---

name: pca-dimensionality-reduction description: Use when performing PCA principal component dimensionality reduction on tabular numeric data. Supports command-line parameter input, automatic numeric feature selection, parameter validation, result directory creation, and CSV or TXT format result export. license: MIT author: AIPOCH

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

PCA Dimensionality Reduction Analysis

Use this skill to run principal component analysis on a tabular dataset and export explained variance, sample scores, feature loadings, and diagnostic figures.

Use This Skill When

• You need to reduce multiple numeric variables into a smaller set of principal components.
• You need a command-line PCA workflow with parameter validation.
• You need standardized output files for downstream analysis.

Primary Command

Rscript scripts/main.R \
  --data_file <input_file> \
  --output_dir <output_dir> \
  --feature_columns <comma_separated_numeric_columns>

Prerequisites

• Rscript is available in the shell.
• Required R packages: optparse, data.table.
• Install missing packages with Rscript -e 'install.packages(c("optparse", "data.table"), repos="https://cloud.r-project.org")'.

Core Arguments

Argument	Required	Description
--data_file	Yes	Input data file in CSV, TXT, or TSV format
--output_dir	No	Output directory, default ./PCA_Results
--feature_columns	No	Comma-separated numeric feature columns. Default uses all numeric columns except ID/group columns
--sample_id_column	No	Optional sample ID column. If omitted and the first column is non-numeric with unique values, it is used automatically
--group_column	No	Optional grouping column to carry into score output and score plot
--n_components	No	Maximum number of principal components to export, default 5
--center_data	No	true or false, default true
--scale_data	No	true or false, default true
--top_loadings	No	Number of top absolute loadings to export per component, default 10
--output_format	No	csv or txt, default csv
--output_prefix	No	Output filename prefix, default pca

Input Requirements

• The input file must contain at least 2 usable numeric feature columns.
• PCA is run on rows as samples and columns as features.
• Missing or non-finite values in selected feature columns are removed row-wise before analysis.
• At least 2 complete samples must remain after filtering.
• Selected feature columns must have non-zero variance after filtering.

Example input:

SampleID,Group,GeneA,GeneB,GeneC,GeneD
S01,Control,2.1,1.9,8.2,4.3
S02,Control,2.4,2.2,8.0,4.6
S03,Treated,6.1,5.7,2.8,8.1

Minimal Workflow

1. Confirm the input file exists and identify the numeric feature columns for PCA.
2. Run scripts/main.R with the requested output directory and optional feature, ID, or group columns.
3. Check the output directory for result files under table/, data/, and figure/.

If you omit --data_file, the script exits with SKILL_MISSING_INPUT.

Outputs

Expected output structure:

<output_dir>/
├── table/
├── figure/
└── data/

Primary result files:

• table/<output_prefix>_summary.csv
• table/<output_prefix>_scores.csv
• table/<output_prefix>_loadings.csv
• table/<output_prefix>_top_loadings.csv

Figure files:

• figure/<output_prefix>_scree_plot.png
• figure/<output_prefix>_score_plot.png

Key fields include:

• component
• standard_deviation
• variance
• proportion_variance
• cumulative_variance
• sample_id
• feature
• loading

Interpretation Guide

• Use proportion_variance and cumulative_variance to decide how many components to retain.
• Use the score table to inspect sample separation in PC space.
• Use the loading tables to identify which original variables drive each component.

Read These Files When Needed

Need	File
PCA method details and interpretation	references/algorithm.md
More CLI examples	references/cli-guide.md
Error diagnosis	references/troubleshooting.md
Main execution entry point	scripts/main.R
Sample test data	tests/data/

Quick Examples

Basic PCA with explicit feature columns:

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_1.csv \
  --sample_id_column SampleID \
  --group_column Group \
  --feature_columns GeneA,GeneB,GeneC,GeneD,GeneE \
  --output_dir tests/output_basic

Auto-detect all numeric columns:

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_2.csv \
  --n_components 3 \
  --output_dir tests/output_numeric_only

Disable scaling:

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_1.csv \
  --sample_id_column SampleID \
  --group_column Group \
  --scale_data false \
  --output_dir tests/output_unscaled

Validation

Rscript scripts/main.R --help

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_1.csv \
  --sample_id_column SampleID \
  --group_column Group \
  --feature_columns GeneA,GeneB,GeneC,GeneD,GeneE \
  --output_dir tests/validation_output

After running analysis, verify that tests/validation_output/table/pca_summary.csv exists.

Common Errors

• SKILL_FILE_NOT_FOUND: Input file path is wrong or inaccessible.
• SKILL_MISSING_COLUMNS: A requested feature, sample ID, or group column is missing.
• SKILL_INVALID_DATA: Input data is malformed or unsuitable for PCA.
• SKILL_INVALID_PARAMETER: An argument value is invalid.
• SKILL_INSUFFICIENT_DATA: Too few complete samples or features remain for PCA.
• SKILL_DEPENDENCY_MISSING: A required R package such as optparse or data.table is unavailable.

If the issue is not obvious, read references/troubleshooting.md.