SpectraX: A Straightforward Tool for Principal Component Analysis-Based Spectral Analysis.
Jian YuHaidy MetwallyJennifer KolwichHailey TommRachel KlotzChang LiuJ C Yves Le BlancThomas R CoveyAvena Clara RossRichard David OleschukPublished in: Journal of the American Society for Mass Spectrometry (2024)
The analysis of complex spectra is an important component of direct/ambient mass spectrometry (MS) applications such as natural product screening. Unlike chromatography-based metabolomics or proteomics approaches, which rely on software and algorithms, the work of spectral screening is mostly performed manually in the initial stages of research and relies heavily on the experience of the analyst. As a result, throughput and spectral screening reliability are problematic when dealing with large amounts of data. Here, we present SpectraX, a MATLAB-based application, which can analyze MS spectra and quickly locate m / z features from them. Principal component analysis (PCA) is used to analyze the data set, and scoring plots are presented to help in understanding the clustering of data. The algorithm uses mass to charge ( m / z ) features to produce a list of potential natural products.
Keyphrases
- mass spectrometry
- liquid chromatography
- optical coherence tomography
- electronic health record
- machine learning
- gas chromatography
- high performance liquid chromatography
- capillary electrophoresis
- big data
- high resolution
- multiple sclerosis
- deep learning
- data analysis
- ms ms
- air pollution
- rna seq
- dual energy
- single cell
- particulate matter
- artificial intelligence
- computed tomography
- magnetic resonance
- climate change
- high speed
- contrast enhanced