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Agent skill

Hierarchical Clustering Plot

by aipoch in aipoch/medical-research-skills

A skill your agent uses when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample…

MITAuto-check passedResearch & Science

Install Hierarchical Clustering Plot

skills CLI
$ npx skills add aipoch/medical-research-skills --skill hierarchical-clustering-plot -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills hierarchical-clustering-plot --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/hierarchical-clustering-plot' .claude/skills/hierarchical-clustering-plot && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hierarchical-clustering-plot
GitHub stars
2k
Token cost
~3.1k tokens
SKILL.md length
1,144 words
Files
19 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample…

  • Works in 4 steps: Validate Input → Align Samples → Build Hierarchical Clustering → …
  • Building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table
  • SKILL.md covers When to Use, When to Read External Files, Usage and Arguments, plus 8 more sections
  • Runs R scripts from its folder

What it does

Hierarchical Clustering Plot is an agent skill from aipoch/medical-research-skills. Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for: differential expression testing, gene clustering heatmaps, single-cell clustering workflows.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 22 other files, including scripts and reference files (for example `eval_report_hierarchical-clustering-plot_result.json`, `references/algorithm.md` and `references/cli-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table
  • Especially for QC
  • Batch inspection
  • Sample similarity assessment

Example prompts

  • “/hierarchical-clustering-plot”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Validate Input
  2. Align Samples
  3. Build Hierarchical Clustering
  4. Save Outputs

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 8 files in scripts/ (R, from the files we listed), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hierarchical Clustering Plot loads about 3.1k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 1,144 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~108
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,144 words, ~3,114 tokens.

Download SKILL.mdSave it as .claude/skills/hierarchical-clustering-plot/SKILL.md (or your agent's skills folder). This skill also uses 18 other files; get the full folder from GitHub.
name
hierarchical-clustering-plot
description
Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for: differential expression testing, gene clustering heatmaps, single-cell clustering workflows.

Hierarchical Clustering Plot

When to Use

Use this skill when you need a sample-level hierarchical clustering dendrogram from a bulk expression matrix and a sample annotation table.

  • Good fits: sample QC, batch inspection, sample similarity assessment, checking whether annotated sample groups cluster as expected.
  • Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity.
  • Not for: differential expression testing, gene clustering heatmaps, single-cell clustering workflows.

When to Read External Files

SituationFile to ReadPurpose
Need algorithm detailsreferences/algorithm.mdDistance calculation, linkage rules, and clustering assumptions
Need to run analysis or inspect CLI entrypoint behaviorscripts/main.RExecute the workflow and inspect argument parsing, defaults, required flags, and sourced modules
Need workflow implementation detailsscripts/run_analysis.RSee orchestration order, temp workspace handling, and output generation
Need logging or warning behaviorscripts/logging_utils.RSee standardized console log formatting and memory usage messages
Need file or parameter validation detailsscripts/validation_utils.RSee path checks, output-directory checks, and scalar validation
Need timeout, temp workspace, or session info behaviorscripts/runtime_utils.RSee timeout control, temp cleanup, output copying, and session-info export
Need expression/group input handlingscripts/input_functions.RSee CSV loading, sample matching, and label extraction
Need clustering logicscripts/clustering_functions.RSee distance calculation and hclust() generation
Need output-writing logicscripts/output_utils.RSee CSV export and PDF rendering
Encounter errors, warnings, or unexpected clustering patternsreferences/troubleshooting.mdCommon failures, warning follow-up, and interpretation guidance
Need CLI examples or common parameter combinationsreferences/cli-guide.mdDetailed command patterns for standard, variant, and test runs
Need example input files or schema-concrete fixturestests/data/Inspect sample CSV layouts for expression and group inputs
Need expected output names or artifact formats## Output Files and references/cli-guide.mdConfirm the files the workflow writes and inspect documented example previews
Need to run regression teststests/run_tests.RExecute the automated test suite
Need exact test assertions or edge casestests/testthat/test-clustering.RInspect validation, reproducibility, and output checks

Usage

bash
Rscript scripts/main.R \
  --input_file ./expression_matrix.csv \
  --group_file ./sample_groups.csv \
  --output_dir ./output/ \
  --distance_method euclidean \
  --linkage_method complete \
  --label_column batch \
  --timeout_seconds 300 \
  --seed 42

Arguments

ShortLongTypeDefaultDescription
-i--input_filecharacterrequiredExpression matrix file (features as rows, samples as columns)
-g--group_filecharacterrequiredSample annotation file (first column sample ID, one metadata column for labels)
-o--output_dircharacter./output/Output directory
-d--distance_methodcharactereuclideanDistance metric for dist(): euclidean, maximum, manhattan, canberra, binary, minkowski
-m--linkage_methodcharactercompleteLinkage method for hclust(): complete, single, average, mcquitty, median, centroid, ward.D, ward.D2
-l--label_columncharactersecond columnColumn used as dendrogram labels
-c--label_cexnumeric0.8Dendrogram label size, must be > 0
-t--timeout_secondsinteger300Elapsed time limit in seconds, must be > 0
-s--seedinteger42Random seed for reproducibility

