Peak selection in multidimensional data

• US 20050209789A1
• Filed: 03/07/2005
• Published: 09/22/2005
• Est. Priority Date: 08/24/2001
• Status: Active Grant

• Current Assignee: Caprion Proteomics Inc. (CellCarta Biosciences, Inc.)
• Original Assignee: PPD Biomarker Discovery Sciences LLC (CellCarta Biosciences, Inc.)
• Inventors: Hastings, Curtis A.
• Granted Patent: US 7,197,401 B2
• Time in Patent Office: Days
• US Class Current: 702/22
• CPC Class Codes: G01N 30/72 Mass spectrometers mass spectrometers per se H01J49/00
• CPC Class Codes: G01N 30/8624 Detection of slopes or peaks; baseline correction
• CPC Class Codes: G01N 30/8631 Peaks
• CPC Class Codes: G01N 30/8634 Peak quality criteria
• CPC Class Codes: H01J 49/0036 Step by step routines describing the handling of the data generated during a measurement
• CPC Class Codes: Y10T 436/24 Nuclear magnetic resonance, electron spin resonance or other spin effects or mass spectrometry

Abstract

An automatic peak selection method for multidimensional data can efficiently select peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

Claims

1. A method for automatically detecting peaks in an n-dimensional data set, where n>

2, comprising;

independently applying m one-dimensional peak selection criteria to each data point in said data set, wherein 2≦

m≦

n, each peak selection criterion corresponding to a particular dimension; and

identifying candidate peaks in said data points, wherein each candidate peak satisfies p of said m applied selection criteria, wherein 2≦

p≦

m.

2. The method of claim 1, wherein m=n.

3. The method of claim 2, wherein p=n.

4. The method of claim 1, wherein one-dimensional selection criteria corresponding to the same dimension and applied to different data points are not necessarily identical.

5. The method of claim 1, wherein each one-dimensional selection criterion applied to a particular data point is computed from a one-dimensional component of said data set that includes said particular data point.

6. The method of claim 1, wherein each selection criterion comprises a threshold value.

7. The method of claim 6, wherein said threshold value is computed from a subset of said data points.

8. The method of claim 7, wherein said threshold value is computed from a median of said subset of said data points.

9. The method of claim 1, further comprising identifying peaks in said candidate peaks using a peak recognition algorithm.

10. The method of claim 9, wherein said peak recognition algorithm comprises clustering said candidate peaks.

11. The method of claim 9, wherein said peak recognition algorithm comprises an algorithm selected from the group consisting of lineshape analysis, Bayesian analysis, maximum likelihood analysis, and isotope distribution analysis.

12. The method of claim 1, wherein said n-dimensional data set comprises two-dimensional chromatography-spectrometry data.

13. The method of claim 12, wherein said n-dimensional data set comprises liquid chromatography-mass spectrometry data, and wherein said peaks represent ionized components of a chemical mixture.

14. The method of claim 1, further comprising acquiring said n-dimensional data set from an analytical instrument.

15. A method for detecting components in a chemical mixture, comprising:

subjecting said mixture to chromatography and mass spectrometry using an instrument;

acquiring a two-dimensional data set comprising mass spectra and mass chromatograms from said instrument;

computing a local noise threshold for each acquired mass spectrum and for each acquired mass chromatogram;

applying corresponding mass spectrum and mass chromatogram noise thresholds to each data point in said two-dimensional data set; and

identifying candidate peaks, wherein each candidate peak exceeds said corresponding mass spectrum noise threshold and said corresponding mass chromatogram noise threshold.

16. The method of claim 15, wherein said local noise threshold for said acquired mass spectrum is computed from a median of nonzero points in said mass spectrum.

17. The method of claim 15, wherein said local noise threshold for said acquired mass chromatogram is computed from a median of nonzero points in said mass chromatogram.

18. A program storage device accessible by a processor, tangibly embodying a program of instructions executable by said processor to perform method steps for automatically detecting peaks in an n-dimensional data set, where n≧

2, said method steps comprising;

independently applying m one-dimensional peak selection criteria to each data point in said data set, wherein 2≦

m≦

n, each peak selection criterion corresponding to a particular dimension; and

identifying candidate peaks in said data points, wherein each candidate peak satisfies p of said m applied selection criteria, wherein 2≦

p≦

m.