/\
/ \ P R I S M S C
/____\ [ rna | atac | wnn ]
PrismSC is a "one-stop" clinical single-cell cohort processing workflow designed for secure, reproducible, and scalable analysis of joint scRNA-seq and scATAC-seq datasets. It integrates quality control, batch correction, automated cell annotation, lineage fate mapping, and clinical diagnostics HTML reporting.
PrismSC is designed to answer key clinical questions:
- Cellular Composition Analysis: Automatically identify and quantify cell populations across multiple patient cohorts under different clinical conditions (e.g., Healthy vs. Inflamed vs. Post-Treatment).
- Multi-Modal Integration: Fuse transcriptomic and chromatin accessibility profiles using Weighted Nearest Neighbors (WNN) or probabilistic deep generative models (MultiVI, scVI).
- Automated Annotation: Eliminate manual annotation bias by using pre-trained CellTypist immune classifiers.
- Developmental Dynamics & Fate Mapping: Use CellRank 2 (GPCCA) and PAGA to map cell transition probabilities and fate commitments along differentiation trajectories (e.g., Monocyte subset maturation).
- Interactive Clinical Reporting: Consolidate QC statistics, modality projections, cell proportions, and fate mapping drivers into a portable, clinician-ready HTML report.
Cohort Manifest (TSV)
│
▼
┌──────────────────────────────────────────────┐
│ Module 1: Preprocessing & Doublet Detection│
│ • QC Filtering (Mito%, Gene counts) │
│ • Scrublet Doublet Removal │
└──────────────────────┬───────────────────────┘
▼
┌──────────────────────────────────────────────┐
│ Module 2: Cohort Integration (Zarr DB) │
│ • scVI (transcriptomics batch correction) │
│ • MultiVI / WNN (joint multi-omics) │
└──────────────────────┬───────────────────────┘
▼
┌──────────────────────────────────────────────┐
│ Module 3: Automated Cell-Type Annotation │
│ • CellTypist Classifier │
│ • Majority Voting Neighborhood Consensus │
└──────────────────────┬───────────────────────┘
▼
┌──────────────────────────────────────────────┐
│ Module 4: Fate Mapping & Lineage Dynamics │
│ • Diffusion Pseudotime & PAGA Trajectories │
│ • CellRank 2 Fate Absorption Probabilities │
└──────────────────────┬───────────────────────┘
▼
┌──────────────────────────────────────────────┐
│ Module 5: Clinician Diagnostic Reporting │
│ • Self-contained HTML report with CSS │
│ • Base64-embedded high-res vector plots │
└──────────────────────────────────────────────┘
PrismSC/
├── config/
│ ├── config.yaml # Pipeline configuration params
│ ├── cohort_schema.json # Manifest JSON validation schema
│ └── cohort_manifest.tsv # Cohort samples and metadata
├── workflow/
│ ├── Snakefile # Main Snakemake execution entrypoint
│ └── rules/
│ ├── preprocessing.smk # Single-sample QC rules
│ ├── integration.smk # Multi-sample batch correction
│ ├── annotation.smk # CellTypist cell typing
│ ├── dynamics.smk # CellRank 2 trajectory inference
│ └── reporting.smk # Jinja2-based clinical report rule
├── scripts/
│ ├── preprocess.py # QC and doublet filtering script
│ ├── integrate.py # Cohort data consolidation and integration
│ ├── annotate.py # CellTypist classifier script
│ ├── dynamics.py # Trajectory and plotting script
│ └── generate_clinical_report.py # Jinja2 HTML reporter
├── envs/
│ └── clinical-sc-omics.yaml # Conda environment dependencies
├── Dockerfile # Containerization specification
├── .gitignore # Git untracked pattern rules
└── README.md # Documentation (this file)
-
Create and activate the conda environment:
conda env create -f envs/clinical-sc-omics.yaml conda activate clinical-sc-omics
-
Run the Snakemake pipeline:
snakemake --cores 8
To verify the workflow DAG and configuration without executing scripts:
snakemake -nThe pipeline is containerized using a lightweight Docker context (excluding large datasets via .dockerignore).
# Build the production-ready container image
docker build -t prismsc-pipeline .
# Execute on CPU (mapping results directory)
docker run -v $(pwd)/results:/app/results prismsc-pipeline snakemake --cores 8
# Execute with GPU Acceleration (requires NVIDIA Container Toolkit)
docker run --gpus all -v $(pwd)/results:/app/results prismsc-pipeline snakemake --cores 8PrismSC includes a pre-configured Snakemake execution profile for SLURM cluster submission:
- Snakemake v8+ (Plugin Executor):
pip install snakemake-executor-plugin-slurm snakemake --profile config/slurm
- Snakemake v7 (Legacy fallback):
snakemake --cluster "sbatch --partition=standard --cpus-per-task={threads} --mem={resources.mem_mb}" -j 16
To run dynamically on AWS serverless compute resources, leverage the official AWS executor plugin to route files to S3 buckets:
pip install snakemake-executor-plugin-aws-batch snakemake-storage-plugin-s3
snakemake --executor aws-batch \
--jobs 100 \
--default-resources s3_bucket=my-single-cell-bucketTo run the workflow on organs other than immune cells, modify params.annotation.model in config.yaml. The cell annotation script will automatically download the respective CellTypist neural net and route to matching lineage fallback marker panels if the model fails:
| Organ | CellTypist Model | Fallback Lineage Markers |
|---|---|---|
| Immune (Default) | Immune_All_Low.pkl |
T-cells, B-cells, Monocytes, NK, Granulocytes |
| Brain | Human_Dev_Brain.pkl |
Neurons, Astrocytes, Oligodendrocytes, Microglia |
| Lung | Human_Lung_Atlas.pkl |
Epithelial, Endothelial, Stromal, Immune |
| Kidney | Kidney_Biopsy.pkl |
Podocytes, Proximal Tubule, Loop of Henle, Collecting Duct |
The clinical diagnostics HTML report is set up to deploy automatically to GitHub Pages. To host and view the interactive, live report on the web:
- Go to your PrismSC repository page on GitHub.
- Click on the Settings tab at the top-right.
- Select Pages on the left-hand navigation sidebar.
- Under Build and deployment -> Source, select
Deploy from a branch. - Set the branch to
mainand the target folder to/docs, then click Save. - GitHub will build the site; your interactive clinical dashboard will be live at:
https://QntmSeer.github.io/PrismSC/
This project is licensed under the MIT License - see the LICENSE file for details.