Input Format

Expression Matrix (input_file)

Features as rows, samples as columns, CSV format with feature IDs in the first column.

csv
,Sample01,Sample02,Sample03
TSPAN6,1.847876677,1.831755661,3.827625975
TNMD,0.034919984,0.053250385,1.388850793

Requirements:

  • The first column contains unique feature IDs.
  • All sample columns must be numeric.
  • Sample column names must be unique and non-empty.
  • At least two matched samples are required.
Sample Annotation (group_file)

CSV with sample IDs in the first column. The second column is used by default for leaf labels unless --label_column is provided.

csv
sample,batch
Sample01,batch1
Sample02,batch2
Sample03,batch1

Requirements:

  • Sample IDs must match expression matrix column names exactly.
  • The selected label column must exist and contain no empty values.
  • The file must contain at least one metadata column in addition to sample IDs.

Output Files

FileDescription
hierarchical_clustering_plot.pdfSample dendrogram plot
sample_distance_matrix.csvPairwise sample distance matrix
clustering_order.csvLeaf order shown in the dendrogram
matched_samples.csvSample-to-label table used for plotting
session_info.txtR session and package version info

Workflow

Step 1: Validate Input

WHEN checking file or parameter validation, READ: scripts/validation_utils.R

WHEN checking expression/group CSV handling, READ: scripts/input_functions.R

  • Check file existence
  • Reject empty files before parsing
  • Read the expression matrix and sample annotation CSV files
  • Validate required columns, unique IDs, and numeric expression values
Step 2: Align Samples

WHEN checking sample matching logic, READ: scripts/input_functions.R

  • Match sample IDs between the annotation file and expression matrix
  • Reorder matrix columns to the annotation file order
  • Select the label column used for plotting
Step 3: Build Hierarchical Clustering

WHEN interpreting distance or linkage behavior, READ: references/algorithm.md

WHEN checking clustering implementation, READ: scripts/clustering_functions.R

  • Transpose the expression matrix to sample-by-feature form
  • Compute pairwise sample distances with dist()
  • Build the dendrogram with hclust()
Show full SKILL.md (469 more words)Show less
Step 4: Save Outputs

WHEN checking output staging and cleanup behavior, READ: scripts/run_analysis.R

WHEN checking PDF/CSV export behavior, READ: scripts/output_utils.R

WHEN checking timeout, session info, or final file copy behavior, READ: scripts/runtime_utils.R

  • Stage outputs in a temporary workspace
  • Export the pairwise distance matrix
  • Export the plotted leaf order
  • Render the dendrogram as PDF
  • Copy finalized outputs into the requested output directory

Methods

Distance Matrix

Sample distances are computed from the transposed expression matrix using base R dist().

Hierarchical Clustering

The clustering tree is built with base R hclust(). The default linkage method is complete, matching the source analysis script.

Examples

Basic Usage
bash
Rscript scripts/main.R \
  -i tests/data/sample_expression_matrix.csv \
  -g tests/data/sample_groups.csv \
  -o ./output/ \
  -t 300
Use Sample IDs as Labels
bash
Rscript scripts/main.R \
  -i tests/data/sample_expression_matrix.csv \
  -g tests/data/sample_groups.csv \
  -o ./output_sample_labels/ \
  -l sample
Use Average Linkage
bash
Rscript scripts/main.R \
  -i tests/data/sample_expression_matrix.csv \
  -g tests/data/sample_groups.csv \
  -o ./output_average/ \
  -m average

Error Handling

Common Errors
ErrorCauseSolutionRead More
SKILL_DEPENDENCY_MISSINGRequired R package is not installedInstall the missing package and rerunreferences/troubleshooting.md#skill_dependency_missing
SKILL_FILE_NOT_FOUNDInput file does not exist or output directory could not be createdCheck the path and permissionsreferences/troubleshooting.md#skill_file_not_found
SKILL_EMPTY_FILEInput file is emptyRe-export the CSV and confirm it contains datareferences/troubleshooting.md#skill_empty_file
SKILL_EMPTY_DATACSV parsed successfully but contains no data rowsConfirm the CSV has at least one data rowreferences/troubleshooting.md#skill_empty_data
SKILL_PARSE_ERRORCSV parsing failedCheck encoding, delimiters, and CSV structurereferences/troubleshooting.md#skill_parse_error
SKILL_MISSING_COLUMNSExpected columns or headers are missingCheck CSV headers and metadata columnsreferences/troubleshooting.md#skill_missing_columns
SKILL_INVALID_TYPEExpression values or parameters have the wrong typeEnsure numeric fields are numericreferences/troubleshooting.md#skill_invalid_type
SKILL_SAMPLE_MISMATCHSample IDs do not matchEnsure the first column in group_file matches matrix column namesreferences/troubleshooting.md#skill_sample_mismatch
SKILL_INVALID_DATAExpression or annotation data is malformedCheck duplicate IDs, missing labels, and numeric valuesreferences/troubleshooting.md#skill_invalid_data
SKILL_INVALID_PARAMETERUnsupported distance, linkage, or label parameterUse one of the documented parameter valuesreferences/troubleshooting.md#skill_invalid_parameter
SKILL_TIMEOUTAnalysis exceeded the time limitIncrease --timeout_seconds and rerunreferences/troubleshooting.md#skill_timeout
SKILL_PLOT_ERRORPlot device failed while writing PDFCheck output directory permissions and rerunreferences/troubleshooting.md#skill_plot_error
SKILL_WRITE_ERROROutput or intermediate files could not be writtenCheck output directory permissions and free disk spacereferences/troubleshooting.md#skill_write_error
SKILL_WARNINGNon-fatal warning occurred during executionInspect console warnings and verify output qualityreferences/troubleshooting.md#skill_warning
SKILL_MEMORY_WARNINGMemory usage exceeded the warning thresholdReduce input size or rerun with more memoryreferences/troubleshooting.md#skill_memory_warning

