Spectrum and Signal Analysis

Pattern or compound identification; alignment and calibration of spectra and signals to peaks and other data sets

Characterize mass spectrometry data by detecting peaks and aligning them with references. Detect potential biomarkers by ranking features and using other machine learning techniques. Visualize mass spectra data and plot a set of peak lists from LC/MS or GC/MS data.

Functions

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Detect and Align Peaks

`mspeaks`	Convert raw peak data to peak list (centroided data)
`mspalign`	Align mass spectra from multiple peak lists from LC/MS or GC/MS data set
`msalign`	Align peaks in signal to reference peaks
`samplealign`	Align two data sets containing sequential observations by introducing gaps
`isotopicdist`	Calculate high-resolution isotope mass distribution and density function

Visualize Mass Spectra

`msheatmap`	Create pseudocolor image of set of mass spectra
`msdotplot`	Plot set of peak lists from LC/MS or GC/MS data set
`msviewer`	Explore mass spectrum or set of mass spectra
`traceplot`	Draw nucleotide trace plots

Rank Features and Train Classifiers

`metafeatures`	Attractor metagene algorithm for feature engineering using mutual information-based learning
`rankfeatures`	Rank key features by class separability criteria
`randfeatures`	Generate randomized subset of features
`classperf`	Evaluate classifier performance
`crossvalind`	Generate indices for training and test sets

Topics

Mass Spectrometry Data Analysis
The mass spectrometry functions preprocess and classify raw data from SELDI-TOF and MALDI-TOF spectrometers and use statistical learning functions to identify patterns.

Featured Examples

Genetic Algorithm Search for Features in Mass Spectrometry Data

Use the Global Optimization Toolbox with the Bioinformatics Toolbox™ to optimize the search for features to classify mass spectrometry (SELDI) data.

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