Computational methods and systems for multidimensional analysis

• US 7,529,629 B2
• Filed: 04/28/2004
• Issued: 05/05/2009
• Est. Priority Date: 04/28/2003
• Status: Expired due to Fees

• Current Assignee: Cerno Bioscience LLC
• Original Assignee: Cerno Bioscience LLC
• Inventors: Wang, Yongdong
• Primary Examiner(s): Le; John H
• Application Number:
  US10/554,863
• Publication Number:
  US 20060288339A1
• Time in Patent Office: 1,833 Days
• Field of Search: 702/22, 702/23, 702/30, 702/27, 702/85, 702/89, 702/109, 702/180, 435/416, 435/7.1, 436/58, 436/89, 436/161, 250/228, 250/287, 250/306, 324/304, 324/309
• US Class Current: 702/22
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16C 20/20 Identification of molecular entities, parts thereof or of chemical compositions

Abstract

A method for analyzing data obtained from at least one sample in a separation system (10, 50, 60) that has a capability for separating components of a sample containing more than one component as a function of at least two different variables including obtaining data representative of the at least one sample from the system, the data being expressed as a function of the two variables; forming a data stack (70, 74, 78, 82, 84) having successive levels, each level containing successive data representative of the at least one sample; forming a data array (R) representative of a compilation of all of the data in the data stack; and separating the data array into a series of matrixes. A chemical analysis system that operates in accordance with the method, and a medium having computer readable program code for causing the system to perform the method.

Claims

1. A method for analyzing data obtained from at least one sample in a measurement system that has a capability for measuring components of a sample containing more than one component as a function of at least two different variables, said method comprising:

obtaining data representative of at least one sample from said system, said data being expressed as a function of said at least two variables;

forming a data stack having successive levels, each level containing one of said data samples;

forming a data array representative of a compilation of all of the data in said data stack; and

separating said data array into a series of matrixes in one overall decomposition process, said matrixes including;

a concentration matrix representative of concentration of each component in said levels of data;

a first profile of the components as a function of said first variable; and

a second profile of the components as a function of said second variable; and

at least one standardization matrix serving to calibrate one of said concentration or profile matrixes.

2. The method of claim 1, wherein said first profile and said second profile are representative of profiles of substantially pure components.

3. The method of claim 1, further comprising performing qualitative analysis using at least one of said first profile and said second profile.

4. The method of claim 1, further comprising standardizing data representative of said at least one sample by performing a data matrix multiplication of such data into the product of a first standardization matrix, the data itself, and a second standardization matrix, to form a standardized data matrix.

5. The method of claim 4, wherein terms in said first standardization matrix and said second standardization matrix have values that cause said data to be represented at positions with respect to said two variables, which are different in said standardized data matrix from those in said data array.

6. The method of claim 5, wherein said first standardization matrix shifts said data with respect to said first variable, and said second standardization matrix shifts said data with respect to said second variable.

7. The method of claim 5, wherein terms in said first standardization matrix and said second standardization matrix have values that serve to standardize distribution shapes of the data with respect to said first and second variable, respectively.

8. The method of claim 4, wherein terms in said first standardization matrix and said second standardization matrix are determined by:

applying a sample having known components to said apparatus; and

selecting terms for said first standardization matrix and said second standardization matrix which cause data produced by said known components to be positioned properly with respect to said first variable and said second variable.

9. The method of claim 8, wherein said terms are determined by selecting terms which produce a smallest error in position of said data with respect to said first variable and said second variable in said standardized data matrix.

10. The method of claim 9, wherein the terms of said first standardization matrix and said second standardization matrix are computed for each sample.

11. The method of claim 10, wherein terms of said first standardization matrix and said second standardization matrix are computed so as to produce a smallest error over all samples.

12. The method of claim 8, wherein values of terms in said first standardization matrix and said second standardization matrix are determined by solving said data array R:

13. The method of claim 4, wherein at least one of the first and second standardization matrices can be simplified to be either a diagonal matrix or an identity matrix.

