Deep learning-based cell composition analysis from tissue expression profiles
Deep learning-based cell composition analysis from tissue expression profiles
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DOI：
10.1126/sciadv.aba2619
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发表时间：
2020-07-01
期刊：
影响因子：
13.6
通讯作者：
Bonn, Stefan
中科院分区：
文献类型：
--
作者：
Menden, Kevin;Marouf, Mohamed;Bonn, Stefan
我们介绍了Scaden，一种用于细胞去卷积的深度神经网络，它使用基因表达信息来推断组织的细胞组成。Scaden在单细胞RNA测序（RNA-seq）数据上进行训练，以设计区分性特征，这些特征具有对偏差和噪声的鲁棒性，使得复杂的数据预处理和特征选择变得不必要。我们证明，Scaden优于现有的反卷积算法的精度和鲁棒性。一个经过训练的网络可以可靠地对批量RNA-seq和微阵列、人类和小鼠组织表达数据进行解卷积，并利用多个数据集的组合信息。由于这种稳定性和灵活性，我们相信深度学习将成为各种数据类型的细胞反卷积的算法支柱。Scaden的软件包和Web应用程序易于在公共资源中提供的新的和各种现有的表达数据集上使用，加深了对发育和疾病过程的分子和细胞理解。
We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden's software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.