Statistical Classification for Raman Spectra of Tumoral Genomic DNA

We exploit Surface-Enhanced Raman Scattering (SERS) to investigate aqueous droplets of genomic DNA deposited onto silver-coated silicon nanowires, and we show that it is possible to efficiently discriminate between spectra of tumoral and healthy cells. To assess the robustness of the proposed technique, we develop two different statistical approaches, one based on the Principal Components Analysis of spectral data and one based on the computation of the l(2) distance between spectra. Both methods prove to be highly efficient, and we test their accuracy via the Cohen's K statistics. We show that the synergistic combination of the SERS spectroscopy and the statistical analysis methods leads to efficient and fast cancer diagnostic applications allowing rapid and unexpansive discrimination between healthy and tumoral genomic DNA alternative to the more complex and expensive DNA sequencing.

Automated summary

This study utilizes Surface-Enhanced Raman Scattering (SERS) to investigate genomic DNA in aqueous droplets deposited onto silver-coated silicon nanowires, and demonstrates the ability to efficiently differentiate between spectra of tumoral and healthy cells. Two statistical approaches, based on Principal Components Analysis and l(2) distance computation, are developed and proven to be highly efficient. The synergy of SERS spectroscopy and statistical analysis enables rapid and cost-effective discrimination of healthy and tumoral genomic DNA, providing an alternative to complex and expensive DNA sequencing.