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Analysis Code for scBaseCount

All code associated with the scBaseCount manuscript.

Repository overview

This repo contains Jupyter notebooks, small Python CLI tools, and a Nextflow pipeline used for analyzing scBaseCount datasets, deriving summary statistics, and comparing data sources (e.g., CellXGene vs scBaseCamp).

Notebook summary

  • CellXGene: CellXGene/cell_counts.ipynb, umi_counts.ipynb, census_summary.ipynb.
  • SRAgent: SRAgent/summary_stats.ipynb, validation.ipynb, run_time_stats.ipynb.
  • Estimation and meta‑analysis: cell_count_estimates/estimate_from_sragent.ipynb, cell_statistics_meta_analysis/MetaAnalysis.ipynb.
  • Tissues and labels: tissues/tissue_annotate*.ipynb, tissue_summary.ipynb.
  • Dataset‑specific and comparisons: Replogle2022/, tiledb/scBaseCamp_vs_CellxGene.ipynb, misc/species_umap.ipynb.

Scripts (CLI utilities)

  • SRA helpers: bioproject2srx.py, srx-to-project.py, srx-to-entrez-id.py.
  • Ontology/labels: get-disease-ontology.py, cluster-ontology.py, tissue_ontology_id2label.py.
  • DB utilities: ChrisData2SQL-DB.py, ChrisDataNoSRX2SQL-DB.py, sql_db_utils.py, db_utils.py.

Pipeline

  • Nextflow H5AD processing: nextflow/metaq/main.nf with config in nextflow/metaq/nextflow.config. Example: nextflow run nextflow/metaq/main.nf -resume -profile slurm --input_dir <in> --output_dir <out>