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A Graph Coarsening Algorithm for Compressing Representations of Single-Cell Data with Clinical or Experimental

Chi-Jane Chen1, Emma Crawford, Natalie Stanley

  • 1Department of Computer Science and Computational Medicine Program The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA, chijane@cs.unc.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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Cytocoarsening is a new graph-coarsening algorithm that makes analyzing large single-cell datasets computationally feasible. It efficiently reduces graph size while preserving biological insights for cell population identification.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Single-cell Genomics

Background:

  • Graph-based algorithms are crucial for single-cell data analysis, including cell-phenotyping and identifying disease states.
  • Analyzing large single-cell datasets with cell-cell similarity graphs is computationally intensive.

Purpose of the Study:

  • Introduce cytocoarsening, a novel graph-coarsening algorithm for single-cell data.
  • Improve computational efficiency in analyzing large-scale single-cell graphs.
  • Facilitate the identification of condition-specific cell populations.

Main Methods:

  • Developed cytocoarsening, a graph-coarsening algorithm tailored for single-cell data.
  • Incorporated both cell phenotypical similarity and associated clinical/experimental attributes.
  • Evaluated coarse graph representations for structural correctness and downstream analysis efficacy.

Main Results:

  • Cytocoarsening significantly reduces the size of single-cell graph representations.
  • The coarsened graphs maintain structural integrity for downstream bioinformatics algorithms.
  • Downstream analyses on coarsened graphs yield comparable biological conclusions to full graphs.

Conclusions:

  • Cytocoarsening offers a computationally efficient approach to single-cell graph analysis.
  • The algorithm effectively identifies condition-specific cell populations by integrating diverse data attributes.
  • This method enhances the scalability of single-cell data analysis for large datasets.