cellNexus

cellNexus is a query interface for programmatic exploration and retrieval of harmonised, curated, and reannotated CELLxGENE human-cell-atlas data.

This standalone documentation website provides:

• Detailed data-processing information (quality control, harmonisation, and expression representations).
• Complete explanations of metadata columns used in filtering and interpretation.
• Guided examples for metadata-first exploration and gene-expression analysis.

Start Here

• Home: package purpose, architecture, and processing pipeline overview.
• Metadata Explore: field-by-field metadata dictionary, examples, and filtering strategy.
• Gene Expression Explore: practical workflows for querying single-cell, pseudobulk, and metacell expression.

Data Processing Overview

The harmonisation pipeline standardises data across datasets so queries are consistent across studies:

1. Metadata are retrieved from cloud-hosted harmonised tables.
2. Standardised quality control removes empty droplets, dead/damaged cells, and likely doublets.
3. Cell-level data are served through common assay layers (counts, cpm, pseudobulk, metacell).
4. Outputs are returned in analysis-ready R formats such as SingleCellExperiment and Seurat.

For implementation details and code examples, see the vignettes listed in the top navigation.

Client Usage Examples

R client (cellNexus)

library(cellNexus)
library(dplyr)
library(stringr)

metadata <- get_metadata(cloud_metadata = SAMPLE_DATABASE_URL)

query <- metadata |>
  filter(
    empty_droplet == FALSE,
    alive == TRUE,
    scDblFinder.class != "doublet",
    self_reported_ethnicity == "African",
    str_like(assay, "%10x%"),
    tissue == "lung parenchyma",
    str_like(cell_type, "%CD4%")
  )

sce <- get_single_cell_experiment(query, assays = "cpm")
pb <- get_pseudobulk(query)

Python client (cellNexusPy)

Python support is available in the companion repository: MangiolaLaboratory/cellNexusPy.

from cellnexuspy import get_metadata, get_anndata

sample_dataset = "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/sample_metadata.1.3.0.parquet"
conn, table = get_metadata(parquet_url=sample_dataset)

table = table.filter("""
    empty_droplet = 'false'
    AND alive = 'true'
    AND "scDblFinder.class" != 'doublet'
    AND feature_count >= 5000
""")

query = table.filter("""
    self_reported_ethnicity = 'African'
    AND assay LIKE '%10%'
    AND tissue = 'lung parenchyma'
    AND cell_type LIKE '%CD4%'
""")

adata = get_anndata(query, assay="cpm")
pb = get_anndata(query, aggregation="pseudobulk")
conn.close()