Data and code on serum Raman spectroscopy as an efficient primary screening of coronavirus disease in 2019 (COVID-19)
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
This fileset consists of 13 data files, 1 code file and 2 ReadMe files.
The dataset data.mat is in .mat file format and therefore not openly-accessible. The following datasets, are an openly-accessible version of the .mat file:
Fig2_1.txt in .txt file format
Fig2_2.txt in .txt file format
Fig2_3.txt in .txt file format
Fig2_4.txt in .txt file format
Fig2_5.txt in .txt file format
Fig2_6.txt in .txt file format
raw_COVID.txt in .txt file format
raw_Helthy.txt in .txt file format
raw_Suspected.txt in .txt file format
raw_Tube.txt in .txt file format
table2_data.txt in .txt file format
wave_number.txt in .txt file format
The code file is the following: code.m in .m file format
The two ReadMe files are the following: readme.txt in .txt file format and readme.m in .m file format.
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.
Software needed to access data: data.mat can only be accessed using the Matlab software. Running the code code.m also requires Matlab.
Study aims and methodology: 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.
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.