Method and apparatus for modeling mass spectrometer lineshapes
View Patent ↗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.
1. A method of analyzing mass spectra comprising:
determining an initial distribution of one or more parameters of at least a first molecule;
determining a theoretical modeled mass-to-charge distribution of at least said first molecule without having said first molecule travel in a mass spectrometer using said initial distribution of said one or more parameters; and
fitting said modeled mass-to-charge distribution to an empirical mass-to-charge distribution of at least said first molecule after it has traveled in said mass spectrometer to form a fitted modeled mass-to-charge distribution of at least said first molecule.
2. The method of claim 1 , wherein the fitting step includes:
deriving a plurality of model basis vectors from the modeled mass-to-charge distribution; and
representing the empirical mass-to-charge distribution with a weighted sum of the plurality of the model basis vectors.
3. The method of claim 2 , wherein the plurality of model basis vectors includes a wavelet vector.
4. The method of claim 3 , wherein the wavelet vector is a standard wavelet vector.
5. The method of claim 3 , wherein the wavelet vector is a wavelet vector derived from a lineshape of the modeled mass-to-charge distribution.
6. The method of claim 3 , wherein the wavelet vector is a wavelet vector derived from a lineshape of the empirical mass-to-charge distribution.
7. The method of claim 2 , wherein the plurality of model basis vectors includes a vaguelette vector.
8. The method of claim 7 , wherein the vaguelette vector is derived from a wavelet vector.
9. The method of claim 7 , wherein the vaguelette vectors is derived from a lineshape of the modeled mass-to-charge distribution.
10. The method of claim 7 , wherein the vaguelette vector is derived from a lineshape of the empirical mass-to-charge distribution.
11. The method of claim 2 , further comprising:
filtering the weighted sum of the plurality of model basis vectors.
12. The method of claim 11 , wherein said filtering step includes hard thresholding.
13. The method of claim 11 , wherein said filtering step includes soft thresholding.
14. The method of claim 1 , wherein said fitting step comprises filtering the fitted modeled mass-to-charge distribution.
15. The method of claim 14 , wherein said filtering step includes hard thresholding.
16. The method of claim 14 , wherein said filtering step includes soft thresholding.
17. The method of claim 14 , wherein said filtering step includes filtering with a filter bank.
18. The method of claim 14 , wherein said filtering step utilizes a wavelet basis vector or a vaguelette basis vector.
19. The method of claim 1 , wherein the fitting step includes an error function.
20. The method of claim 19 , wherein the error function is a squared error function or a penalized squared error function.
21. The method of claim 1 , wherein the fitted modeled mass-to-charge distribution is used for pattern recognition.
22. The method of claim 21 , wherein said pattern recognition is used for finding one or more proteins indicative of one or more diseases.
23. The method of claim 1 wherein said one or more parameters affect time-of-flight of said first molecule.
24. The method of claim 23 wherein said one or more parameters is selected from the group consisting of: initial position, initial energy, ionization, position focusing, extraction source shape, fringe effects of electric field, statistical mechanics of ion gasses, and electronic. hardware artifacts.
25. The method of claim 1 wherein said initial distribution of said one or more parameters is represented by a Gaussian distribution.
26. The method of claim 1 wherein said determining a modeled mass-to-charge distribution step utilizes a time-of-flight function.
27. The method of claim 1 wherein said fitting step involves scaling said modeled mass-to-charge distribution or said empirical mass-to-charge distribution to generate constant lineshape widths.
28. The method of claim 1 wherein said mass spectrometer is a time-of-flight mass spectrometer.
29. The method of claim 1 wherein said fitted modeled mass-to-charge distribution has reduced noise as compared to said empirical mass-to-charge distribution.
30. The method of claim 1 wherein said fitted modeled mass-to-charge distribution has compressed data as compared to said empirical mass-to-charge distribution.
31. The method of claim 1 wherein said fitted modeled mass-to-charge distribution includes recovered data as compared to said empirical mass-to-charge distribution.
