CITATION.cff

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type: conference-paper
authors:
  - family-names: Saha
    given-names: Utsha
  - family-names: Liu
    given-names: Lu
title: "An Exploration of Integrative Analysis of Simultaneously Profiled scHi-C and scRNA-Seq Data Using Graph Convolutional Network"
abstract: >-
  Understanding the relationship between three-dimensional genome organization
  and gene expression is key to unraveling cellular identity. In this study,
  we explore an integrative analysis method for simultaneously profiled scHi-C
  and scRNA-seq data using unsupervised Graph Convolutional Networks (GCNs).
  By representing chromatin interactions and transcriptional activity as
  graphs, we analyze single-cell data, with nodes capturing genomic bins and
  edges reflecting chromatin interactions. Using unsupervised GCNs, we first
  train models on each dataset, then extract embeddings, reduce their
  dimensionality with UMAP, and cluster them using k-means. We applied our
  framework to simultaneously profiled datasets of diverse mouse cell types.
  Performance metrics included Adjusted Rand Index (ARI), Silhouette scores,
  and UMAP visualizations. Our key findings are: (1) While scHi-C alone
  highlighted genome structures (Silhouette median ~ 0.6), integrating
  scRNA-seq did not consistently enhance clustering. (2) Low ARI scores
  (median ~ 0) indicate discrepancies between clustering and biological
  annotations, suggesting the complexity of uncovering novel genomic insights.
  Further refinement is needed to align computational outputs with biological
  interpretations.
collection-title: "2025 IEEE 11th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService)"
year: 2025
start: 155
end: 162
doi: 10.1109/BigDataService65758.2025.00028
keywords:
  - Graph Convolutional Networks (GCNs)
  - Simultaneous Profiled
  - scHi-C
  - scRNA-seq
  - Single-cell Analysis
  - Genomics
  - Chromatin Interactions
  - Gene Expression
  - Bioinformatics