Method and apparatus for modeling mass spectrometer lineshapes

• US 20040254741A1
• Filed: 06/12/2003
• Published: 12/16/2004
• Est. Priority Date: 06/12/2003
• Status: Active Grant

• Current Assignee: Vern Norviel
• Original Assignee: Biospect Incorporated (Response Genetics, Inc)
• Inventors: Bitter, Hans, Ahmed, Zulfikar
• Granted Patent: US 7,072,772 B2
• Time in Patent Office: Days
• US Class Current: 702/28
• CPC Class Codes: H01J 49/40 Time-of-flight spectrometers H01J49/36 takes precedence

Abstract

Methods and apparatuses are disclosed that model the lineshapes of mass spectrometry data. Ions can be modeled with an initial distribution that models molecules as having multiple positions and/or energies prior to traveling in the mass spectrometer. These initial distributions can be pushed forward by time of flight functions. Fitting can be performed between the modeled lineshapes and empirical data. Filtering can greatly reduce dimensions of the empirical data, remove noise, compress the data, recover lost and/or damaged data.

Claims

1. A method of modeling mass spectra, comprising:

representing, with a modeled mass-to-charge distribution detected by a mass spectrometer, at least a first plurality of molecules of at least a first molecule type, wherein the modeled mass-to-charge distribution is based on at least a modeled initial distribution of one or more parameters descriptive of the first plurality of molecules, the modeled initial distribution representing at least the first plurality of molecules prior to traveling in the mass spectrometer, the one or more parameters affecting time-of-flight of the first plurality of molecules traveling in the mass spectrometer,

wherein the modeled initial distribution represents at least the first plurality of molecules as having a plurality of values for at least one parameter of the one or more parameters; and

comparing the modeled mass-to-charge distribution, and an empirical mass-to-charge distribution of at least the first plurality of molecules of at least the first molecule type.

2. The method of claim 1, wherein the one or more parameters includes at least one of:

position, energy, ionization, position focusing, extraction source shape, fringe effects of electric fields, and electronic hardware artifacts.

3. The method of claim 1, wherein the one or more parameters includes at least position and energy.

4. The method of claim 3, wherein the modeled mass-to-charge distribution is further based on at least modeling of the first plurality of molecules traveling at least one or more electric field-free regions of the mass spectrometer.

5. The method of claim 4, wherein the modeled mass-to-charge distribution is further based on at least modeling of the first plurality of molecules traveling at least one or more electric field regions of the mass spectrometer.

6. The method of claim 1, wherein the modeled initial distribution is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution, such that the modeled mass-to-charge distribution is based on at least the modeled initial distribution representing at least the first plurality of molecules prior to traveling in the mass spectrometer.

7. The method of claim 1, wherein the plurality of positions of the first plurality of molecules is represented at least by a Gaussian distribution.

8. The method of claim 7, wherein the modeled initial distribution representing the plurality of positions of the first plurality of molecules at least by the Gaussian distribution is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

9. The method of claim 1, wherein the plurality of positions of the first plurality of molecules is represented by one or more equations based on at least statistical mechanics of ion gases.

10. The method of claim 9, wherein the modeled initial distribution representing the plurality of positions of the first plurality of molecules at least by the one or more equations based on at least the statistical mechanics of ion gases is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

11. The method of claim 1, wherein the plurality of energies of the first plurality of molecules is represented at least by a Gaussian distribution.

12. The method of claim 11, wherein the modeled initial distribution representing the plurality of energies of the first plurality of molecules at least by the Gaussian distribution is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

13. The method of claim 1, wherein the plurality of energies of the first plurality of molecules is represented by one or more equations based on at least the statistical mechanics of ion gases.

14. The method of claim 13, wherein the modeled initial distribution representing the plurality of energies of the first plurality of molecules at least by the one or more equations based on at least the statistical mechanics of ion gases is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

15. The method of claim 1, further comprising:

detecting, with the mass spectrometer, the empirical mass-to-charge distribution of at least the first plurality of molecules of at least the first molecule type.

16. The method of claim 1, further comprising:

performing a fit between the empirical mass-to-charge distribution and the modeled mass-to-charge distribution.

17. The method of claim 16, wherein the fit includes at least a least squares fit.

18. The method of claim 16, wherein the fit includes at least a penalized least squares fit.

19. The method of claim 16, further comprising:

filtering the fit.

