Methods for time-alignment of liquid chromatography-mass spectrometry data
AssigneePPD Biomarker Discovery Sciences, LLC
InventorScott Norton
Life of this patent
Estimating the term…
Abstract
Nonlinear retention time variations in chromatography-mass spectrometry data sets are adjusted by time-alignment methods, enabling automated comparison of spectra for differential phenotyping and other applications.
Claims8
A plurality of chromatography-mass spectrometry data sets aligned according to a method comprising:
- a) computing a distance function between said data sets in dependence on at least two mass chromatograms from each data set;
- and
- b) aligning said data sets by minimizing said distance function to obtain aligned data sets.
A program storage device accessible by a processor, tangibly embodying a program of instructions executable by said processor to perform method steps for a method for time-aligning chromatography-mass spectrometry data sets, each comprising a plurality of mass chromatograms, said method steps comprising:
- a) computing a distance function between said data sets in dependence on at least two mass chromatograms from each data set;
- and
- b) aligning said data sets by minimizing said distance function to obtain aligned data sets.
A plurality of samples compared according to a method comprising:
- a) performing chromatography-mass spectrometry on each sample to obtain at least two data sets, each comprising a plurality of mass chromatograms;
- b) computing a distance function between two selected data sets in dependence on at least two mass chromatograms from each selected data set;
- c) aligning said selected data sets by minimizing said distance function to obtain aligned selected data sets;
- and
- d) comparing said aligned selected data sets.
A biomarker differentiating two cohorts identified by the method comprising:
- a) comparing at least two samples, at least one each of said samples representing a different one of said two cohorts according to the method comprising:
- i) performing chromatography-mass spectrometry on each sample to obtain at least two data sets, each comprising a plurality of mass chromatograms;
- ii) computing a distance function between two selected data sets in dependence on at least two mass chromatograms from each selected data set;
- iii) aligning said selected data sets by minimizing said distance function to obtain aligned selected data sets;
- and
- iv) comparing said aligned selected data sets;
- and
- b) identifying a biomarker in dependence on said comparison.
A plurality of chromatography-mass spectrometry data sets aligned according to a method comprising:
- a) selecting peaks in said data sets;
- b) identifying potentially corresponding peaks from said selected peaks;
- and
- c) performing a locally-weighted regression smoothing on said potentially corresponding peaks to obtain aligned data sets.
A program storage device accessible by a processor, tangibly embodying a program of instructions executable by said processor to perform method steps for a method for time-aligning two-dimensional chromatography-mass spectrometry data sets, said method steps comprising:
- a) selecting peaks in said data sets;
- b) identifying potentially corresponding peaks from said selected peaks;
- and
- c) performing a locally-weighted regression smoothing on said potentially corresponding peaks to obtain aligned data sets.
A plurality of samples compared according to a method comprising:
- a) performing chromatography-mass spectrometry on each sample to obtain at least two two-dimensional data sets;
- b) selecting peaks in two selected data sets;
- c) identifying potentially corresponding peaks from said selected peaks;
- d) performing a locally-weighted regression smoothing on said potentially corresponding peaks to obtain aligned selected data sets;
- and
- e) comparing said aligned selected data sets.
A biomarker identified by the method comprising:
- a) performing chromatography-mass spectrometry on each sample to obtain at least two two-dimensional data sets;
- b) selecting peaks in two selected data sets;
- c) identifying potentially corresponding peaks from said selected peaks;
- d) performing a locally-weighted regression smoothing on said potentially corresponding peaks to obtain aligned selected data sets;
- and
- e) comparing said aligned selected data sets.
Dependencies are inferred from the claim text.