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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
Summary
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.
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.