20. The method of claim 19, wherein filtering the fit includes hard thresholding.

21. The method of claim 19, wherein filtering the fit includes soft thresholding.

22. The method of claim 19, wherein filtering the fit includes filtering with a filter bank.

23. The method of claim 19, wherein filtering uses at least one of wavelet basis vectors and vaguelette basis vectors.

24. The method of claim 16, wherein performing the fit includes:

deriving a plurality of model basis vectors from at least the modeled mass-to-charge distribution; and

representing the empirical mass-to-charge distribution with a weighted sum of the plurality of model basis vectors.

25. The method of claim 24, wherein the plurality of model basis vectors includes wavelet vectors.

26. The method of claim 25, wherein the wavelet vectors include standard wavelet vectors.

27. The method of claim 25, wherein the wavelet vectors include wavelet vectors derived at least from one or more lineshapes of the modeled mass-to-charge distribution.

28. The method of claim 25, wherein the wavelet vectors include wavelet vectors derived at least from one or more lineshapes of the empirical mass-to-charge distribution.

29. The method of claim 24, wherein the plurality of model basis vectors includes vaguelette vectors.

30. The method of claim 29, wherein the vaguelette vectors are derived at least from one or more wavelet vectors.

31. The method of claim 29, wherein the vaguelette vectors include vaguelette vectors derived at least from one or more lineshapes of the modeled mass-to-charge distribution.

32. The method of claim 29, wherein the vaguelette vectors include vaguelette vectors derived at least from one or more lineshapes of the empirical mass-to-charge distribution.

33. The method of claim 24, further comprising:

filtering the weighted sum of the plurality of model basis vectors.

34. The method of claim 33, wherein filtering the plurality of model basis vectors includes hard thresholding.

35. The method of claim 33, wherein filtering the plurality of model basis vectors includes soft thresholding.

36. The method of claim 1, such that the modeled mass-to-charge distribution shows noise reduction compared to the empirical mass-to-charge distribution.

37. The method of claim 1, such that the modeled mass-to-charge distribution shows data compression compared to the empirical mass-to-charge distribution.

38. The method of claim 1, such that the modeled mass-to-charge distribution shows data recovery compared to the empirical mass-to-charge distribution.

39. The method of claim 1, such that the modeled mass-to-charge distribution shows dimensionality reduction compared to the empirical mass-to-charge distribution.

40. The method of claim 1, such that the modeled mass-to-charge distribution is used for pattern recognition.

41. The method of claim 1, further comprising:

finding one or more proteins indicative of one or more diseases based at least partly on the modeled mass-to-charge distribution.

42. The method of claim 41, further comprising:

diagnosing, based at least partly on the one or more proteins, at least one person with the one or more diseases.

43. The method of claim 1, further comprising:

diagnosing at least one person with one or more diseases based at least partly on the modeled mass-to-charge distribution.

44. A method of modeling mass spectra, comprising:

representing, with a modeled mass-to-charge distribution detected by a mass spectrometer, at least a first plurality of molecules of at least a first molecule type, wherein the modeled mass-to-charge distribution is based on at least a modeled initial distribution of one or more parameters descriptive of the first plurality of molecules, the modeled initial distribution representing at least the first plurality of molecules prior to traveling in the mass spectrometer, the one or more parameters affecting time-of-flight of the first plurality of molecules traveling in the mass spectrometer,

wherein the modeled mass-to-charge distribution is derived at least partly from a push forward probability density transformation of the modeled initial distribution by one or more functions based at least partly on a configuration of the mass spectrometer,

wherein the modeled initial distribution represents at least the first plurality of molecules as having a plurality of values for at least one parameter of the one or more parameters.

45. The method of claim 44, wherein the modeled initial distribution represents at least the first plurality of molecules as having the plurality of positions and the plurality of energies.

46. The method of claim 45, wherein the modeled mass-to-charge distribution is further based on at least modeling of the first plurality of molecules traveling at least one or more electric field-free regions of the mass spectrometer.

47. The method of claim 46, wherein the modeled mass-to-charge distribution is further based on at least modeling of the first plurality of molecules traveling at least one or more electric field regions of the mass spectrometer.

48. The method of claim 44, wherein the plurality of positions of the first plurality of molecules is represented at least by a Gaussian distribution.

49. The method of claim 44, wherein the modeled initial distribution representing the plurality of positions of the first plurality of molecules at least by the Gaussian distribution is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

50. The method of claim 44, wherein the plurality of positions of the first plurality of molecules is represented by one or more equations based on at least statistical mechanics of ion gases.

51. The method of claim 50, wherein the modeled initial distribution representing the plurality of positions of the first plurality of molecules at least by the one or more equations based on at least the statistical mechanics of ion gases is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

52. The method of claim 44, wherein the plurality of energies of the first plurality of molecules is represented at least by a Gaussian distribution.

