Application of random forest based approaches to surface-enhanced Raman scattering data

Area of Science:

• Analytical Chemistry
• Biotechnology
• Computational Biology

Background:

• Surface-enhanced Raman scattering (SERS) is a powerful technique for biological sample analysis.
• Extracting meaningful data from SERS, especially without reporter molecules, presents significant challenges.
• Advanced computational methods are needed to interpret complex SERS spectral data.

Purpose of the Study:

• To evaluate the suitability of random forest algorithms for analyzing label-free SERS data from biological samples.
• To develop and present a simulation framework for generating SERS data.
• To demonstrate the capability of random forest methods in identifying significant SERS signals and spectral groups.

Main Methods:

• Development of a SERS data simulation framework.
• Application of random forest algorithms, including Boruta and surrogate minimal depth (SMD), for signal selection.
• Utilizing the Learner of Functional Enrichment (LeFE) method for analyzing spectral group relevance.
• Comparative analysis of different random forest approaches.

Main Results:

• Demonstrated ability to identify important SERS signals using Boruta and SMD.
• Showcased the effectiveness of LeFE in evaluating predefined spectral groups.
• Revealed the potential for analyzing relationships between different SERS signals.
• Validated the utility of a simulation framework for SERS data analysis.

Conclusions:

• Random forest approaches, specifically Boruta, SMD, and LeFE, are highly suitable for analyzing label-free SERS data.
• The combination of random forest methods and SERS data offers a promising avenue for sophisticated analysis of complex biological samples.
• This approach enhances the interpretability and utility of SERS in biological and chemical research.