scRNAseq Analysis Recipe

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

flowchart LR;
    ngs_fetch-->scrnaseq_processing_seurat;
    scrnaseq_processing_seurat-->genome_tracks;
    scrnaseq_processing_seurat-->unsupervised_analysis;
    scrnaseq_processing_seurat-->dea_seurat;
    unsupervised_analysis-->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. Genome Browser Track Visualization for quality control and visual analysis of genomic regions of interest or top hits.
4. Unsupervised Analysis to understand and visualize similarities and variations across cells, including dimensionality reduction and cluster analysis.
5. Differential Analysis using Seurat to identify and visualize statistically significantly differentially expressed genes between groups.
6. Enrichment Analysis for biomedical interpretation of differential analysis results using prior knowledge.

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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