Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis,ACS Sensors

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Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis
ACS Sensors ( IF 8.2 ) Pub Date : 2024-06-27 , DOI: 10.1021/acssensors.4c00149
Qiang Yu _{1,

2} , Xiaokun Shen ₂ , LangLang Yi ₃ , Minghui Liang ₃ , Guoqian Li ₃ , Zhihui Guan ₃ , Xiaoyao Wu ₄ , Helene Castel ₅ , Bo Hu _{2,

4,

6} , Pengju Yin ₄ , Wenbo Zhang ₇

Affiliation

Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct from discrete sequences, which limits the application of deep learning-based algorithms in data analysis due to the lack of discretization. Herein, a model called fragment-fusion transformer is proposed, which integrates the discrete fragmentation of continuous spectra based on their intrinsic characteristics with the extraction of intrafragment features and the fusion of interfragment features. The model integrates the intrinsic feature-based fragmentation of spectra with transformer, constructing the fragment transformer block for feature extraction within fragments. Interfragment information is combined through the pyramid design structure to improve the model’s receptive field and fully exploit the spectral properties. During the pyramidal fusion process, the information gain of the final extracted features in the spectrum has been enhanced by a factor of 9.24 compared to the feature extraction stage within the fragment, and the information entropy has been enhanced by a factor of 13. The fragment-fusion transformer achieved a spectral recognition accuracy of 94.5%, which is 4% higher compared to the method without fragmentation and fusion processes on the test set of cell Raman spectroscopy identification experiments. In comparison to common spectral classification models such as KNN, SVM, logistic regression, and CNN, fragment-fusion transformer has achieved 4.4% higher accuracy than the best-performing CNN model. Fragment-fusion transformer method has the potential to serve as a general framework for discretization in the field of continuous spectral data analysis and as a research tool for analyzing the intrinsic information within spectra.

中文翻译：

Fragment-Fusion Transformer：基于深度学习的连续单细胞拉曼光谱分析离散化方法

拉曼光谱已成为监测细胞水平生化变化的重要单细胞分析工具。然而，拉曼光谱数据通常表现为具有高维特征的连续数据，与离散序列不同，由于缺乏离散化，限制了基于深度学习的算法在数据分析中的应用。本文提出了一种称为片段融合变压器的模型，该模型将基于连续光谱的内在特征的离散片段化与片段内特征的提取和片段间特征的融合相结合。该模型将基于内在特征的光谱分段与变压器集成，构建用于片段内特征提取的片段变压器块。通过金字塔设计结构组合片段间信息，以提高模型的感受野并充分利用光谱特性。在金字塔融合过程中，最终提取的谱中特征的信息增益比片段内特征提取阶段增强了9.24倍，信息熵增强了13倍。 -fusion Transformer在细胞拉曼光谱识别实验的测试集上实现了94.5%的光谱识别准确率，比没有裂解和融合过程的方法提高了4%。与 KNN、SVM、逻辑回归和 CNN 等常见谱分类模型相比，片段融合 Transformer 的准确率比性能最佳的 CNN 模型高出 4.4%。片段融合变换方法有潜力作为连续光谱数据分析领域离散化的通用框架，以及作为分析光谱内在信息的研究工具。

更新日期：2024-06-27

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