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Overview

The MultiChamberPipeline class performs integrated analysis across multiple heart chambers (atria, ventricles, etc.), identifying chamber-specific markers and cross-chamber correlations. Inheritance: BasePipeline Source: heartmap.pipelines.MultiChamberPipeline (src/heartmap/pipelines/init.py:310)

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Constructor

MultiChamberPipeline(config: Config)
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config
Config
required
Configuration object containing analysis parameters.

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Attributes

Inherited from BasePipeline:
  • config (Config): Configuration object
  • data_processor (DataProcessor): Data processing handler
  • visualizer (Visualizer): Visualization handler
  • exporter (ResultsExporter): Results export handler
  • results (Dict[str, Any]): Dictionary storing pipeline results

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Methods

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run()

Run the multi-chamber analysis pipeline on cardiac tissue data. 
def run(data_path: str, output_dir: Optional[str] = None) -> Dict[str, Any]
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data_path
str
required
Path to H5AD file containing single-cell data from multiple heart chambers.
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output_dir
Optional[str]
Directory to save multi-chamber analysis results and visualizations. If None, results are returned but not saved.
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return
Dict[str, Any]
Dictionary containing pipeline results:
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adata
AnnData
Annotated data object (unchanged from input)
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results
Dict[str, Any]
Multi-chamber analysis results
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chamber_markers
Dict[str, Any]
Dictionary of chamber-specific marker genes and their statistics
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cross_chamber_correlations
pd.DataFrame
DataFrame containing correlation metrics between chambers
Raises:
  • ImportError: If required dependencies (scanpy, pandas, numpy) are not available
Pipeline Steps:
  1. Data Loading - Reads H5AD file with multi-chamber data
  2. Chamber Pattern Analysis - Identifies chamber-specific expression patterns
  3. Marker Identification - Finds marker genes specific to each chamber
  4. Cross-Chamber Correlation - Computes correlations between chamber transcriptomes
  5. Visualization - Generates chamber composition, marker, and correlation plots (if output_dir provided)

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save_results()

Inherited from BasePipeline. Save pipeline results to disk. 
def save_results(output_dir: str) -> None
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output_dir
str
required
Directory path where results will be saved

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Usage Example

from heartmap.config import Config
from heartmap.pipelines import MultiChamberPipeline

# Create configuration
config = Config(
    data_path="data/multi_chamber_heart.h5ad"
)

# Initialize pipeline
pipeline = MultiChamberPipeline(config)

# Run multi-chamber analysis
results = pipeline.run(
    data_path="data/whole_heart_atlas.h5ad",
    output_dir="results/multi_chamber"
)

# Access chamber markers
chamber_markers = results['results']['chamber_markers']
for chamber, markers in chamber_markers.items():
    print(f"\n{chamber} markers:")
    print(markers)

# Access cross-chamber correlations
correlations = results['results']['cross_chamber_correlations']
print(f"\nCross-chamber correlations:\n{correlations}")

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Data Requirements

The input data should:
  • Contain single-cell RNA-seq data from multiple heart chambers
  • Include chamber annotations in adata.obs (e.g., ‘chamber’, ‘tissue’, or ‘region’ column)
  • Be properly normalized and quality-filtered
Supported chamber types:
  • Left atrium (LA)
  • Right atrium (RA)
  • Left ventricle (LV)
  • Right ventricle (RV)
  • Septum
  • Apex

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Output Files

When output_dir is specified, the pipeline generates:
  • figures/chamber_composition.png - Cell type composition per chamber
  • figures/chamber_markers.png - Heatmap of chamber-specific markers
  • figures/cross_chamber_correlations.png - Correlation matrix between chambers
  • Results exported via ResultsExporter

BasicPipeline

Basic single-cell analysis pipeline

ComprehensivePipeline

Run all analyses including multi-chamber

Visualizer

Visualization utilities for multi-chamber data

Multi-Chamber Analysis Guide

Detailed guide on analyzing multi-chamber heart data

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