https://github.com/epigen/mixscape_seurat
A Snakemake workflow and MrBiomics module for performing perturbation analyses of pooled (multimodal) CRISPR screens with sc/snRNA-seq read-out (scCRISPR-seq) powered by the R package Seurat's method Mixscape.
https://github.com/epigen/mixscape_seurat
bioinformatics biomedical-data-science perturbation-analysis sccrispr-seq scrna-seq single-cell snakemake snrna-seq visualization workflow
Last synced: 2 months ago
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A Snakemake workflow and MrBiomics module for performing perturbation analyses of pooled (multimodal) CRISPR screens with sc/snRNA-seq read-out (scCRISPR-seq) powered by the R package Seurat's method Mixscape.
- Host: GitHub
- URL: https://github.com/epigen/mixscape_seurat
- Owner: epigen
- License: mit
- Created: 2022-04-14T14:26:34.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-20T15:56:44.000Z (6 months ago)
- Last Synced: 2025-04-12T00:13:10.098Z (2 months ago)
- Topics: bioinformatics, biomedical-data-science, perturbation-analysis, sccrispr-seq, scrna-seq, single-cell, snakemake, snrna-seq, visualization, workflow
- Language: R
- Homepage: https://epigen.github.io/mixscape_seurat/
- Size: 113 KB
- Stars: 19
- Watchers: 4
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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# scCRISPR-seq Perturbation Analysis Workflow using Seurat's Mixscape
A [Snakemake 8](https://snakemake.readthedocs.io/en/stable/) workflow for performing perturbation analyses of pooled (multimodal) CRISPR screens with scRNA-seq read-out (scCRISPR-seq, CROP-seq, Perturb-seq) powered by the R package [Seurat's](https://satijalab.org/seurat/index.html) method [Mixscape](https://satijalab.org/seurat/articles/mixscape_vignette.html).
> [!NOTE]
> This workflow adheres to the module specifications of [MrBiomics](https://github.com/epigen/MrBiomics), an effort to augment research by modularizing (biomedical) data science. For more details, instructions, and modules check out the project's repository.
>
> ⭐️ **Star and share modules you find valuable** 📤 - help others discover them, and guide our future work!
> [!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.8424761](https://doi.org/10.5281/zenodo.8424761).**
# Authors
- [Stephan Reichl](https://github.com/sreichl)
- [Christoph Bock](https://github.com/chrbock)
# 💿 Software
This project wouldn't be possible without the following software and it's dependencies:
| Software | Reference (DOI) |
| :------------: | :-----------------------------------------------: |
| data.table | https://r-datatable.com |
| ggplot2 | https://ggplot2.tidyverse.org/ |
| Mixscape | https://doi.org/10.1038/s41588-021-00778-2 |
| mixtools | https://CRAN.R-project.org/package=mixtools |
| patchwork | https://CRAN.R-project.org/package=patchwork |
| Seurat | https://doi.org/10.1016/j.cell.2021.04.048 |
| Snakemake | https://doi.org/10.12688/f1000research.29032.2 |
# 🔬 Methods
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 (`mixscape_seurat/envs/*.yaml`). Parameters that have to be adapted depending on the data or workflow configurations are denoted in squared brackets e.g., [X].
The outlined analyses were performed using the R package Seurat (ver) [ref] unless stated otherwise.
**Mixscape**. We applied the Mixscape workflow [ref], implemented in Seurat, on each [sample] separately as well as all [samples] simultaneously to identify perturbed cells compared to non-targeting (NT) guide RNA (gRNA) assigned cells. Briefly, cells putatively assigned to a gRNA and respective knockout (KO) target gene in conjunction with NT cells were used to calculate cell-wise perturbation signatures by using Seurat::CalcPerturbSig to subtract the average expression profile of the [n_neighbors] closest NT cells in [ndims]-dimensional PCA space. Using Seurat::RunMixscape, with a log2(fold change) threshold of [lfc_th] and a minimum of [min_de_genes] differentially expressed genes, cells were classified as perturbed or non-perturbed using posterior probabilities of an expectation-maximization (EM) algorithm for mixtures of univariate normals, assuming each putatively annotated target gene group is a mixture of two Gaussian distributions (perturbed signal and non-perturbed background).