IF error persists, READ: references/troubleshooting.md

Testing

Test with Sample Data
bash
# Check help
Rscript scripts/main.R --help

# Run with sample data
Rscript scripts/main.R \
  -i tests/data/sample_expression_matrix.csv \
  -g tests/data/sample_groups.csv \
  -o ./output/

# Run unit tests (requires testthat and data.table)
Rscript tests/run_tests.R
Validation Commands
bash
# Check main output plot exists
ls -la ./output/hierarchical_clustering_plot.pdf

# Inspect clustering order
wc -l ./output/clustering_order.csv

Implementation Checklist

  • CLI parsing with optparse
  • set.seed() for reproducibility
  • Input validation (file existence, emptiness, types, required columns)
  • Try-catch based fatal error handling
  • Standardized SKILL_* error classification
  • Timeout control with setTimeLimit()
  • Standardized console-only logging
  • Base R clustering implementation
  • Session info recording with sink()
  • Temporary workspace cleanup with on.exit()
  • Memory usage reporting with gc()
  • File reading instructions in SKILL.md
  • Modular script structure across scripts/
  • Test template added under tests/testthat/
  • Test data provided
  • Error handling with SKILL_* codes
  • get_script_dir() defined before use
  • Scripts in scripts/ directory
  • References in references/ directory

Last updated: 2026-04-16 | Version: 1.0.0

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 18 other files (scripts, references) in awesome-med-research-skills/Data Analysis/hierarchical-clustering-plot of aipoch/medical-research-skills.

  • SKILL.md
  • DESCRIPTION
  • eval_report_hierarchical-clustering-plot_result.json
  • references/algorithm.md
  • references/cli-guide.md
  • references/troubleshooting.md
  • scripts/clustering_functions.R
  • scripts/input_functions.R
  • scripts/logging_utils.R
  • scripts/main.R
  • scripts/output_utils.R
  • scripts/run_analysis.R
  • scripts/runtime_utils.R
  • scripts/validation_utils.R
  • tests/data/sample_expression_matrix.csv
  • tests/data/sample_groups.csv
  • tests/run_tests.R
  • … and 2 more

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Hierarchical Clustering Plot next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Hierarchical Clustering Plot

What does Hierarchical Clustering Plot do?

A skill your agent uses when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample…. Hierarchical Clustering Plot is an agent skill from aipoch/medical-research-skills. Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment.

When should I use Hierarchical Clustering Plot?

Hierarchical Clustering Plot fits situations like: building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table; especially for QC; batch inspection; sample similarity assessment.

How do I install Hierarchical Clustering Plot in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill hierarchical-clustering-plot -a claude-code`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/hierarchical-clustering-plot in aipoch/medical-research-skills) into .claude/skills/hierarchical-clustering-plot in your project. Claude Code loads it when a task matches its description.

How do I install Hierarchical Clustering Plot in Codex?

Run `npx skills add aipoch/medical-research-skills --skill hierarchical-clustering-plot -a codex`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/hierarchical-clustering-plot in aipoch/medical-research-skills) into .agents/skills/hierarchical-clustering-plot in your project. Codex loads it when a task matches its description.

Can I use Hierarchical Clustering Plot in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill hierarchical-clustering-plot -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hierarchical-clustering-plot, .gemini/skills/hierarchical-clustering-plot, .github/skills/hierarchical-clustering-plot and .opencode/skills/hierarchical-clustering-plot in your project.

What does Hierarchical Clustering Plot need to run?

Going by SKILL.md and its folder, Hierarchical Clustering Plot needs R for the scripts in its folder.

Does Hierarchical Clustering Plot access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hierarchical Clustering Plot safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Hierarchical Clustering Plot use?

Hierarchical Clustering Plot is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hierarchical Clustering Plot use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.2k tokens, read only when the agent opens those files.

What are the alternatives to Hierarchical Clustering Plot?

Skills that share tags, products or a category with Hierarchical Clustering Plot: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 32k stars), deepTools NGS Toolkit (davila7/claude-code-templates, 32k stars), Bulkrna Cosinor Rhythm (TianGzlab/OmicsClaw, 161 stars) and PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hierarchical Clustering Plot?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.