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.

N-ACT 是第一个可解释的深度神经网络，利用神经 - 注意力检测显著基因，用于细胞类型识别的自动细胞类型识别，通过将其与传统的监督式细胞类型识别方法进行比较，证明 N-ACT 在无监督的情况下准确识别标记基因和细胞类型，同时在多个数据集上表现与传统监督的 ACTI 现有技术水平相当。

N-ACT：一种可解释的深度学习模型用于自动细胞类型和显著基因的识别

N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification

Background Precise prediction of cancer types is vital for cancer diagnosis
and therapy. Important cancer marker genes can be inferred through predictive
model. Several studies have attempted to build machine learning models for this
task however none has taken into consideration the effects of tissue of origin
that can potentially bias the identification of cancer markers. Results In this
paper, we introduced several Convolutional Neural Network (CNN) models that
take unstructured gene expression inputs to classify tumor and non-tumor
samples into their designated cancer types or as normal. Based on different
designs of gene embeddings and convolution schemes, we implemented three CNN
models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and
tested on combined 10,340 samples of 33 cancer types and 731 matched normal
tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent
prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal).
Furthermore, we interpreted one of the models, known as 1D-CNN model, with a
guided saliency technique and identified a total of 2,090 cancer markers (108
per class). The concordance of differential expression of these markers between
the cancer type they represent and others is confirmed. In breast cancer, for
instance, our model identified well-known markers, such as GATA3 and ESR1.
Finally, we extended the 1D-CNN model for prediction of breast cancer subtypes
and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be
found at this https URL

本研究使用卷积神经网络 (CNN) 模型对 10,340 个来自 33 种不同癌症类型和 731 个正常组织样品进行分类，并在 1D-CNN 模型中确定了 2,090 个癌症标记物，其中包括 GATA3 和 ESR1 等已知标记，并对乳腺癌亚型进行了预测，取得了 88.42% 的平均准确率。