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

Graph-based algorithms have become essential in the analysis of single-cell data for numerous tasks, such as automated cell-phenotyping and identifying cellular correlates of experimental perturbations or disease states. In large multi-patient, multi-sample single-cell datasets, the analysis of cell...

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Bibliographic Details
Published in:Biocomputing 2023 Vol. 28; pp. 85 - 96
Main Authors: Chen, Chi-Jane, Crawford, Emma, Stanley, Natalie
Format: Book Chapter Journal Article
Language:English
Published: United States WORLD SCIENTIFIC 01.01.2023
Subjects:
Algorithms
Computational Biology - methods
GRAPH REPRESENTATIONS AND ALGORITHMS IN BIOMEDICINE
Humans
Software
Single-Cell Bioinformatics
Graph Coarsening
Cytometry
ISBN:9789811270628, 9811270627, 9789811270604, 9811270600, 9789811270611, 9811270619
ISSN:2335-6936, 2335-6936
Online Access:Get full text
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Summary:Graph-based algorithms have become essential in the analysis of single-cell data for numerous tasks, such as automated cell-phenotyping and identifying cellular correlates of experimental perturbations or disease states. In large multi-patient, multi-sample single-cell datasets, the analysis of cell-cell similarity graphs representations of these data becomes computationally prohibitive. Here, we introduce cytocoarsening, a novel graph-coarsening algorithm that significantly reduces the size of single-cell graph representations, which can then be used as input to downstream bioinformatics algorithms for improved computational efficiency. Uniquely, cytocoarsening considers both phenotypical similarity of cells and similarity of cells' associated clinical or experimental attributes in order to more readily identify condition-specific cell populations. The resulting coarse graph representations were evaluated based on both their structural correctness and the capacity of downstream algorithms to uncover the same biological conclusions as if the full graph had been used. Cytocoarsening is provided as open source code at https://github.com/ChenCookie/cytocoarsening.
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content type line 23
ISBN:9789811270628
9811270627
9789811270604
9811270600
9789811270611
9811270619
ISSN:2335-6936
2335-6936
DOI:10.1142/9789811270611_0009