**Visualizations**. Statistics of the Mixscape classification of perturbed cells versus cells with no detectable perturbation on a target gene and gRNA basis using barplots. Perturbation scores of cells split by their Mixscape classification as density plots. Posterior probability values of non-perturbed and perturbed cells as violin plots using the Seurat function VlnPlot. Perturbation scores and posterior probabilities were additionally plotted split by replicates [split_by_col] and experiment conditions [split_by_col]. For the visualization of protein surface expression measured by Antibody Capture technologies the Seurat function VlnPlot for violin plots split by perturbation classification of cells was used.
**Linear discriminant analysis (LDA)**. LDA was applied on the perturbation signatures of all perturbed and NT cells using Seurat::MixscapeLDA with number of principal components [npcs] per KO class to find the most discriminative subspace, given the KO/NT classes, to project the data into and visualized in two dimensions using UMAP with Seurat::RunUMAP.
**The analysis and visualizations described here were performed using a publicly available Snakemake [ver] (ref) workflow [10.5281/zenodo.8424761](https://doi.org/10.5281/zenodo.8424761).**
# 🚀 Features
The workflow performs all steps of the [Mixscape Vignette](https://satijalab.org/seurat/articles/mixscape_vignette.html) on all samples in the annotation file according to the parametrization in the config file.
- Calculation of local perturbation signatures (`{analysis}/`)
- all and filtered (i.e., only pertubed cells) perturbation signatures (`{ALL|FILTERED}_PRTB_data.csv`).
- Mixscape classification of perturbed cells versus cells with no detectable perturbation (`{analysis}/{ALL|FILTERED}_*`)
- Mixscape classification statistics (`{analysis}/mixscape_stats.csv`).
- Visualization of Mixscape results (`{analysis}/plots/`)
- Statistics of the Mixscape classification on a target gene and guide RNA basis as bar plots (`stats/{KO}.png').
- Perturbation scores of cells split by their mixscape classification as density plots (`PerturbScore/{KO}_{split}.png').
- Posterior probability values in non perturbed and perturbed cells as violin plots (`PosteriorProbability/{KO}_{split}.png').
- (optional) if Antibody Capture was used: Surface protein expression measurements split by perturbation classification of cells as violin plots (`{Antibody_Capture_flag}_expression/{protein}.png').
- Analysis of perturbation responses with Linear Discriminant Analysis (LDA)
- LDA components (`LDA_data.csv`)
- 2D visualization using UMAP as scatter plot (`{analysis}/plots/LDA_UMAP`).
# 🛠️ Usage
Read the [Mixscape Vignette](https://satijalab.org/seurat/articles/mixscape_vignette.html).
# ⚙️ Configuration
Detailed specifications can be found here [./config/README.md](./config/README.md)
# 📖 Example
--- COMING SOON ---
# 🔗 Links
- [GitHub Repository](https://github.com/epigen/mixscape_seurat/)
- [GitHub Page](https://epigen.github.io/mixscape_seurat/)
- [Zenodo Repository](https://doi.org/10.5281/zenodo.8424761)
- [Snakemake Workflow Catalog Entry](https://snakemake.github.io/snakemake-workflow-catalog?usage=epigen/mixscape_seurat)
# 📚 Resources
- Recommended compatible [MrBiomics](https://github.com/epigen/MrBiomics) modules for
- upstream processing:
- [scRNA-seq Data Processing & Visualization](https://github.com/epigen/scrnaseq_processing_seurat) for processing (multimodal) single-cell transcriptome data.input.
- downstream analyses:
- [Unsupervised Analysis](https://github.com/epigen/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.
- [Differential Analysis using Seurat](https://github.com/epigen/dea_seurat) to identify and visualize statistically significantly different features (e.g., genes or proteins) between groups.
- [Enrichment Analysis](https://github.com/epigen/enrichment_analysis) for biomedical interpretation of (differential) analysis results using prior knowledge.
- Mixscape publication: [Papalexi et al. (2021) Nature Genetics - "Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens."](https://doi.org/10.1038/s41588-021-00778-2).
# 📑 Publications
The following publications successfully used this module for their analyses.
- [FirstAuthors et al. (202X) Journal Name - Paper Title.](https://doi.org/10.XXX/XXXX)
- ...
# ⭐ Star History
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