Mass spectrometric method for determining the presence or absence of a chemical element in an analyte
The present invention relates to a mass spectrometric method for determining (predicting) the presence or absence of a chemical element in an analyte which provides valuable information towards reduction of complexity for annotating a chemical formula to the analyte. The method is based on representing a measured isotopic pattern of an analyte as a feature vector and assigning the feature vector to the presence/absence class using a machine learning algorithm, like a support vector machine (SVM) or an artificial neural network (NN).
1. A mass spectrometric method for determining the presence or absence of a chemical element in an analyte, the method comprising:
(a) generating analyte ions from the analyte;
(b) measuring an isotopic pattern of the analyte ions by mass spectrometry, wherein the isotopic pattern comprises multiple isotopic peaks each characterized by a mass value and an intensity value;
(c) representing the isotopic pattern as a feature vector {right arrow over (v)}; and
(d) applying the feature vector {right arrow over (v)} to a supervised element classifier that assigns the feature vector {right arrow over (v)} to a first class indicative of the chemical element being present or to a second class indicative of the chemical element being absent, wherein the supervised element classifier is trained on a set of feature vectors {right arrow over (v)} t which represent isotopic patterns of compounds with known elemental composition and wherein the chemical element is present in a proper subset of the compounds.
2. The method according to claim 1 , wherein each of the feature vector {right arrow over (v)} and the feature vectors of the set {right arrow over (v)} t representing a corresponding isotopic pattern comprises mass values and normalized intensity values of the isotopic peaks of its respective isotopic pattern.
3. The method according to claim 1 , wherein each of the feature vector {right arrow over (v)} and the feature vectors of the set {right arrow over (v)} t representing a corresponding isotopic pattern comprises a mass value of a monoisotopic peak, mass differences between the monoisotopic peak and other isotopic peaks and normalized intensity values of the isotopic peaks of its respective isotopic pattern.
4. The method according to claim 3 , wherein each of the feature vector {right arrow over (v)} and the feature vectors of the set {right arrow over (v)} t further comprises a mass difference between the monoisotopic peak and a nominal mass.
5. The method according to claim 4 , wherein each of the feature vector {right arrow over (v)} and the feature vectors of the set {right arrow over (v)} t is arranged as follows: [m 0 , s 0 , d(m 0 , m i ), s i , d(m 0 , M 0 )] with i=1 . . . N, wherein m 0 is the mass value of the monoisotopic peak, s 0 is a normalized intensity value of the monoisotopic peak, d(m 0 , m i ) is a mass difference between the monoisotopic peak and the ith isotopic peak, s i is a normalized intensity value of an ith isotopic peak, and d(m 0 , M 0 ) is a difference between the mass value of the monoisotopic peak and nominal mass M 0 .
6. The method according to claim 2 , wherein normalized intensity values s i of a feature vector are calculated from intensity values s i of corresponding isotopic peaks by using a p-norm:
s i =s i /∥s ∥ with ∥ s ∥=(Σ| s i | p ) 1/p with 1≤ p.
7. The method according to claim 1 , wherein each of the feature vector {right arrow over (v)} and the feature vectors of the set {right arrow over (v)} t representing a corresponding isotopic pattern comprises mass values and transformed intensity values of the isotopic peaks of its respective isotopic pattern.
8. The method according to claim 7 , wherein the intensity values of the isotopic peaks of said corresponding isotopic pattern are transformed by a centered-log ratio (clr) transformation or by an isometric log-ratio (ilr) transformation.
9. The method according to claim 8 , wherein each of the feature vector {right arrow over (v)} and the feature vectors of the set {right arrow over (v)} t is arranged as follows: [m 0 , clr 0 , d(m 0 , m i ), clr i , d(m 0 , M 0 )] with i=1 . . . N,
wherein m 0 is the mass value of a monoisotopic peak, clr 0 is a clr-transformed intensity value of the monoisotopic peak, d(m 0 , m i ) is a mass difference between the monoisotopic peak and an ith isotopic peak, clr i is a clr-transformed intensity value of the ith isotopic peak, and d(m 0 , M 0 ) is a difference between the mass value of the monoisotopic peak and a nominal mass and
wherein the clr-transformation is defined by:
clr i =log( s i / N+1 √{square root over ( s 0 ·s 1 · . . . s N )}) with s i=0 . . . N being the intensity values of the isotopic peaks.
10. The method according to claim 8 , wherein each of the feature vector {right arrow over (v)} and the feature vectors of the set {right arrow over (v)} t is arranged as follows: [m 0 , ilr 0 , d(m 0 , m i ), ilr i , d(m 0 , m N ), d(m 0 , M 0 )] with i=1 . . . N−1,
wherein m 0 is the mass value of a monoisotopic peak, ilr i are the ilr-transformed intensity values of the isotopic peaks, d(m 0 , m i ) is a mass difference between the monoisotopic peak and a ith isotopic peak, and d(m 0 , M 0 ) is a difference between the mass value of the monoisotopic peak and a nominal mass and
wherein the ilr-transformation is defined by:
{right arrow over (ilr)}={right arrow over (clr)}· B with {right arrow over (ilr)} =( ilr i=0 . . . N−1 ), {right arrow over (clr)} =( clr i=0 . . . N ), and balance matrix B of reduced dimension dim ( B )=( N+ 1)× N and B · B T = I N .
11. The method according to claim 1 , wherein the supervised element classifier is one of a support vector machine (SVM), an artificial neural network (NN) and a random forest (RF, random decision forest) classifier.
12. The method according to claim 11 , wherein the inherent parameters of the supervised element classifier (hyperparameter) are optimized during the training of the supervised element classifier.
13. The method according to claim 1 , wherein the presentation of the isotopic pattern as a feature vector is optimized during the training of the supervised element classifier.
14. The method according to claim 13 , wherein a selection of features or estimation of feature importance is performed during the training of the supervised element classifier.
15. The method according to claim 1 , wherein the chemical element is one of Br, Cl, S, I, F, P, K, Na and Pt.
16. The method according to claim 15 , wherein, in step (d), the first class corresponds to the presence of two or more of the chemical elements and the second class corresponds to the absence of said two or more of the chemical elements and wherein the supervised element classifier is trained on a set of feature vectors {right arrow over (v)} t which represent isotopic patterns of compounds with known elemental composition and wherein said two or more of the chemical elements are present in a proper subset of the compounds.
17. The method according to claim 1 , wherein the isotopic patterns of compounds used for training the supervised element classifier are theoretically derived.
18. The method according to claim 1 , wherein the isotopic patterns of compounds used for training the supervised element classifier are experimentally measured.
19. The method according to claim 18 , wherein the isotopic patterns of compounds used for the supervised element classifier and the isotopic pattern of the analyte ions are measured on the same mass spectrometric system.
20. The method according to claim 1 , wherein determination of the presence or absence of the chemical element is used for reducing or enhancing the number of chemical elements during annotating a chemical formula to the analyte.