论文标题
N-ACT：一种可解释的自动细胞类型和显着基因识别的深度学习模型
N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification
论文作者
论文摘要
单细胞RNA测序（SCRNASEQ）正在迅速促进我们对复杂组织和生物体内细胞组成的理解。大多数SCRNASEQ分析管道中的一个主要限制是依靠手动注释来确定细胞身份，这些细胞身份耗时，主观和需要专业知识。鉴于细胞测序的激增，有监督的方法，尤其是深度学习模型，以实现自动细胞类型识别（ACTI），可实现高精度和可扩展性。但是，所有现有的ACTI深度学习框架都缺乏可解释性，被用作“黑盒”模型。我们提出了N-ACT（细胞类型识别的神经注意事项）：使用神经注意事项的ACTI的首个可解释的深神经网络可检测用于细胞类型鉴定的显着基因。我们将N-ACT与两个先前手动注释的数据集的常规注释方法进行了比较，这表明N-ACT以无监督的方式准确地识别标记基因和细胞类型，同时在传统监督ACTI中对当前最新的数据集进行了相当的数据集。
Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, supervised methods-especially deep learning models-have been developed for automatic cell type identification (ACTI), which achieve high accuracy and scalability. However, all existing deep learning frameworks for ACTI lack interpretability and are used as "black-box" models. We present N-ACT (Neural-Attention for Cell Type identification): the first-of-its-kind interpretable deep neural network for ACTI utilizing neural-attention to detect salient genes for use in cell-type identification. We compare N-ACT to conventional annotation methods on two previously manually annotated data sets, demonstrating that N-ACT accurately identifies marker genes and cell types in an unsupervised manner, while performing comparably on multiple data sets to current state-of-the-art model in traditional supervised ACTI.
