FilippoGas/GeneProgramsProfiler

Characterize cell type specific gene program deregulations from scRNAseq datasets.

Overview

Latest release: None, Last update: 2026-08-16

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=FilippoGas/GeneProgramsProfiler

Quality control: linting: passed formatting: failed

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/FilippoGas/GeneProgramsProfiler . --tag None

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 using apptainer/singularity, use

snakemake --cores all --sdm apptainer

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

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.

Workflow parameters

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

Parameter

Type

Description

Required

Default

scRNAseq

string

yes

celltype_conversion_dictionary

string

Path to json dictionary to match celltype in the dataset to celltypes in cytopus

yes

analysis_name

string

yes

queues

. cpu

string

. gpu

string

case_condition

string

yes

control_condition

string

yes

preprocess

. annotate_and_save

. . celltype_annotation_colname

string

Name of celltype annotation column in the seurat object’s metadata

yes

. . cores

integer

yes

. . rstudio_memory

integer

Memory occupied by loading the scRNAseq dataset on R

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . sample_column

string

Column of sample name in the Seurat metadata

yes

. . condition_column

string

Column of condition name in the Seurat metadata

yes

spectra

. prepare_cytopus_list

. . mem_mb

integer

Memory required to donwload and save the required cytopus gene sets

yes

. . cores

integer

yes

. . global_celltype

string

cell type to use as global celltype in the cytopus list. For more details visit https://github.com/wallet-maker/cytopus

. . time

string

Job walltime, if required by computing infrastructure.

. run_spectra

. . lambda

number

weighs relative contribution of cytopus list and expression loss functions. For more details visit https://github.com/dpeerlab/spectra

yes

. . cores

integer

yes

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. rename_programs

. . cores

integer

yes

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. spectra_WMW

. . cores

integer

yes

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. spectra_plots

. . cores

integer

yes

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . effect_size_thresh

number

Effect size threshold for Wilcoxon-Mann-Whitney U-test’s rank-biserial correlation

yes

. . FDR_thresh

number

False Discovery Rate threshold for Wilcoxon-Mann-Whitney U-test

yes

DE_analysis

. run_DE_analysis

. . cores

integer

yes

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . logFC

number

log(Fold Change) threshold to consider a gene to be differentially expressed

yes

. . FDR

number

False Discovery Rate threshold to consider a gene to be differentially expressed

yes

. DEA_plots

. . cores

integer

yes

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

functional_enrichment

. run_gsea

. . cores

integer

yes

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . padj_thresh

number

Value to use as threshold for adjusted pvalue to consider fgsea results significant.

yes

. run_ora

. . cores

integer

. . mem_mb

integer

. . time

string

Job walltime, if required by computing infrastructure.

. . padj_thresh

number

cNMF

. cNMF_prepare

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . cores

integer

yes

. . max_nmf_iter

integer

maximum number of optimization iterations that the underlying Non-negative Matrix Factorization (NMF) solver is allowed to perform in order to reach convergence during a single factorization replicate.

yes

. . k_min

integer

Minimum value of k to try

yes

. . k_max

integer

Maximum value of k to try

yes

. . k_step

integer

yes

. . n_iter

integer

Number of iterations for each k

yes

. cNMF_factorize_worker

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . cores

integer

yes

. cNMF_combine

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . cores

integer

yes

. cNMF_k_selection_plot

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . cores

integer

yes

. extract_best_k

. . mem_mb

integer

yes

. . time

string

Job walltime, if required by computing infrastructure.

. . cores

integer

yes

Linting and formatting

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