cxg-query-enhancer 0.2.0

A Python library that enhances biological query by expanding terms (cell types, tissues, etc..) to include subtypes and parts using ontologies, ensuring comprehensive data retrieval.

cxg-query-enhancer

A Python library that enhances biological queries by automatically expanding ontology terms (like cell types, tissues, etc.) to include all subtypes and part-of relationships based on underlying ontologies. This ensures that queries for general terms (e.g., 'macrophage', 'kidney') also capture annotations to more specific entities (like 'alveolar macrophage', 'renal cortex'), making your CellXGene Census queries more comprehensive.

The Challenge: Incomplete Queries in Biological Data

When exploring biological datasets, querying for a general term like "macrophage" often misses data annotated with more specific subtypes (e.g., "alveolar macrophage," "Kupffer cell"). Similarly, a search for "kidney" might not automatically include its constituent parts like "renal cortex" or "nephron." Manually identifying and including all these related terms for every query is time-consuming and prone to omissions, leading to incomplete results.

How cxg-query-enhancer Solves This:

cxg_query_enhancer tackles this by automatically enhancing your queries in the CellXGene Census. It uses the Ubergraph knowledge graph to find all relevant subtypes and parts for your search terms and automatically expand your query. By default, it also filters these expanded terms against the latest version of the CellXGene Census, ensuring results are both comprehensive and relevant to your dataset.

Key Features

cxg-query-enhancer provides:

1. Automated query expansion: Rewrites query filters to include all subtypes and parts of specified terms.
2. Multiple ontology support:
   • Cell Ontology (CL) for cell types
   • Uberon for anatomical structures
   • MONDO for diseases
   • Developmental stage ontologies
     • Human Developmental Stages (HsapDv)
     • Mouse Developmental Stages (MmusDv)
3. Flexible Term Input: Accepts input terms as:

• Labels (e.g., 'neuron', 'kidney')
• Ontology IDs (e.g., 'CL:0000540', 'UBERON:0002113')
• Ontology synonyms

4. CellxGene Census Filtering:

• By default, filters expanded Ubergraph terms against the "latest" version of the CellXGene Census.
• You can customize this using the census_version parameter in the enhance function:
  • You can specify a particular version string (e.g., a date like "2024-12-01").
  • You can set to None to disable census filtering (in which case, only Ubergraph expansion is performed).
• This filtering requires the organism parameter (e.g., "homo_sapiens", "mus_musculus") to ensure species-appropriate term matching against the census data.

Prerequisites

Ensure you have the following installed:

• Poetry (for managing dependencies)
• Python >=3.10,<3.12

Installation

You can install cxg-query-enhancer PyPI package using pip

# Using pip
pip install cxg-query-enhancer

# Using Poetry
poetry add cxg-query-enhancer

Quick Start: Enhanced Querying of the CELLxGENE Census with cxg-query-enhancer

Get started by using cxg-query-enhancer to enhance your CELLxGENE Census queries. This example demonstrates how to retrieve an AnnData object by filtering for 'medium spiny neuron' cells, with enhance() function automatically expanding the cell type to include its subclasses.

import cellxgene_census
from cxg_query_enhancer import enhance

# Open the latest version of the CELLxGENE Census
with cellxgene_census.open_soma(census_version="latest") as census:

    # Retrieve an AnnData object based on specific filters
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        var_value_filter="feature_id in ['ENSG00000161798', 'ENSG00000188229']",
        obs_value_filter=enhance(             #enhance function to expand the query
            "sex == 'female' and cell_type in ['medium spiny neuron']",
            organism="Homo sapiens",  #organism specified in the enhance function 
        ),
        obs_column_names=[
                "assay",
                "cell_type",
                "tissue",
                "tissue_general",
                "suspension_type",
                "disease",
            ],
    )

print(adata.obs)

The output is a pandas.DataFrame over 5k cells demonstrating the inclusion of medium spiny neuron subclasses with the function enhance():

