A Snakemake 8 workflow for performing and visualizing analyses of data (e.g., ...) powered by the package package.
Note
This workflow adheres to the module specifications of MrBiomics, an effort to augment research by modularizing (biomedical) data science. For more details, instructions, and modules check out the project's repository.
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Important
If you use this workflow in a publication, please don't forget to give credit to the authors by citing it using this DOI 10.5281/zenodo.XXXXXX.
This project wouldn't be possible without the following software and their dependencies.
| Software | Reference (DOI) |
|---|---|
| Snakemake | https://doi.org/10.12688/f1000research.29032.2 |
| packageA | https://doi.org/10.AAA/ |
| packageB | https://doi.org/10.BBBB/ |
This is a template for the Methods section of a scientific publication and is intended to serve as a starting point. Only retain paragraphs relevant to your analysis. References [ref] to the respective publications are curated in the software table above. Versions (ver) have to be read out from the respective conda environment specifications (workflow/envs/*.yaml file) or post-execution in the result directory ({module}/envs/*.yaml). Parameters that have to be adapted depending on the data or workflow configurations are denoted in squared brackets e.g., [X].
Analysis. Analysis was performed...
Visualization. The results were visualized...
The analysis and visualizations described here were performed using a publicly available Snakemake (ver) [ref] workflow [ref - cite this workflow here].
The workflow performs the following steps that produce the outlined results:
- Analysis
- ...
- (optional) ...
- Visualizations
- ...
- Limitations
- ...
Here are some tips for the usage of this workflow:
- ...
Detailed specifications can be found here ./config/README.md
Explore detailed examples showcasing module usage in comprehensive end-to-end analyses (including data, configuration, annotation and results) in our MrBiomics Recipes:
- Recommended compatible MrBiomics Modules for up-/downstream analyses:
- Unsupervised Analysis to understand and visualize similarities and variations between cells/samples, including dimensionality reduction and cluster analysis. Useful for all tabular data including single-cell and bulk sequencing data.
- Split, Filter, Normalize and Integrate Sequencing Data after count quantification.
- Differential Analysis with limma to identify and visualize statistically significantly different features (e.g., genes or genomic regions) between sample groups.
- Enrichment Analysis for biomedical interpretation of (differential) analysis results using prior knowledge.
- Genome Browser Track Visualization for quality control and visual inspection/analysis of genomic regions/genes of interest or top hits.
- ATAC-seq Data Processing & Quantification Pipeline for processing, quantification and annotation of chromatin accessibility.
- scRNA-seq Data Processing & Visualization for processing (multimodal) single-cell transcriptome data.
- Differential Analysis using Seurat to identify and visualize statistically significantly different features (e.g., genes or proteins) between groups.
- Perturbation Analysis using Mixscape from Seurat to identify perturbed cells from pooled (multimodal) CRISPR screens with sc/snRNA-seq read-out (scCRISPR-seq).
The following publications successfully used this module for their analyses.