gabrielmoreira Consensus Clustering Analysis
When to Use
Use this skill when you need to identify stable sample subtypes from a bulk expression matrix with ConsensusClusterPlus, compare candidate clustering settings with PAC, and export consensus matrix/CDF visualizations.
Do not use this skill for differential expression analysis, single-cell clustering, or non-expression tabular data.
When to Read External Files
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md |
Consensus clustering, PAC scoring, and preprocessing assumptions |
| Need to run analysis | scripts/main.R |
Execute: Rscript scripts/main.R --input_file ... --group_file ... |
| Encounter errors | references/troubleshooting.md |
Common errors and solutions |
| Need CLI examples | references/cli-guide.md |
Detailed CLI usage examples with verified local runs |
Usage
Rscript scripts/main.R \
--input_file ./expression_matrix.csv \
--group_file ./groups.csv \
--disease_group case \
--max_k 4 \
--output_dir ./output/ \
--gene_selection highly_variable \
--top_n 5000 \
--reps 1000 \
--p_item 0.8 \
--p_feature 1.0 \
--timeout_seconds 3600 \
--seed 42
Arguments
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-i |
--input_file |
character | required | Expression matrix file (genes as rows, samples as columns) |
-g |
--group_file |
character | required | Group information file (sample ID + group columns) |
-d |
--disease_group |
character | case |
Group label retained for clustering |
-k |
--max_k |
integer | 4 |
Maximum cluster count to evaluate |
-o |
--output_dir |
character | ./output/ |
Output directory |
-m |
--gene_selection |
character | highly_variable |
Gene selection mode: highly_variable or custom |
-n |
--top_n |
integer | 5000 |
Number of top variable genes to keep |
-l |
--gene_list |
character | NULL |
Custom gene list file when gene_selection=custom |
-c |
--center_data |
logical | TRUE |
Median-center each gene before clustering |
-r |
--reps |
integer | 1000 |
Consensus resampling repetitions |
--p_item |
double | 0.8 |
Sample resampling proportion | |
--p_feature |
double | 1.0 |
Feature resampling proportion | |
-t |
--timeout_seconds |
integer | 3600 |
Elapsed timeout in seconds |
-s |
--seed |
integer | 42 |
Random seed for reproducibility |
Input Format
Expression Matrix (input_file)
Genes as rows, samples as columns, CSV/TSV/TXT format with gene ID in the first column.
,Sample01,Sample02,Sample03
TSPAN6,1.8479,1.8318,3.8276
TNMD,0.0349,0.0533,1.3889
Group File (group_file)
Delimited text file with sample ID and group columns.
sample,group
Sample01,case
Sample02,control
Sample03,case
Gene List (gene_list)
Optional plain text or single-column CSV file with one gene symbol per line.
TNMD
DPM1
SCYL3
Output Files
| File | Description |
|---|---|
Cluster_res.csv |
PAC summary for each distance/algorithm combination with is_best marking the selected model |
genes_for_clustering.csv |
Selected genes and gene selection mode |
samples_for_clustering.csv |
Samples retained after disease-group filtering |
result_<distance>_<algorithm>/ |
Method-specific consensus outputs and PAC_scores.csv |
Consensus Matrix Plot.pdf |
Consensus matrix heatmap for the optimal model |
CDF curve Plot.pdf |
CDF curves for the optimal method |
session_info.txt |
R session and package version info |
Workflow
Step 1: Validate Input
- Check file existence
- Detect sample and group columns in the group file
- Validate sample matching between expression matrix and group file
Step 2: Prepare Clustering Matrix
- Filter samples by the requested disease group
- Select genes using
highly_variableorcustom - Median-center genes if requested
Step 3: Run Consensus Clustering
- Evaluate supported distance and clustering algorithm combinations
- Compute PAC scores across candidate K values
- Select the optimal model by minimum PAC
Step 4: Generate Outputs
- Save result tables
- Generate consensus matrix and CDF plots
- Record session information for reproducibility
Methods
ConsensusClusterPlus
Repeated subsampling is used to estimate cluster stability across candidate K values and clustering settings.
PAC Score
The proportion of ambiguous clustering is computed as CDF(0.9) - CDF(0.1) from lower-triangle consensus values. Lower PAC indicates more stable clustering.
Gene Selection
highly_variable: rank genes by median absolute deviationcustom: use the intersection of the provided gene list and matrix row names
Examples
Basic Usage
Rscript scripts/main.R \
-i expression_matrix.csv \
-g groups.csv \
-d case \
-k 3 \
-r 20 \
-o output/example_basic \
-t 120
With a Custom Gene List
Rscript scripts/main.R \
-i expression_matrix.csv \
-g groups.csv \
-d case \
-m custom \
-l genes.csv \
-k 4 \
-r 20 \
-o output/example_custom \
-t 120
Without Median Centering
Rscript scripts/main.R \
-i expression_matrix.csv \
-g groups.csv \
-d case \
-c FALSE \
-k 3 \
-r 20 \
-o output/example_rawscale \
-t 120
Error Handling
Common Errors
| Error | Cause | Solution |
|---|---|---|
SKILL_FILE_NOT_FOUND |
Input file does not exist | Check file path and permissions |
SKILL_MISSING_COLUMNS |
Group file lacks sample/group columns | Verify column names in the group file |
SKILL_SAMPLE_MISMATCH |
Sample names do not match | Ensure group file sample IDs match matrix columns |
SKILL_INVALID_PARAMETER |
CLI value is invalid | Check allowed options and numeric ranges |
SKILL_INVALID_DATA |
Too few samples/genes remain after filtering | Lower max_k or review the input data |
SKILL_TIMEOUT |
Run exceeded the configured timeout | Increase timeout_seconds or reduce reps |
SKILL_DEPENDENCY_MISSING |
Required R package is not installed | Install missing packages before rerunning |
IF error persists, READ: references/troubleshooting.md
Testing
Smoke Check
# Check help
Rscript scripts/main.R --help
# Run analysis
Rscript scripts/main.R \
-i tests/data/expression_matrix.csv \
-g tests/data/groups.csv \
-d case \
-k 3 \
-r 20 \
-o output/example_basic \
-t 120
Validation Commands
# Inspect selected model
cat output/example_basic/Cluster_res.csv
# Check output plots exist
ls -la output/example_basic
Implementation Checklist
- CLI parsing with
optparse -
set.seed()for reproducibility -
requireNamespace()dependency checks - Session info recording
-
data.table::fread()input reading - File reading instructions in
SKILL.md - Modular script structure (<150 lines per file)
- Test data provided
- Error handling with
SKILL_*codes - Scripts in
scripts/directory - References in
references/directory
Last updated: 2026-04-17 | Version: 1.0.0