Single-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout data becomes a critical concern in scRNA-seq data analysis. Present imputation methods predominantly rely on statistical or machine learning approaches, often overlooking inter-sample correlations. To address this limitation, We introduced SAE-Impute, a new computational method for imputing single-cell data by combining subspace regression and auto-encoders for enhancing the accuracy and reliability of the imputation process. Specifically, SAE-Impute assesses sample correlations via subspace regression, predicts potential dropout values, and then leverages these predictions within an autoencoder framework for interpolation. To validate the performance of SAE-Impute, we systematically conducted experiments on both simulated and real scRNA-seq datasets. These results highlight that SAE-Impute effectively reduces false negative signals in single-cell data and enhances the retrieval of dropout values, gene-gene and cell-cell correlations. Finally, We also conducted several downstream analyses on the imputed single-cell RNA sequencing (scRNA-seq) data, including the identification of differential gene expression, cell clustering and visualization, and cell trajectory construction. These results once again demonstrate that SAE-Impute is able to effectively reduce the droupouts in single-cell dataset, thereby improving the functional interpretability of the data.

Accurate classification of cancer subtypes is critical for precision oncology, yet existing methods often fail to integrate multi-omic data while providing biologically interpretable insights. This study presents a novel hybrid deep learning framework for cancer classification that synergistically combines graph neural networks (GNNs) and transformers to model complex interactions across genomic, epigenetic, and functional biological layers. Our approach introduces three key innovations: (1) a heterogeneous graph integrating gene expression, DNA methylation, and pathway nodes with biologically meaningful edges (protein interactions, regulatory relationships); (2) discrete wavelet transforms for spatial-aware dimensionality reduction of methylation data, preserving critical regional patterns; and (3) pathway-guided attention mechanisms that prioritize oncogenic signaling pathways while ensuring interpretability. Experiments demonstrate superior performance over state-of-the-art methods in the classification of 23 cancer types and of breast-cancer subtypes. By bridging artificial intelligence (AI) with cancer biology, our framework enables interpretable multi-omic integration, offering clinicians pathway-level explanations for predictions while maintaining cross-platform robustness. This work advances precision oncology by providing both accurate classification and actionable biological insights, paving the way for improved therapeutic strategies.