FAIR Metadata

Overview

This repository provides FAIR-compliant metadata for the BE1 benchmark dataset from the ELIXIR Single Cell Omics Implementation Study (WP3) – a 10XGenomics single-cell RNA sequencing experiment providing controlled cancer heterogeneity using seven lung cancer cell lines with distinct driver mutations: EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1.

The be1_query.py extracts single cell expression data from the GEO repository

Quick Start

# Clone repository
git clone https://github.com/biofold/sco-benchmark-fair.git
cd sco-benchmark-fair

# Install requirements
pip install -r requirements.txt

# Basic gene query (uses embedded defaults)
python scripts/be1_query.py --gene EGFR,KRAS,BRAF

# Generate enriched metadata (as JSON)
python scripts/be1_query.py \
  --geo GSE243665 \
  --metadata sco-benchmark-experiment-1.json \
  --gene EGFR,KRAS,BRAF \
  --output results.csv \
  --verbose
        

Dataset Statistics

Total sequencing: 2.46 billion reads, ≥71.26% bases ≥Q30 on Illumina NovaSeq 6000

FAIR Compliance Evaluation

This repository has been evaluated using comprehensive FAIR principles analysis. Overall Score: 80/100

Note: The scores and improvement items above are specifically for the biofold/sco-benchmark-fair repository. Implementing the listed "Improvements Needed" can raise the overall FAIR score to 100/100.

Metadata Architecture

Generated File Structure

sco-benchmark-fair/
├── scrna-schema/
│   ├── sco-benchmark-experiment-1.json      # Complete BE1 metadata (16 sheets)
│   └── schema.json                           # Single Cell Schema v0.4 definition
├── scripts/
│   ├── be1_query.py                          # Python query tool
│   ├── test_fair_gitrepo.py                  # Python FAIR repo evaluation
│   └── generate_fair_report.py               # Python FAIR Report Generator
├── CITATION.cff                              # Citation metadata
├── LICENSE.cff                                # CC BY 4.0 license
├── requirements.txt                           # Python dependencies
└── index.html                                 # FAIRification report (this page)
        

Data Schema (Single Cell v0.4)

{
  "manifest": {
    "key": "version_dwc_sc_rnaseq",
    "name": "Single-cell Ribonucleic Acid Sequencing [Darwin Core]",
    "standard": "dwc",
    "technology": "sc_rnaseq",
    "manifest_version": "0.4"
  },
  "study": [ /* study metadata */ ],
  "person": [ /* author information */ ],
  "sample": [ /* 8 sample entries */ ],
  "dissociation": [ /* protocol details */ ],
  "cell_suspension": [ /* per-line cell counts */ ],
  "lib_prep": [ /* 10X Genomics v3.1 */ ],
  "sequencing": [ /* NovaSeq 6000 parameters */ ],
  "analysis_derived_data": [ /* QC metrics */ ],
  "raw_data_processing": [ /* Cell Ranger v7.1.0 */ ],
  "downstream_processing": [ /* validation clustering */ ],
  "data_availability_checklist": [ /* access points */ ],
  "file": [ /* 24+ file listings */ ],
  "expression_data_process_setting": [ /* normalization */ ],
  "expression_data_file": [ /* matrix details */ ]
}
        

Usage Examples

import scanpy as sc
import pandas as pd

# Load A549 data
adata = sc.read_10x_mtx(
    './be1_data',
    prefix='GSE243665_A549_',
    var_names='gene_symbols'
)

# Query EGFR expression
egfr_idx = list(adata.var_names).index('EGFR')
expression = adata.X[:, egfr_idx].toarray().flatten()

results = pd.DataFrame({
    'cell_barcode': adata.obs_names,
    'EGFR_expression': expression,
    'EGFR_log1p': np.log1p(expression)
})
print(f"Mean EGFR: {results['EGFR_expression'].mean():.2f}")
                    

# Install BE1 package
install.packages("devtools")
devtools::install_github("kendomaniac/BE1")

# Launch Shiny app
library(BE1)
# Run the mixing app at:
# http://aisc.hpc4ai.unito.it:3838/
                    

Command Line Options

# Full metadata query with all options
python scripts/be1_query.py \
  --geo GSE243665 \                           # GEO accession
  --metadata scrna-schema/sco-benchmark-experiment-1.json \
  --gene EGFR,KRAS,BRAF \                      # Comma-separated genes
  --cell AAACCCAAGAGG-1 \                       # Cell barcode search
  --cell-line A549 \                            # Filter by cell line
  --min-umi 2 \                                 # Expression threshold
  --output results.csv \                         # Save results
  --verbose                                      # Detailed logging
        

Key Features & Innovations

FAIR Improvements Roadmap

Based on the FAIR assessment (80/100), here are prioritized improvements to reach a higher score. Each item includes potential point gain toward the target of 100/100.

Implementation Priorities

Implementing all improvements will raise the FAIR score from 80/100 to 100/100, making the repository fully FAIR-compliant.

License and Attribution

License

Creative Commons Attribution 4.0 International (CC BY-NC 4.0)
This work is licensed under a Creative Commons Attribution 4.0 International License.

Citation

@article{arigoni2024single,
   title={A single cell RNAseq benchmark experiment embedding 'controlled' cancer heterogeneity},
   author={Arigoni, Maddalena and Ratto, Maria Luisa and Riccardo, Federica and Balmas, Elisa and Calogero, Lorenzo and Cordero, Francesca and Beccuti, Marco and Calogero, Raffaele A. and Alessandri, Luca},
   journal={Scientific Data},
   volume={11},
   number={1},
   pages={159},
   year={2024},
   publisher={Nature Publishing Group},
   doi={10.1038/s41597-024-03002-8},
   pmid={38307867},
   pmcid={PMC10837414}
}

@dataset{elixir2024fairmetadata,
   title={BE1 Benchmark Dataset for Single-Cell RNA Sequencing: Controlled Cancer Heterogeneity FAIR Metadata},
   author={{ELIXIR Single Cell Omics Community}},
   year={2024},
   version={1.0},
   publisher={Zenodo},
   doi={10.5281/zenodo.XXXXXXXX}  # Placeholder
}
        

Dataset Reference Publication

"A single cell RNAseq benchmark experiment embedding 'controlled' cancer heterogeneity"
📄 https://doi.org/10.1038/s41597-024-03002-8

Contact

• ELIXIR Single Cell Omics Community: https://elixir-europe.org/communities/single-cell-omics
• Repository Issues: GitHub Issues

Related Resources

Contributing

We welcome contributions to improve FAIR compliance! See our GitHub Issues for current improvement tasks.

Support

For questions or support, please:

1. Check the FAIR Improvements Roadmap section
2. Review existing GitHub Issues
3. Create a new issue with detailed description

This project is part of the ELIXIR Single Cell Omics Community's efforts to promote FAIR data practices in structural bioinformatics.