14. The method of claim 4, wherein the terms in said first standardization matrix and said second standardization matrix are based on parameterized known functional dependence of said terms on said variables.

15. The method of claim 1, wherein said apparatus is a two-dimensional electrophoresis separation system.

16. The method of claim 15, wherein said first variable is isoelectric point and said second variable is molecular weight.

17. The method of claim 1, wherein said variables are a result of any combination, in no particular sequence, and including self-combination, of chromatographic separation, capillary electrophoresis separation, gel-based separation, affinity separation and antibody separation.

18. The method of claim 1, wherein one of the two variables is mass associated with the mass axis of a mass spectrometer.

19. The method of claim 18, wherein said apparatus further comprises a chromatography system for providing said samples to said mass spectrometer, retention time being another of the two variables.

20. The method of claim 18, wherein said apparatus further comprises an electrophoresis separation system for providing said samples to said mass spectrometer, migration characteristics of said sample being another of the two variables.

21. The method of claim 18, wherein said data is continuum mass spectral data.

22. The method of claim 18, wherein said data is used without centroiding.

23. The method of claim 18, further comprising correcting said data for time skew.

24. The method of claim 18, further comprising performing a calibration of said data with respect to mass and mass spectral peak shapes.

25. The method of claim 18, wherein the other one of said first variable and said second variable is that of a region on a protein chip having a plurality of protein affinity regions.

26. The method of claim 1, further comprising:

obtaining data for said data array by using a single channel analyzer and by analyzing the samples successively.

27. The method of claim 26, wherein said single channel detector is based on one of light absorption, light emission, light reflection, light transmission, light scattering, refractive index, electrochemistry, conductivity, radioactivity, or any combination thereof.

28. The method of claim 27, wherein the components in said sample are bound to at least one of fluorescence tags, isotope tags, stains, affinity tags, or antibody tags.

29. A computer readable medium comprising computer readable code thereon for causing a computer to analyze data by performing the method of one of claims 1-28.

30. A chemical analysis system configured to perform the method of one of claims 1-28.

31. The method of claim 1 wherein said at least one standardization matrix includes a first standardization matrix for shifting said data with respect to a first variable, and a second standardization matrix for shifting said data with respect to a second variable.

32. A method for analyzing data obtained from multiple samples in a separation system that has a capability for separating components of a sample containing more than one component as a function of two different variables, said method comprising:

obtaining data representative of multiple samples from said system, said data being expressed as a function of said two variables;

forming a data stack having successive levels, each level containing one of said data samples;

forming a data array representative of a compilation of all of the data in said data stack; and

separating said data array into a series of matrixes, said matrixes being;

a concentration matrix representative of concentration of each component in said sample;

a first profile of the components as a function of said first variable;

a second profile of the components as a function of said second variable; and

a first standardization matrix and a second standardization matrix;

wherein said first standardization matrix shifts said data with respect to said first variable, and said second standardization matrix shifts said data with respect to said second variable.

33. The method of claim 32, wherein said at least one sample comprises a single sample, and said successive data is representative of said sample as a function of time.

34. The method of claim 32, wherein said at least one sample comprises a single sample, and said successive data is representative of said sample as a function of mass of its components.

35. The method of claim 34, wherein said at least one sample comprises a plurality of samples, and said successive data is representative of successive samples.

36. A method for analyzing data obtained from multiple samples in a separation system that has a capability for separating components of a sample containing more than one component as a function of two different variables, said method comprising:

obtaining data representative of multiple samples from said system, said data being expressed as a function of said two variables;

forming a data stack having successive levels, each level containing one of said data samples;

forming a data array representative of a compilation of all of the data in said data stack;

separating said data array into a series of matrixes, said matrixes being;

a concentration matrix representative of concentration of each component in said sample;

a first profile of the components as a function of said first variable; and

a second profile of the components as a function of said second variable; and

standardizing data representative of a sample by performing a data matrix multiplication of such data into the product of a first standardization matrix, the data itself, and a second standardization matrix, to form a standardized data matrix;

wherein terms in said first standardization matrix and said second standardization matrix have values that cause said data to be represented at positions with respect to said two variables, which are different in said standardized data matrix from those in said data array; and

said first standardization matrix shifts said data with respect to said first variable, and said second standardization matrix shifts said data with respect to said second variable.

