Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity. A major challenge, however, lies in the prevalence of non-biological zeros-false measurements caused by technical limitations that mask a cell's true transcriptome. This fundamental issue of distinguishing these artifacts from true biological zeros, where a gene is genuinely absent, remains a key hurdle for computational methods, as misclassification can distort biological signals during data recovery. To overcome this, we introduce D3Impute, a discriminative imputation framework built on three key innovations: (1) a distribution-aware normalization step that adapts to dataset-specific characteristics while preserving meaningful biological variation; (2) a dual-network discriminator that uses bulk RNA-seq data as a biological reference to accurately identify non-biological zeros while retaining the true biological zeros; and (3) a density-guided imputation engine that recovers expression values while maintaining local cellular neighborhood structures. Through comprehensive benchmarking against 12 state-of-the-art methods across six diverse datasets, D3Impute demonstrates consistent and significant improvements in essential downstream analyses, including cell clustering, trajectory inference, and differential expression detection. Furthermore, we provide an extensive practical evaluation of D3Impute, demonstrating its robustness across varying data qualities and providing clear guidelines for optimal application. By offering a robust, biologically informed, and user-oriented solution, D3Impute not only enhances scRNA-seq data analysis but also offers a generalizable framework for handling zero-inflated data in computational biology.