ADAP Aligner

This alignment algorithm has been developed as part of ADAP-GC v1.0, Automatic Data Analysis Pipeline for processing GC-MS metabolomics data.

For details, see Jiang, W.; Qiu, Y.; Ni, Y.; Su, M.; Jia, W.; Du, X.: An automated data analysis pipeline for GC-TOF-MS metabonomics studies. Journal of proteome research 2010, 9 (11), 5974-81

Requirements

ADAP Aligner requires mass spectra to be constructed prior to the alignment (e.g. using Spectral Deconvolution or CAMERA). A typical workflow where this alignment is used can be as following:

  1. Raw data methods / Raw data import imports raw data files
  2. Raw datamethods / Peak detection / Mass detection detects masses in the raw data
  3. Raw datamethods / Peak detection / ADAP Chromatogram builder builds extracted-ion chromatograms
  4. Peak list methods / Peak deteciton / Chromatogram deconvoltion detects peaks (features) in each chromatogram
  5. Peak list methods / Spectral deconvolution / Multivariate Curve Resolution combines the detected peaks (features) into analytes and builds pure fragmentation mass spectra for each analyte
  6. Peak list methods / Alignment / ADAP Aligner (GC) aligns the analytes produced by the previous step
  7. Peak list methods / Export/Import / Export to MSP file exports fragmentation mass spectra into MSP format

Description

ADAP Aligner aligns features based on their mass spectra and retention time similarity. This approach is different from Join Aligner that aligns peaks across all samples, using their m/z and retention time similarity. Instead, ADAP Aligner uses mass spectra and retention time to detect similar features in each sample and align them together. Due to the usage of mass spectra, this alignment approach is significantly different from the approach of Join Aligner. Therefore,

In fact, this algorithm is similar to Hierarchical Aligner (GC), but it uses a different clustering method.

Similarity between two features f1 and f2 is calculated by the following score:

S(f1, f2) = w Stime(f1, f2) + (1 - w) Sspec(f1, f2)

where Stime(f1, f2) is the relative retention time difference between two features and Sspec(f1, f2) is the spectrum similarity between two features.

Parameters