用于空间转录组学中细胞类型解卷积的掩码对抗神经网络
Masked adversarial neural network for cell type deconvolution in spatial transcriptomics
AI总结：
本文提出MACD，一种结合对抗域对齐与掩码特征学习的神经网络，用于缩小scRNA-seq与ST数据差异，并在32个模拟及2个真实数据集上提升细胞类型解卷积准确性。
摘要：
准确测定疾病相关组织中的细胞类型组成，对于识别疾病靶点至关重要。大多数现有空间转录组学（ST）技术无法实现单细胞分辨率，因此难以准确确定细胞类型。为解决这一问题，研究者已开发出多种解卷积方法。这些方法大多使用来自同一组织的单细胞RNA测序（scRNA-seq）数据作为参考，以推断ST数据spot中的细胞类型。然而，它们往往忽视scRNA-seq与ST数据之间的差异。为克服这一局限，我们提出一种掩码对抗神经网络（Masked Adversarial Neural Network，MACD）。MACD采用对抗学习，将真实ST数据与由scRNA-seq数据生成的模拟ST数据进行对齐。通过将二者映射到统一的潜在空间，它能够最小化两类数据之间的差异。此外，MACD使用掩码技术有效学习真实ST数据的特征并缓解噪声。我们在32个模拟数据集和2个真实数据集上评估了MACD，证明了其在执行细胞类型解卷积时的准确性。本文使用的所有代码和公开数据集均可在https://github.com/wenwenmin/MACD和https://zenodo.org/records/12804822获取。
英文摘要：
Accurately determining cell type composition in disease-relevant tissues is crucial for identifying disease targets. Most existing spatial transcriptomics (ST) technologies cannot achieve single-cell resolution, making it challenging to accurately determine cell types. To address this issue, various deconvolution methods have been developed. Most of these methods use single-cell RNA sequencing (scRNA-seq) data from the same tissue as a reference to infer cell types in ST data spots. However, they often overlook the differences between scRNA-seq and ST data. To overcome this limitation, we propose a Masked Adversarial Neural Network (MACD). MACD employs adversarial learning to align real ST data with simulated ST data generated from scRNA-seq data. By mapping them into a unified latent space, it can minimize the differences between the two types of data. Additionally, MACD uses masking techniques to effectively learn the features of real ST data and mitigate noise. We evaluated MACD on 32 simulated datasets and 2 real datasets, demonstrating its accuracy in performing cell type deconvolution. All code and public datasets used in this paper are available at https://github.com/wenwenmin/MACD and https://zenodo.org/records/12804822.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
