美国生物科技公司Calico Life Sciences研究员袁瀚与David Kelley共同合作近期取得重要工作进展，他们研究开发了一种新的基于深度卷积神经网络的对单细胞ATAC进行建模的方法——scBasset。相关论文于2022年8月8日在线发表于国际顶尖学术期刊《自然—方法学》杂志上。

研究人员介绍了scBasset，一种基于序列的卷积神经网络方法来对scATAC数据进行建模。 他们表明，通过利用可访问性峰值背后的DNA序列信息和神经网络模型的表现力，scBasset在scATAC和单细胞多组学数据集的各种任务中实现了最先进的性能，包括细胞聚类、scATAC数据去噪、跨分析的数据集成和转录因子活性推断。

据介绍，使用scATAC测序对转座酶可及染色质进行单细胞分析，在研究表观遗传概况中细胞异质性方面具有很大的前景，但由于scATAC固有的高维度和稀疏性，在分析scATAC数据时仍存在重大挑战。

附：英文原文

Title: scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks

Author: Yuan, Han, Kelley, David R.

Issue&Volume: 2022-08-08

Abstract: Single-cell assay for transposase-accessible chromatin using sequencing (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain important challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single-cell multiome datasets, including cell clustering, scATAC profile denoising, data integration across assays and transcription factor activity inference. Using a sequence-based deep neural network, scBasset facilitates various tasks of single-cell ATAC-seq analysis in a unified framework.

DOI: 10.1038/s41592-022-01562-8

Source: https://www.nature.com/articles/s41592-022-01562-8