Data and code on serum Raman spectroscopy as an efficient primary screening of coronavirus disease in 2019 (COVID-19)

<b>Please note that there is no peer-reviewed publication associated with this data record.</b><br><br>This fileset consists of 13 data files, 1 code file and 2 ReadMe files.The dataset <b>data.mat</b> is in .mat file format and therefore not openly-accessible. The following datasets, are an openly-accessible version of the .mat file:<br><b>Fig2_1.txt</b> in .txt file format<b>Fig2_2.txt</b> in .txt file format<b>Fig2_3.txt</b> in .txt file format<b>Fig2_4.txt</b> in .txt file format<b>Fig2_5.txt </b>in .txt file format<b>Fig2_6.txt</b> in .txt file format<b>raw_COVID.txt</b> in .txt file format<b>raw_Helthy.txt</b> in .txt file format<b>raw_Suspected.txt</b> in .txt file format<b>raw_Tube.txt </b>in .txt file format<b>table2_data.txt </b>in .txt file format<b>wave_number.txt</b> in .txt file formatThe code file is the following: <b>code.m</b> in .m file formatThe two ReadMe files are the following: <b>readme.txt </b>in .txt file format and <b>readme.m</b> in .m file format.<br>Data in Fig2_1.txt, Fig2_2.txt, Fig2_3.txt, Fig2_4.txt, Fig2_5.txt and Fig2_6.txt were used to plot Figure 2 in the related manuscript.raw_COVID.txt contains the raw Raman spectroscopy data from the serum samples obtained from the 53 confirmed COVID-19 patients.raw_Helthy.txt contains the raw Raman spectroscopy data from the serum samples obtained from healthy individuals.raw_Suspected.txt contains the raw Raman spectroscopy data from the serum samples obtained from suspected cases (individuals suspected of COVID-19 infection)raw_Tube.txt contains the raw spectra data from cryopreservation tubes with saline solution inside.wave_number.txt contains data of the Raman Spectrum shift.table2_data.txt was used to generate Table 2 in the related manuscript.The code code.m was used for data processing.<br><b>Software needed to access data</b>: data.mat can only be accessed using the Matlab software. Running the code code.m also requires Matlab.<br><b>Study aims and methodology</b>: The recommended diagnosis method for the coronavirus disease (COVID-19 is a qPCR-based technique, however, it is a time consuming, expensive, and a sample dependent procedure with relative high false negative ratio. The aim of this study was to develop a widely available, cheap and quick method to diagnose COVID-19 disease based on Raman spectroscopy.A total of 157 serum samples were collected from 53 confirmed patients, 54 suspected cases (fever but not COVID-19) and 50 healthy controls. Raman spectroscopy was used to analyse these samples and the machine learning support vector machine (SVM) method were applied to the spectral dataset to build a diagnostic algorithm.<br>The experimental set up consisted of a Volume Phase Holographic (VPH) spectrograph, deep-cooled CCD camera, and a Raman probe and laser. A total of 2355 spectra from 157 individuals were imported to MATLAB (R2013a) software (Math-200 works, Inc.).For more details on the methodology, please read the related article.<br>

请注意：本数据记录未关联任何经过同行评审的出版物。

本数据集包含13个数据文件、1个代码文件与2份自述文件。其中数据集**data.mat**采用.mat文件格式，无法公开获取；以下数据集为该.mat文件的公开可用版本：
**Fig2_1.txt**（.txt格式）、**Fig2_2.txt**（.txt格式）、**Fig2_3.txt**（.txt格式）、**Fig2_4.txt**（.txt格式）、**Fig2_5.txt**（.txt格式）、**Fig2_6.txt**（.txt格式）、**raw_COVID.txt**（.txt格式）、**raw_Helthy.txt**（.txt格式）、**raw_Suspected.txt**（.txt格式）、**raw_Tube.txt**（.txt格式）、**table2_data.txt**（.txt格式）及**wave_number.txt**（.txt格式）。

代码文件为**code.m**，采用.m文件格式。

两份自述文件分别为**readme.txt**（.txt格式）与**readme.m**（.m文件格式）。

Fig2_1.txt、Fig2_2.txt、Fig2_3.txt、Fig2_4.txt、Fig2_5.txt及Fig2_6.txt中的数据用于绘制相关手稿中的图2。

raw_COVID.txt收录了53名确诊新型冠状病毒肺炎（COVID-19）患者血清样本的原始拉曼光谱（Raman spectroscopy）数据。
raw_Helthy.txt收录了健康个体血清样本的原始拉曼光谱数据。
raw_Suspected.txt收录了疑似病例（疑似感染COVID-19的个体）血清样本的原始拉曼光谱数据。
raw_Tube.txt收录了内部盛放生理盐水的低温保存管的原始光谱数据。
wave_number.txt收录了拉曼光谱位移相关数据。
table2_data.txt用于生成相关手稿中的表2。

代码文件code.m用于完成数据处理工作。

**数据访问所需软件**：data.mat仅可通过Matlab软件读取，运行code.m也需依赖Matlab环境。

**研究目标与方法**：当前新型冠状病毒肺炎（COVID-19）的推荐诊断方案为基于定量聚合酶链式反应（qPCR）的技术，但该方法存在耗时久、成本高、依赖样本且假阴性率相对较高的缺陷。本研究旨在开发一种基于拉曼光谱的、可及性广、低成本且快速的COVID-19诊断方法。

本研究共收集157份血清样本，涵盖53名确诊患者、54名疑似病例（发热但未确诊COVID-19）及50名健康对照个体。研究采用拉曼光谱对上述样本开展分析，并将机器学习中的支持向量机（Support Vector Machine, SVM）方法应用于光谱数据集，以构建诊断算法。

实验装置包含体相全息（Volume Phase Holographic, VPH）光谱仪、深冷CCD相机、拉曼探针与激光器。共将来自157名个体的2355条光谱数据导入MATLAB（R2013a）软件（MathWorks公司）。

如需了解方法学的更多细节，请查阅相关研究文章。