32. The method of claim 1 wherein said fitted modeled mass-to-charge distribution has reduced dimensionality as compared to said empirical mass-to-charge distribution.
33. the method of claim 1 wherein said determining an initial distribution occurs prior to said first molecule traveling through said mass spectrometer.
34. A method of analyzing mass spectra comprising:
determining an initial distribution of one or more parameters of at least a first molecule;
determining a modeled mass-to-charge distribution of at least said first molecule using said initial distribution of said one or more parameters;
fitting said modeled mass-to-charge distribution to an empirical mass-to-charge distribution of at least said first molecule after it has traveled in a mass spectrometer to form a fitted modeled mass-to-charge distribution of at least said first molecule, wherein said fifing step includes:
deriving a plurality of model basis vectors from the modeled mass-to-charge distribution; and
representing the empirical mass-to-charge distribution with a weighted sum of said plurality of model basis vectors, wherein said plurality of model basis vectors includes a wavelet vector derived from a lineshape of said modeled mass-to-charge distribution.
35. A method of analyzing mass spectra comprising:
determining an initial distribution of one or more parameters of at least a first molecule;
determining a modeled mass-to-charge distribution of at least said first molecule using said initial distribution of said one or more parameters;
fitting said modeled mass-to-charge distribution to an empirical mass-to-charge distribution of at least said first molecule after it has traveled in a mass spectrometer to form a fitted modeled mass-to-charge distribution of at least said first molecule, wherein said fitting step includes:
deriving a plurality of model basis vectors from the modeled mass-to-charge distribution; and
representing the empirical mass-to-charge distribution with a weighted sum of said plurality of model basis vectors, wherein said plurality of model basis vectors includes a wavelet vector derived from a lineshape of said empirical mass-to-charge distribution.
36. A method of analyzing mass spectra comprising:
determining an initial distribution of one or more parameters of at least a first molecule;
determining a modeled mass-to-charge distribution of at least said first molecule using said initial distribution of said one or more parameters;
fitting said modeled mass-to-charge distribution to an empirical mass-to-charge distribution of at least said first molecule after it has traveled in a mass spectrometer to form a fitted modeled mass-to-charge distribution of at least said first molecule, wherein said fitting step includes:
deriving a plurality of model basis vectors from the modeled mass-to-charge distribution; and
representing the empirical mass-to-charge distribution with a weighted sum of said plurality of model basis vectors, wherein said plurality of model basis vectors includes a vaguelette vector derived from a lineshape of said modeled mass-to-charge distribution.
37. A method of analyzing mass spectra comprising:
determining an initial distribution of one or more parameters of at least a first molecule;
determining a modeled mass-to-charge distribution of at least said first molecule using said initial distribution of said one or more parameters;
fitting said modeled mass-to-charge distribution to an empirical mass-to-charge distribution of at least said first molecule after it has traveled in a mass spectrometer to form a fitted modeled mass-to-charge distribution of at least said first molecule, wherein said fitting step includes:
deriving a plurality of model basis vectors from the modeled mass-to-charge distribution; and
representing the empirical mass-to-charge distribution with a weighted sum of said plurality of model basis vectors, wherein said plurality of model basis vectors includes a vaguelette vector derived from a lineshape of said empirical mass-to-charge distribution.
38. A method of analyzing mass spectra comprising:
determining an initial distribution of one or more parameters of at least a first molecule;
determining a modeled mass-to-charge distribution of at least said first molecule using said initial distribution of said one or more parameters;
fitting said modeled mass-to-charge distribution to an empirical mass-to-charge distribution of at least said first molecule after it has traveled in a mass spectrometer to form a fitted modeled mass-to-charge distribution of at least said first molecule, wherein said determining a modeled mass-to-charge distribution step involves scaling said modeled mass-to-charge distribution or said empirical mass-to-charge distribution to generate constant lineshape widths.