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  1. Home
  3. Scii: Dual-threshold Adaptive Integration Of Single-cell Multiomics Data Driven By Imputation.
  1. Home
  3. Scii: Dual-threshold Adaptive Integration Of Single-cell Multiomics Data Driven By Imputation.

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scII: Dual-Threshold Adaptive Integration of Single-Cell Multiomics Data Driven by Imputation.

Yi Zhang1,2, Yuru Li1,2, Zhicheng Jin1,2

  • 1School of Computer Science and Engineering, Guilin University of Technology, Guilin 541004, China.

Journal of Chemical Information and Modeling
|January 15, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed scII, a novel framework for integrating single-cell gene expression and chromatin accessibility data. This method enhances data integrity and enables accurate cell-type prediction, overcoming limitations of existing multiomics integration techniques.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell multiomics technologies offer deep insights into cellular heterogeneity but face challenges like data sparsity and modality discrepancies.
  • Existing integration methods struggle with nonlinear relationships, data quality, and computational demands, limiting their scalability and accuracy.

Purpose of the Study:

  • To present scII, an adaptive framework for integrating single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) data.
  • To address limitations in current multiomics integration, focusing on enhancing data integrity, modeling nonlinear relationships, and improving cell selection.

Main Methods:

  • scII employs scRNA-seq-guided imputation to improve scATAC-seq data quality.
  • Utilizes a multilayer perceptron with Maxout activation for nonlinear relationship modeling and a dual-threshold mechanism for adaptive cell selection.
  • Incorporates Bayesian Information Criterion (BIC) for automated Gaussian Mixture Model component determination, removing the need for manual parameter setting.

    • Main Results:

    • scII efficiently integrates unpaired scRNA-seq and scATAC-seq data.
    • Demonstrates accurate transfer of cell-type annotations between modalities.
    • Achieves high-precision cell-type prediction for scATAC-seq data, validated on diverse datasets.

    Conclusions:

    • scII provides an effective solution for integrating single-cell multiomics data, particularly scRNA-seq and scATAC-seq.
    • The framework overcomes key challenges in data integration, leading to improved accuracy and interpretability.
    • Enables robust cell-type prediction, advancing the analysis of cellular heterogeneity.