We have developed analytical techniques as well as data processing and data analysis tools. Please follow the links to the resources that we have made freely available.

Analytical procedures we have developed 

• Standard Operating Procedures (SOPs) for Metabolomics and SERS

Here are some of our published approaches in Nature Protocols:

• Large scale metabolomics of serum and plasma using GC-MS and LC-MS
• A proposed framework to evaluate the quality and reliability of targeted metabolomics assays
• Metabolic profiling of suspension-cultured mammalian cells

Chemometrics

Here is a link to a collection of very useful on-line resources

Below is a list of code from BioSpec and others:

• Cluster Toolbox on Github – our BioSpec.Net data analysis Matlab code
• Machine learning on GitHub – our BioSpec.Net our R code for PLS-DA, SVM, RFs with resampling
• PyChem – our BioSpec.Net GUI data analysis Python code
• hyperSpec – code written in R from Claudia Beleites
• Matlab tools box in Github from Alex Henderson for handling hyperspectral data generated by SIMS, FTIR and Raman instruments.
• CLIRSPEC Summer School – Matlab/Octave code from @AlexHenderson00 in Github
• Matlab tutorial with code from 2003

Here are some useful papers highlighting need for robust data analysis:

• A BioSpec trends paper on conducting chemometrics with rigour: Mind your Ps and Qs.
• A BioSpec paper on different validation methods in metabolomics: Validating supervised learning methods.
• A BioSpec tutorial review on metabolomics and PLS-DA – a marriage of convenience or a shotgun wedding
• From John Ioannidis and his group a Manifesto for Reproducible Science
• A Dave Broadhurst and Doug Kell perspective on Avoiding False Discoveries
• George Poste on Bring on the Biomarkers
• Rasmus Bro on How to Overfit Data