标题： Mix-Geneformer：人类和小鼠单细胞RNA测序数据的统一表示学习 显示英文标题

标题： Mix-Geneformer: Unified Representation Learning for Human and Mouse scRNA-seq Data

摘要： 单细胞RNA测序（scRNA-seq）能够实现单细胞转录组分析，揭示细胞异质性和稀有群体。 最近的深度学习模型如Geneformer和Mouse-Geneformer在细胞类型分类和体外扰动等任务中表现良好。 然而，它们的物种特异性设计限制了跨物种泛化能力和转化应用，这对于推进转化研究和药物发现至关重要。 我们提出了Mix-Geneformer，一种基于Transformer的新模型，通过结合掩码语言建模（MLM）和基于SimCSE的对比损失的混合自监督方法，将人类和小鼠的scRNA-seq数据整合到统一表示中，以捕捉共享和物种特异性基因模式。 一种排名值编码方案进一步强调了训练过程中的高方差基因信号。 在来自不同人类和小鼠器官的大约5000万个细胞上进行训练，Mix-Geneformer在细胞类型分类和体外扰动任务中达到了或超过了最先进的基线模型，在小鼠肾脏数据上的准确率为95.8%，而最佳现有模型为94.9%。 它还成功识别了经体内研究验证的关键调控基因。 通过实现可扩展的跨物种转录组建模，Mix-Geneformer为比较转录组学和转化应用提供了一个强大的工具。 虽然我们的结果展示了出色的表现，但我们也承认存在一些局限性，例如计算成本和零样本迁移的变异性。 显示英文摘要

摘要： Single-cell RNA sequencing (scRNA-seq) enables single-cell transcriptomic profiling, revealing cellular heterogeneity and rare populations. Recent deep learning models like Geneformer and Mouse-Geneformer perform well on tasks such as cell-type classification and in silico perturbation. However, their species-specific design limits cross-species generalization and translational applications, which are crucial for advancing translational research and drug discovery. We present Mix-Geneformer, a novel Transformer-based model that integrates human and mouse scRNA-seq data into a unified representation via a hybrid self-supervised approach combining Masked Language Modeling (MLM) and SimCSE-based contrastive loss to capture both shared and species-specific gene patterns. A rank-value encoding scheme further emphasizes high-variance gene signals during training. Trained on about 50 million cells from diverse human and mouse organs, Mix-Geneformer matched or outperformed state-of-the-art baselines in cell-type classification and in silico perturbation tasks, achieving 95.8% accuracy on mouse kidney data versus 94.9% from the best existing model. It also successfully identified key regulatory genes validated by in vivo studies. By enabling scalable cross-species transcriptomic modeling, Mix-Geneformer offers a powerful tool for comparative transcriptomics and translational applications. While our results demonstrate strong performance, we also acknowledge limitations, such as the computational cost and variability in zero-shot transfer.