37. A method for analyzing data obtained from multiple samples in a separation system that has a capability for separating components of a sample containing more than one component as a function of two different variables, said method comprising:

obtaining data representative of multiple samples from said system, said data being expressed as a function of said two variables;

forming a data stack having successive levels, each level containing one of said data samples;

forming a data array representative of a compilation of all of the data in said data stack;

separating said data array into a series of matrixes, said matrixes being;

a concentration matrix representative of concentration of each component in said sample;

a first profile of the components as a function of said first variable; and

a second profile of the components as a function of said second variable; and

standardizing data representative of a sample by performing a data matrix multiplication of such data into the product of a first standardization matrix, the data itself, and a second standardization matrix, to form a standardized data matrix;

wherein terms in said first standardization matrix and said second standardization matrix have values that cause said data to be represented at positions with respect to said two variables, which are different in said standardized data matrix from those in said data array; and

terms in said first standardization matrix and said second standardization matrix have values that serve to standardize distribution shapes of the data with respect to said first and second variable, respectively.

38. A method for analyzing data obtained from multiple samples in a separation system that has a capability for separating components of a sample containing more than one component as a function of two different variables, said method comprising:

obtaining data representative of multiple samples from said system, said data being expressed as a function of said two variables;

forming a data stack having successive levels, each level containing one of said data samples;

forming a data array representative of a compilation of all of the data in said data stack;

separating said data array into a series of matrixes, said matrixes being;

a concentration matrix representative of concentration of each component in said sample;

a first profile of the components as a function of said first variable; and

a second profile of the components as a function of said second variable; and

standardizing data representative of a sample by performing a data matrix multiplication of such data into the product of a first standardization matrix, the data itself, and a second standardization matrix, to form a standardized data matrix;

wherein terms in said first standardization matrix and said second standardization matrix are determined by;

applying a sample having known components to said apparatus; and

selecting terms for said first standardization matrix and said second standardization matrix which cause data produced by said known components to be positioned properly with respect to said first variable and said second variable; and

which produce a smallest error in position of said data with respect to said first variable and said second variable in said standardized data matrix.

39. A method for analyzing data obtained from multiple samples in a separation system that has a capability for separating components of a sample containing more than one component as a function of two different variables, said method comprising:

obtaining data representative of multiple samples from said system, said data being expressed as a function of said two variables;

forming a data stack having successive levels, each level containing one of said data samples;

forming a data array representative of a compilation of all of the data in said data stack; and

separating said data array into a series of matrixes, said matrixes being;

a concentration matrix representative of concentration of each component in said sample;

a first profile of the components as a function of said first variable; and

a second profile of the components as a function of said second variable;

wherein said system is a two-dimensional electrophoresis separation system;

said first variable is isoelectric point and said second variable is molecular weight.

40. A method for analyzing data obtained from multiple samples in a separation system that has a capability for separating components of a sample containing more than one component as a function of two different variables, said method comprising:

obtaining data representative of multiple samples from said system, said data being expressed as a function of said two variables;

forming a data stack having successive levels, each level containing one of said data samples;

forming a data array representative of a compilation of all of the data in said data stack; and

separating said data array into a series of matrixes, said matrixes being;

a concentration matrix representative of concentration of each component in said sample;

a first profile of the components as a function of said first variable; and

a second profile of the components as a function of said second variable;

wherein one of said first variables and said second variable is mass associated with the mass axis of a mass spectrometer; and

another one of said first variable and said second variable is that of a region on a protein chip having a plurality of protein affinity regions.