美国耶鲁大学Smita Krishnaswamy课题组在最新研究中，利用他们所研发的深度多任务神经网络对单细胞数据进行了分析。相关论文2019年10月7日在线发表于《自然—方法学》。

研究人员提出了一个名为SAUCIE的算法，这是一个深度神经网络，它结合了神经网络提供的并行化和可扩展性，以及可以由其学习以实现多个单细胞数据分析任务的数据深度展示。该算法的正则化（惩罚机制）使得从神经网络隐藏层中学习到的内容得以揭示。在大型的多患者数据集上，SAUCIE的各个隐藏层包含经过去噪和批处理校正的数据、低维可视化和无监督聚类，以及可用于探索数据的其他信息。研究人员分析了180个样本的数据集，其由来自印度登革热患者的1100万个T细胞组成（由质谱流式细胞术所测量）。SAUCIE可以分批纠正和识别急性登革热感染的簇特征，并解析病人对登革热产生的不同免疫反应。

据悉，目前，要分析由许多细胞和样品组成的单细胞数据，并解决因批次效应和不同样品制备而引起的变化是一项挑战。

附：英文原文

Title: Exploring single-cell data with deep multitasking neural networks

Author: Matthew Amodio, David van Dijk, Krishnan Srinivasan, William S. Chen, Hussein Mohsen, Kevin R. Moon, Allison Campbell, Yujiao Zhao, Xiaomei Wang, Manjunatha Venkataswamy, Anita Desai, V. Ravi, Priti Kumar, Ruth Montgomery, Guy Wolf & Smita Krishnaswamy 

Issue&Volume: 7 October 2019

Abstract: 

It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE’s various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue.

DOI: 10.1038/s41592-019-0576-7

Source: https://www.nature.com/articles/s41592-019-0576-7