Method for analyzing mass spectra

• US 7,027,933 B2
• Filed: 09/23/2003
• Issued: 04/11/2006
• Est. Priority Date: 11/16/2000
• Status: Expired due to Fees

• Current Assignee: Bio-Rad Laboratories Incorporated
• Original Assignee: Ciphergen Biosystems Incorporated
• Inventors: Rich, William E., Gavin, Edward J., Braginsky, Leonid, Fung, Eric T., Paulse, Chris D.
• Primary Examiner(s): Barlow, John
• Assistant Examiner(s): LAU, TUNG S
• Application Number:
  US10/669,499
• Publication Number:
  US 20040059530A1
• Time in Patent Office: 931 Days
• Field of Search: 702/27, 702/76, 702/28, 702/22, 702/30, 702/23, 250/282, 250/288, 250/291, 250/292, 250/360, 713/193, 707/1, 422/70, 436/518, 435/7.1, 435/4
• US Class Current: 702/27
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: H01J 49/0036 Step by step routines describing the handling of the data generated during a measurement

Abstract

A method that analyzes mass spectra using a digital computer is disclosed. The method includes entering into a digital computer a data set obtained from mass spectra from a plurality of samples. Each sample is, or is to be assigned to a class within a class set having two or more classes and each class is characterized by a different biological status. A classification model is then formed. The classification model discriminates between the classes in the class set.

Claims

1. A method for analyzing mass spectra, the method comprising:

detecting signals including signal intensifies in a plurality of spectra, wherein each spectrum in the plurality of spectra comprises data representing signal strength as a function of time-of-flight, mass-to-charge ratio, or a value derived from time-of-flight or mass-to-charge ratio;

forming at least one signal cluster by clustering signals with similar times-of-flight, mass-to-charge ratios, or values derived from times-of-flights or mass-to-charge ratios;

determining a predetermined number of signals, wherein the predetermined number of signals represents a minimum number of signals for a selected signal cluster;

selecting one or more signal clusters from the plurality of signal clusters if the number of signals in a signal cluster meets or exceeds the predetermined number of signals and excluding one or more signal clusters from the plurality of signal clusters if the number of signals in a signal cluster is below the predetermined number of signals; and

selecting the times-of-flights, the mass-to-charge ratios, or the values derived from the times-of-flights or the mass-to-charge ratios of the selected one or more signal clusters.

2. The method of claim 1 wherein the plurality of mass spectra is a first plurality of mass spectra and wherein the method further comprises:

forming a second plurality of mass spectra, wherein the second plurality of mass spectra is formed using the selected times-of flights, the mass-to-charge ratios, or the values derived from the selected times-of flights or mass-to-charge ratios.

3. The method of claim 2 further comprising normalizing signal intensity values in the second plurality of mass spectra.

4. The method of claim 2

wherein the plurality of mass spectra is from a plurality of samples, and wherein each sample is, or is to be assigned to a class within a class set comprising two or more classes, each class being characterized by a different biological status.

5. The method of claim 2 further comprising modifying the second plurality of mass spectra by adding estimates for missing signals.

6. The method of claim 1 wherein the plurality of spectra is generated using a surface enhanced laser desorption ionization process.

7. A computer readable medium comprising:

code for detecting signals including signal intensities in a plurality of spectra, wherein each spectrum in the plurality of spectra comprises data representing signal strength as a function of time-of-flight, mass-to-charge ratio, or a value derived from time-of-flight or mass-to-charge ratio;

code for forming at least one signal cluster by clustering signals with similar times-of-flights, mass-to-charge ratios, or values derived from times-of-flights or mass-to-charge ratios;

code for determining a redetermined number of signals, wherein the predetermined number of signals represents a minimum number of signals for a selected signal cluster;

code for selecting one or more signal clusters from the plurality of signal clusters of the number of signals in a signal cluster meets or exceeds the predetermined number of signals and excluding one or more signal clusters from the plurality of signal clusters if the number of signals in a signal cluster is below the predetermined number of signals; and

code for selecting the times-of-flights, the mass-to-charge ratios, or the values derived from the times-of-flights or the mass-to-charge ratios of the selected one or more signal clusters.

8. The computer readable medium of claim 7 wherein the plurality of mass spectra is a first plurality of mass spectra and wherein the computer readable medium further comprises:

code for forming a second plurality of mass spectra, wherein the second plurality of mass spectra is formed using the times-of-flights, the mass-to-charge ratios, or the values derived from the times-of-flights or mass-to-charge ratios obtained from the selected signal clusters.

9. The computer readable medium of claim 8 further comprising code for normalizing signal intensity values in the second plurality of mass spectra.

10. The computer readable medium of claim 8 further comprising code for modifying the second plurality of mass spectra by adding estimates for missing signals.

11. A system comprising:

a gas phase ion spectrometer;

a digital computer adapted to process data from the gas phase ion spectrometer; and

the computer readable medium of claim 7 in operative association with the digital computer.

12. The computer readable medium of claim 7 wherein the plurality of spectra is generated using a surface enhanced laser desorption ionization process.