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

Single-cell multiomics technologies provide unprecedented opportunities to dissect cellular heterogeneity by capturing multidimensional information on complex cellular states and regulatory networks. However, challenges such as high dimensionality, extreme data sparsity, and modality-specific discrepancies hinder the accuracy, interpretability, and scalability of the existing integration methods. Existing integration paradigms, including horizontal, vertical, and diagonal strategies, are further limited by their inability to fully capture nonlinear biological relationships, their reliance on high-quality data, and their substantial computational demands. Here, we present scII (Dual-Threshold Adaptive Integration of Single-Cell Multiomics Data Driven by Imputation), an adaptive framework designed to integrate gene expression (scRNA-seq) and chromatin accessibility (scATAC-seq) data. Our approach is built on several key conceptual innovations: (i) scRNA-seq-guided signal imputation to enhance information integrity in scATAC-seq; (ii) a multilayer perceptron with the Maxout activation function to improve the modeling of complex nonlinear relationships and mitigate the vanishing gradient problem; (iii) a dynamic dual-threshold adaptive selection mechanism that jointly evaluates cross-modality feature similarity and classification reliability to select high-quality cells; and (iv) Bayesian Information Criterion (BIC)-based optimization to dynamically determine the number of Gaussian Mixture Model components according to data distribution, thereby eliminating reliance on manually preset parameters. Extensive experiments on multiple real-world and simulated data sets demonstrate that scII not only enables efficient integration of unpaired scRNA-seq and scATAC-seq data but also achieves accurate transfer of cell-type annotations, allowing high-precision cell-type prediction for scATAC-seq.