrix Normalization

When to Use

Use this skill when the user wants to normalize a numeric expression matrix before plotting, clustering, or exploratory comparison.

Typical requests:

• "Normalize this gene expression matrix with log2"
• "Do z-score scaling across samples"
• "Map protein abundance values into 0 to 1"

When Not to Use

Do not use this skill for:

• Count-model normalization such as CPM, TPM, TMM, or DESeq2 size factors
• Batch correction or covariate adjustment
• Single-cell preprocessing workflows
• Matrices that contain missing, Inf, or NaN values unless they are cleaned first

When to Read External Files

When executing the analysis, run:

Rscript scripts/main.R --input_file <matrix.csv> --output_dir <output_dir> --method <log2|zscore|minmax>

Situation	File to Read	Purpose
Need to execute the workflow	scripts/main.R	CLI entry point
Need algorithm details	references/algorithm.md	Method definitions and assumptions
Encounter an error	references/troubleshooting.md	Standard error codes and fixes
Need examples or baseline run details	references/cli-guide.md	Ready-to-run commands and test record
Need dependency declarations	DESCRIPTION	Runtime package list

Usage

Rscript scripts/main.R \
  --input_file tests/data/expression_matrix.csv \
  --output_dir ./output \
  --method log2 \
  --pseudo_count 1 \
  --seed 42

Arguments

Short	Long	Type	Default	Description
-i	--input_file	file	required	Expression matrix in CSV or TSV format
-o	--output_dir	dir	./output	Output directory
-m	--method	string	log2	Normalization method: log2, zscore, minmax
-r	--margin	string	column	Apply normalization by row or column
-p	--pseudo_count	numeric	1	Added before log2 transformation
-c	--center	boolean	true	Center values for z-score
-s	--scale_values	boolean	true	Scale values for z-score
-t	--timeout_seconds	integer	0	Optional timeout; 0 disables it
-d	--delimiter	string	auto	Input delimiter: auto, csv, or tsv
	--seed	integer	42	Random seed
	--verbose	boolean	true

Input Format

The first column must contain feature identifiers. Remaining columns must be finite numeric sample values.

Missing values and non-finite values such as NA, NaN, Inf, and -Inf are rejected.

feature,S1,S2,S3
TP53,10,20,30
EGFR,3,5,9

This skill accepts gene or protein expression matrices. It does not infer count-model normalization such as CPM, TPM, TMM, or DESeq2 size factors.

Output Files

If --output_dir already exists, result files with the same names are overwritten. When --verbose=true, the workflow prints a warning before writing into a non-empty output directory.

For single-sample inputs, feature_summary.csv reports per-feature standard deviations as 0 by design because each feature contributes one observed value.

File	Description
table/normalized_matrix.csv	Normalized matrix with the original feature column preserved
table/feature_summary.csv	Per-feature min, max, mean, and SD before and after normalization
table/sample_summary.csv	Per-sample min, max, mean, and SD before and after normalization
data/normalized_matrix.rds	Serialized normalized matrix and run metadata
run_record.txt	Structured execution record
output_manifest.txt	Output file manifest
session_info.txt	R session information

Methods

log2

Computes log2(x + pseudo_count) for each numeric value.

zscore

Centers and scales along the selected margin. margin=column standardizes each sample; margin=row standardizes each feature.

When center=false and scale_values=true, the workflow divides by standard deviation without subtracting the mean first.

minmax

Rescales values to [0, 1] along the selected margin. Constant vectors are returned as zeros to avoid division-by-zero errors.

Error Handling

Error	Cause	Solution
SKILL_FILE_NOT_FOUND	Input file path is invalid	Check the input path
SKILL_MISSING_COLUMNS	Matrix has fewer than two columns	Provide one feature column and at least one sample column
SKILL_INVALID_PARAMETER	CLI value is unsupported or malformed, or the matrix contains non-finite values	Review the argument table and inspect the matrix values
SKILL_TIMEOUT	The run exceeded --timeout_seconds	Increase the timeout or simplify the input size
SKILL_EMPTY_DATA	No usable rows or columns remain	Check the input matrix

Testing

Rscript scripts/main.R --help

Rscript tests/run_tests.R

Rscript tests/run_tests.R audit_output_check

Rscript tests/test_skill.R

Rscript tests/test_skill.R audit_output_check --skip-prepare

tests/run_tests.R executes bundled log2, zscore, and minmax runs and writes their outputs under tests/output/.

When you pass a relative directory name such as audit_output_check, the test runner writes outputs under tests/output/audit_output_check/.

Run tests/run_tests.R before tests/test_skill.R when you want to validate pre-generated outputs explicitly. The validation script can also prepare missing outputs on its own.