53. The method of claim 52, wherein the modeled initial distribution representing the plurality of energies of the first plurality of molecules at least by the Gaussian distribution is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

54. The method of claim 44, wherein the plurality of energies of the first plurality of molecules is represented by one or more equations based on at least the statistical mechanics of ion gases.

55. The method of claim 54, wherein the modeled initial distribution representing the plurality of energies of the first plurality of molecules at least by the one or more equations based on at least the statistical mechanics of ion gases is pushed forward by one or more equations representing one or more time of flight functions to at least partly yield the modeled mass-to-charge distribution.

56. The method of claim 44, further comprising:

comparing the modeled mass-to-charge distribution and an empirical mass-to-charge distribution of at least the first plurality of molecules of at least the first molecule type; and

performing a fit between the empirical mass-to-charge distribution and the modeled mass-to-charge distribution.

57. The method of claim 56, wherein the fit includes at least a least squares fit.

58. The method of claim 56, wherein the fit includes at least a penalized least squares fit.

59. The method of claim 56, further comprising:

filtering the fit.

60. The method of claim 59, wherein filtering the fit includes hard thresholding.

61. The method of claim 59, wherein filtering the fit includes filtering with a filter bank.

62. The method of claim 59, wherein the filtering uses at last one of wavelet basis vectors and vaguelette basis vectors.

63. The method of claim 59, wherein filtering the fit includes soft thresholding.

64. The method of claim 56, wherein performing the fit includes:

deriving a plurality of model basis vectors from at least the modeled mass-to-charge distribution; and

representing the empirical mass-to-charge distribution with a weighted sum of the plurality of model basis vectors.

65. The method of claim 64, wherein the plurality of model basis vectors includes wavelet vectors.

66. The method of claim 65, wherein the wavelet vectors include standard wavelet vectors.

67. The method of claim 65, wherein the wavelet vectors include wavelet vectors derived at least from one or more lineshapes of the modeled mass-to-charge distribution.

68. The method of claim 65, wherein the wavelet vectors include wavelet vectors derived at least from one or more lineshapes of the empirical mass-to-charge distribution.

69. The method of claim 64, wherein the plurality of model basis vectors includes vaguelette vectors.

70. The method of claim 69, wherein the vaguelette vectors are derived at least from one or more wavelet vectors.

71. The method of claim 69, wherein the vaguelette vectors include vaguelette vectors derived at least from one or more lineshapes of the modeled mass-to-charge distribution.

72. The method of claim 69, wherein the vaguelette vectors include vaguelette vectors derived at least from one or more lineshapes of the empirical mass-to-charge distribution.

73. The method of claim 64, further comprising:

filtering the weighted sum of the plurality of model basis vectors.

74. The method of claim 73, wherein filtering the plurality of model basis vectors includes hard thresholding.

75. The method of claim 73, wherein filtering the plurality of model basis vectors includes soft thresholding.

76. The method of claim 44, such that the modeled mass-to-charge distribution shows noise reduction when compared to the empirical mass-to-charge distribution.

77. The method of claim 44, such that the modeled mass-to-charge distribution shows data compression when compared to the empirical mass-to-charge distribution.

78. The method of claim 44, such that the modeled mass-to-charge distribution shows data recovery when compared to the empirical mass-to-charge distribution.

79. The method of claim 44, such that the modeled mass-to-charge distribution shows dimensionality reduction when compared to the empirical mass-to-charge distribution.

80. The method of claim 44, such that the modeled mass-to-charge distribution is used for pattern recognition.

81. The method of claim 44, further comprising:

finding one or more proteins indicative of one or more diseases based at least partly on the modeled mass-to-charge distribution.

82. The method of claim 81, further comprising:

diagnosing, from the one or more proteins, at least one person with the one or more diseases.

83. The method of claim 44, further comprising:

diagnosing at least one person with one or more diseases based at least partly on the modeled mass-to-charge distribution.

84. A method of processing mass spectra, comprising:

accessing a mass spectrum; and

decorrelating at least two overlapping peaks of the mass spectrum.

85. The method of claim 84, wherein at least part of the mass spectrum is simulated.

86. The method of claim 84, wherein at least part of the mass spectrum is empirical.

87. The method of claim 84, wherein at least part of the mass spectrum is derived at least partly from a push forward probability density transformation of a modeled initial distribution by one or more functions based at least partly on a configuration of a mass spectrometer.

88. The method of claim 84, wherein the mass spectrum was taken from at least one biological sample.