| assay     | cell_type                            | tissue          | tissue_general | suspension_type | disease | sex    |
|-----------|--------------------------------------|-----------------|----------------|-----------------|---------|--------|
| 10x 3' v3 | indirect pathway medium spiny neuron | caudate nucleus | brain          | nucleus         | normal  | female |
| 10x 3' v3 | direct pathway medium spiny neuron   | caudate nucleus | brain          | nucleus         | normal  | female |
| 10x 3' v3 | indirect pathway medium spiny neuron | caudate nucleus | brain          | nucleus         | normal  | female |
| 10x 3' v3 | indirect pathway medium spiny neuron | caudate nucleus | brain          | nucleus         | normal  | female |
| 10x 3' v3 | direct pathway medium spiny neuron   | caudate nucleus | brain          | nucleus         | normal  | female |
| ...       | ...                                  | ...             | ...            | ...             | ...     | ...    |
| 10x 3' v3 | medium spiny neuron                  | cerebral cortex | brain          | cell            | normal  | female |
| 10x 3' v3 | medium spiny neuron                  | cerebral cortex | brain          | cell            | normal  | female |
| 10x 3' v3 | medium spiny neuron                  | cerebral cortex | brain          | cell            | normal  | female |
| 10x 3' v3 | medium spiny neuron                  | cerebral cortex | brain          | cell            | normal  | female |
| 10x 3' v3 | medium spiny neuron                  | cerebral cortex | brain          | cell            | normal  | female |

5471 rows × 7 columns

Usage Examples

Example 1: Basic Query Expansion (Ubergraph Only and No Census Filtering)

from cxg_query_enhancer import enhance

# Original query filter
original_query = "cell_type in ['neuron']"

# Expand to include all subtypes of neurons (no Census filtering)
expanded_query = enhance(
    original_query,
    census_version=None  # disable census filtering
)

print(expanded_query)

# Output: cell_type in ['neuron', 'Purkinje neuron', 'motor neuron', ...]

Example 2: Multiple Categories with Census Filtering

from cxg_query_enhancer import enhance

# Expand cell types, tissues, diseases, and development stages, but filter against terms in the Census

original_query = "cell_type in ['medium spiny neuron'] and tissue in ['kidney'] and disease in ['diabetes mellitus'] and development_stage in ['10-month-old stage']"

expanded_query = enhance(
    original_query,   
    organism="homo_sapiens",  # specify the organism for accurate Census filtering                           
    )

print(expanded_query)

# Output: cell_type in ['direct pathway medium spiny neuron', 'indirect pathway medium spiny neuron', 'medium spiny neuron'] and tissue in ['cortex of kidney', 'kidney', 'kidney blood vessel', 'renal medulla', 'renal papilla', 'renal pelvis'] and disease in ['type 1 diabetes mellitus', 'type 2 diabetes mellitus'] and development_stage in ['10-month-old stage']

Function Reference

Main Function

enhance(query_filter, categories=None, organism=None, census_version="latest")

Rewrites a query filter to include the subtypes and part-of relationships of specified terms.

Parameters:

• query_filter (str): The original query filter string.
• categories (list or None): Categories to expand.
  • If None (default): Categories are auto-detected by scanning the query for patterns like cell_type in [...] or tissue_ontology_term_id in [...].
  • If a list (e.g., ["cell_type", "disease"]): Only the specified categories are expanded.
  • Supported categories: "cell_type", "tissue", "tissue_general","disease", "development_stage".
• organism (str): The organism to query in the CellxGene census (e.g., "homo_sapiens", "mus_musculus").
  • Default: If not provided by the user, defaults to "homo_sapiens".
  • Important: When expanding the development_stage category without an explicitly provided organism, the function enhance() uses the default "homo_sapiens" but logs a warning—to help users avoid unintended results in multi-organism datasets.
  • Recommended: Explicitly provide the organism for census filtering and if your query targets "development_stage" (e.g. enhance("sex == 'female' and cell_type in ['medium spiny neuron']", organism="mus_musculus")).
• census_version (str): Version of the CellXGene Census to use for filtering terms against the census.
  • Default: "latest" for the most recent version
  • Alternative: Specify a date like "2024-12-01" for reproducible results
  • Disable filtering: Set to None to skip filtering and perform only ontology expansion

Returns:

• str: The rewritten query filter with expanded terms.

Additional Classes

These classes are used internally and don't need to be called directly:

SPARQLClient

A client for interacting with Ubergraph endpoint using SPARQL queries.

OntologyExtractor

Extracts subclasses and part-of relationships from Ubergraph for ontology terms.

How It Works

1. Parse Query: The library identifies terms in your query that need expansion
2. Resolve Terms: For each term, it:
   • Resolves labels to ontology IDs (if necessary)
   • Queries Ubergraph to find all subclasses and part-of relationships
   • Filters the expanded terms against the CellXGene Census
3. Rewrite Query: The expanded terms are rewritten into the original query format

Acknowledgments

• Ubergraph for providing the ontology knowledge graph
• CellXGene Census for single-cell reference data