scCRISPRseq Analysis Recipe

The scCRISPR-seq Analysis Recipe takes you from raw count matrices to enrichment analysis results of your differentially expressed genes to characterize knockout (KO) phenotypes while providing unsupervised analyses and genome browser tracks for visualization and quality control.

flowchart LR;
    ngs_fetch-->scrnaseq_processing_seurat;
    scrnaseq_processing_seurat-->unsupervised_analysis;
    scrnaseq_processing_seurat-->mixscape_seurat;
    mixscape_seurat-->genome_tracks;
    mixscape_seurat-->unsupervised_analysis;
    mixscape_seurat-->dea_seurat;
    dea_seurat-->enrichment_analysis;

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Modules

The following Modules are used in this Recipe:

1. (optional) Fetch publicly available bulk ATAC-seq data (coming soon).
2. scRNA-seq Data Processing & Visualization for processing and preparing a Seurat object for all downstream analyses.
3. Unsupervised Analysis to understand and visualize similarities and variations across cells, including dimensionality reduction and cluster analysis.
4. Perturbation Analysis using Mixscape from Seurat to identify perturbed cells from pooled (multimodal) CRISPR screens with sc/snRNA-seq read-out (scCRISPR-seq).
5. Genome Browser Track Visualization for quality control and visual analysis of genomic regions of interest (KOs) or top hits.
6. Differential Analysis using Seurat to identify and visualize statistically significantly differentially expressed genes due to perturbations (e.g., KO).
7. Enrichment Analysis for biomedical interpretation of differential analysis results using prior knowledge.

• add pseudobulk analysis as orthogonal analysis that adds modeling capabilities

Strategy

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Templates for a Methods section of a scientific publication can be found in each Module's README.

Data

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Code & Configuration

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Results

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