epigen/unsupervised_analysis

A general purpose Snakemake workflow and MrBiomics module to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

Overview

Latest release: v4.0.1, Last update: 2026-05-18

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=epigen/unsupervised_analysis

Quality control: linting: passed formatting: passed

Topics: data-science high-dimensional-data snakemake workflow unsupervised-learning principal-component-analysis umap pca visualization clustering data-visualization dimensionality-reduction heatmap densmap cluster-analysis cluster-validation clustering-algorithm clustree leiden-algorithm

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/epigen/unsupervised_analysis . --tag v4.0.1

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration

You need one configuration file to configure the analyses and one annotation file describing the data to run the complete workflow. If in doubt read the comments in the config and/or try the default values. We provide a full example including data and configuration in config/ and test/ as a starting point.

  • project configuration (config/config.yaml): Different for every project and configures the analyses to be performed.

  • sample annotation (config/annotation.csv): CSV file consisting of four mandatory columns.

    • name: A unique name for the dataset (tip: keep it short but descriptive).

    • data: Path to the tabular data as a comma-separated table (CSV).

    • metadata: Path to the metadata as a comma-separated table (CSV) with the first column being the index/identifier of each observation/sample and every other column metadata for the respective observation (either numeric or categorical, not mixed). No NaN or empty values allowed, and no special characters (all except a-z, 0-9, _) in the index.

    • samples_by_features: Boolean indicator if the data matrix is observations/samples (rows) x features (columns): 0==no, 1==yes.

Set workflow-specific resources or command line arguments (CLI) in the workflow profile workflow/profiles/default.config.yaml, which supersedes global Snakemake profiles.

Provided JSON Schemas for the workflow config and annotation files are used by Snakemake for validation before execution.

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter

Type

Description

Required

Default

mem

integer

Memory limit in MB used by workflow rules.

32000

threads

integer

Default thread count used by workflow rules.

2

annotation

string

Path to the sample annotation CSV file.

yes

result_path

string

Base output directory for workflow results.

yes

project_name

string

Short project identifier used in reports and exported config copies.

yes

pca

yes

. n_components

Number of components, explained variance fraction, or “mle”.

yes

. svd_solver

string

yes

umap

yes

. metrics

array

yes

. n_neighbors

array

yes

. min_dist

array

yes

. n_components

array

yes

. densmap

integer

yes

. connectivity

integer

yes

. diagnostics

integer

yes

heatmap

yes

. metrics

array

yes

. hclust_methods

array

yes

. n_observations

yes

. n_features

yes

leiden

yes

. metrics

array

yes

. n_neighbors

array

yes

. partition_types

array

yes

. resolutions

array

yes

. n_iterations

integer

yes

clustree

yes

. count_filter

integer

yes

. prop_filter

number

yes

. layout

string

yes

. categorical_label_option

string

yes

. numerical_aggregation_option

string

yes

sample_proportion

number

Set to 0 to skip internal cluster validation.

yes

metadata_of_interest

array

yes

[]

coord_fixed

integer

yes

scatterplot2d

yes

. size

number

yes

. alpha

number

yes

features_to_plot

array

yes

[]

Linting and formatting

Linting results
All tests passed!
Formatting results
All tests passed!