Systems and methods for extending the dynamic range of mass spectrometry
Systems and methods are used to predict intensities for points not measured or not measured with a high degree of confidence of a peak using a peak predictor. A set of data is selected from the plurality of intensity measurements that includes a peak. Confidence values are assigned to each data point in the set of data producing a plurality of confidence value weighted data points. A peak predictor is selected. The peak predictor is applied to the plurality of confidence value weighted data points of the peak that have confidence values greater than a first threshold level using the prediction module, producing predicted intensities for data points of the peak not measured and/or measured data points of the peak that have confidence values less than or equal to a second threshold level. The confidence values can include system confidence values, predictor confidence values, or any combination of the two.
1. A system for predicting intensities for data points not measured or not measured with a high degree of confidence of a peak using a peak predictor, comprising:
a mass spectrometer that produces a plurality of intensity measurements; and
a processor in communication with the mass spectrometer, wherein the processor obtains the plurality of intensity measurements from the mass spectrometer,
the processor selects a set of data from the plurality of intensity measurements that comprises a peak,
the processor assigns confidence values to each data point in the set of data producing a plurality of confidence value weighted data points,
the processor selects a peak predictor, and
the processor applies the peak predictor to the plurality of confidence value weighted data points of the peak that have confidence values greater than a first threshold level and the peak predictor produces predicted intensities for data points of the peak not measured and/or measured data points of the peak that have confidence values less than or equal to a second threshold level.
2. The system of claim 1 , wherein the confidence values comprise system confidence values based on a system characteristic.
3. The system of claim 2 , wherein the system characteristic comprises a detector dynamic range.
4. The system of claim 1 , wherein the confidence values comprise predictor confidence values that are found by comparing predictor intensities to measured data points of another peak that have confidence values greater than the threshold level.
5. The system of claim 1 , wherein the confidence values comprise combined system confidence values based on a system characteristic and predictor confidence values that are found by comparing predictor intensities to measured data points of another peak that have confidence values greater than the threshold level.
6. The system of claim 1 , wherein the peak predictor comprises a theoretical model.
7. The system of claim 1 , wherein the peak predictor comprises an analytical function representing a best fit of a plurality of probability density functions to a first set of measured data points of another peak that have confidence values greater than the threshold level.
8. The system of claim 7 , wherein the plurality of probability density functions comprises three Gaussian functions.
9. The system of claim 1 , wherein the peak is a chromatographic peak.
10. The system of claim 1 , wherein the peak is a mass spectral peak.
11. A method for predicting intensities for data points not measured or not measured with a high degree of confidence of a peak using a peak predictor, comprising:
producing a plurality of intensity measurements using a mass spectrometer;
obtaining the plurality of intensity measurements from the mass spectrometer using a processor in communication with the mass spectrometer;
selecting a set of data from the plurality of intensity measurements that comprises a peak using the processor;
assigning confidence values to each data point in the set of data producing a plurality of confidence value weighted data points using the processor;
selecting a peak predictor using the processor; and
applying the peak predictor to the plurality of confidence value weighted data points of the peak that have confidence values greater than a first threshold level using the processor, producing predicted intensities for data points of the peak not measured and/or measured data points of the peak that have confidence values less than or equal to a second threshold level.
12. The method of claim 11 , wherein the confidence values comprise system confidence values based on a system characteristic.
13. The method of claim 12 , wherein the system characteristic comprises a detector dynamic range.
14. The method of claim 11 , wherein the confidence values comprise predictor confidence values that are found by comparing predictor intensities to measured data points of another peak that have confidence values greater than the threshold level.
15. The method of claim 11 , wherein the confidence values comprise combined system confidence values based on a system characteristic and predictor confidence values that are found by comparing predictor intensities to measured data points of another peak that have confidence values greater than the threshold level.
16. The method of claim 11 , wherein the peak predictor comprises a theoretical model.
17. The method of claim 11 , wherein the peak predictor comprises an analytical function representing a best fit of a plurality of probability density functions to a first set of measured data that includes data points of another peak that have confidence values greater than the threshold level.
18. The method of claim 17 , wherein the plurality of probability density functions comprises three Gaussian functions.
19. The method of claim 11 , wherein the peak is a chromatographic peak.
20. The method of claim 11 , wherein the peak is a mass spectral peak.
21. A computer program product, comprising a non-transient, tangible computer-readable storage medium whose contents include a program with instructions being executed on a processor so as to perform a method for predicting intensities for data points not measured or not measured with a high degree of confidence of a peak using a peak predictor, the method comprising:
providing a system, wherein the system comprises distinct software modules, and wherein the distinct software modules comprise a measurement module, an analysis module, and a prediction module;
obtaining a plurality of intensity measurements from a mass spectrometer using the measurement module;
selecting a set of data from the plurality of intensity measurements that comprises a peak using the analysis module;
assigning confidence values to each data point in the set of data producing a plurality of confidence value weighted data points using the analysis module;
selecting a peak predictor using the prediction module; and
applying the peak predictor to the plurality of confidence value weighted data points of the peak that have confidence values greater than a first threshold level using the prediction module, producing predicted intensities for data points of the peak not measured and/or measured data points of the peak that have confidence values less than or equal to a second threshold level.