Systems and Methods for Feature Detection in Mass Spectrometry Using Singular Spectrum Analysis

1 . A system for detecting a feature from mass spectrometry data, comprising:

a mass spectrometer that performs a plurality of scans of a sample producing mass spectrometry data; and

a processor in communication with the mass spectrometer that

obtains the mass spectrometry data from the mass spectrometer,

performs singular spectrum analysis on the mass spectrometry data in which one or more components other than the highest ranked component are grouped in a set and the one or more components grouped in the set are summed producing reconstructed data, and

detects a feature of the mass spectrometry data by analyzing an aspect of the reconstructed data.

2 . The system of claim 1 , wherein the mass spectrometry data comprises a mass spectrum.

3 . The system of claim 1 , wherein the mass spectrometry data comprises a chromatogram.

4 . The system of claim 1 , wherein the number of the one or more components other than the highest ranked component that are grouped in the set is based on a sub-linear function.

5 . The system of claim 4 , wherein the processor performs singular spectrum analysis using a fixed window width and wherein the sub-linear function comprises a square root of the fixed window width.

6 . The system of claim 1 , wherein the one or more components other than the highest ranked component that are grouped in the set are consecutive components.

7 . The system of claim 1 , wherein the one or more components other than the highest ranked component that are grouped in the set are not consecutive components.

8 . The system of claim 1 , wherein the one or more components other than the highest ranked component that are grouped in the set are grouped based on a heuristic.

9 . The system of claim 8 , wherein the heuristic includes grouping one or more components other than the highest ranked component based on a correlation among the one or more components.

10 . The system of claim 1 , wherein analyzing an aspect of the reconstructed data comprises using locations of transitions from negative to positive values and from positive to negative values in the reconstructed data to detect bounds on a location of the feature in the mass spectrometry data.

11 . The system of claim 1 , wherein analyzing an aspect of the reconstructed data comprises using a location of a maximum in the reconstructed data to detect an apex of the feature in the mass spectrometry data.

12 . A method for detecting a feature from mass spectrometry data, comprising:

performing a plurality of scans of a sample producing mass spectrometry data using a mass spectrometer;

obtaining the mass spectrometry data from the mass spectrometer using a processor;

performing singular spectrum analysis on the mass spectrometry data in which one or more components other than the highest ranked component are grouped in a set and the one or more components grouped in the set are summed producing reconstructed data using the processor; and

detecting a feature of the mass spectrometry data by analyzing an aspect of the reconstructed data using the processor.

13 . The method of claim 12 , wherein the mass spectrometry data comprises a mass spectrum.

14 . The method of claim 12 , wherein the mass spectrometry data comprises a chromatogram.

15 . The method of claim 12 , wherein the number of the one or more components other than the highest ranked component that are grouped in the set is based on a sub-linear function.

16 . The method of claim 15 , wherein performing singular spectrum analysis on the mass spectrometry data comprises using a fixed window width and wherein the sub-linear function comprises a square root of the fixed window width.

17 . The method of claim 12 , wherein the one or more components other than the highest ranked component that are grouped in the set are consecutive components.

18 . The method of claim 12 , wherein the one or more components other than the highest ranked component that are grouped in the set are not consecutive components.

19 . The method of claim 12 , wherein the one or more components other than the highest ranked component that are grouped in the set are grouped based on a heuristic.

20 . The method of claim 19 , wherein the heuristic includes grouping one or more components other than the highest ranked component based on a correlation among the one or more components.

21 . The method of claim 12 , wherein analyzing an aspect of the reconstructed data comprises using locations of transitions from negative to positive values and from positive to negative values in the reconstructed data to detect bounds on a location of the feature in the mass spectrometry data.

22 . The method of claim 12 , wherein analyzing an aspect of the reconstructed data comprises using a location of a maximum in the reconstructed to detect an apex of the feature in the mass spectrometry data.

23 . A computer program product, comprising a tangible computer-readable storage medium whose contents include a program with instructions being executed on a processor so as to perform a method for detecting a feature from mass spectrometry data, the method comprising:

providing a system, wherein the system comprises distinct software modules, and wherein the distinct software modules comprise a measurement module and an detection module;

obtaining the mass spectrometry data from the mass spectrometer that performs a plurality of scans of a sample using the measurement module;

performing singular spectrum analysis on the mass spectrometry data in which one or more components other than the highest ranked component are grouped in a set and the one or more components grouped in the set are summed producing reconstructed data using the detection module; and

detecting a feature of the mass spectrometry data by analyzing an aspect of the reconstructed data using the detection module.