COMPUTER SYSTEMS AND METHODS FOR HIERARCHICAL CLUSTER ANALYSIS OF LARGE SETS OF BIOLOGICAL DATA INCLUDING HIGHLY DENSE GENE ARRAY DATA

• US 20020052692A1
• Filed: 09/15/1999
• Published: 05/02/2002
• Est. Priority Date: 09/15/1999
• Status: Abandoned Application

• Current Assignee: Eoin D. Fahy
• Original Assignee: Eoin D. Fahy
• Inventors: FAHY, EOIN D.
• Application Number:
  US09/397,380
• Publication Number:
  US 20020052692A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/20 Polymerase chain reaction [PCR]; Primer or probe design; Probe optimisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/30 Unsupervised data analysis

Abstract

A system and corresponding method analyzes biological data for sets of test subjects such as gene arrays of group test subjects into clusters and order the clusters into a hierarchy based on similarities and differences of biological data corresponding to the test subjects. A combination of nonhierarchical clustering and hierarchical clustering methods is used to efficiently and effectively perform hierarchical clustering of such biological data as highly dense gene arrays containing many thousand test subjects such as genes. First the test subjects are nonhierarchically clustered according to similarities and differences of their biological data as determined by distance techniques. Representative values, such as mean values, of the biological data are determined for each nonhierarchical cluster of test subjects. These representative values are then used to hierarchically cluster the nonhierarchical clusters. Biological data for each test subject is displayed in a row of a table. The rows of the table are arranged by the nonhierarchical clustering and further by the hierarchical clustering. Each value of the biological data is color coded according to its value to display patterns in the hierarchically clustered biological data.

Claims

1. A system for analyzing information based on measurements of at least one measurement type, a measurement of each measurement type performed on each of a plurality of biological test subjects, the system comprising:

an input component configured to receive a data file of a test matrix containing sets of measurement values, each set of measurement values containing a measurement of each measurement type for one of the plurality of biological test subjects;

a pre-conditioning component configured to assign each of the sets of measurement values to one of a plurality of nonhierarchical clusters, at least one of the nonhierarchical clusters having more than one set of measurement values assigned;

a reduction component configured to generate a data file of a reduced test matrix from the data file of the test matrix, the reduced test matrix containing one set of representative values associated with each nonhierarchical cluster, each set of representative values based on the sets of measurement values assigned to the nonhierarchical cluster associated with the each set of representative values; and

a hierarchical clustering component configured to order the sets of representative values into hierarchical clusters.

2. The system of claim 1 wherein the input component receives a gene expression data file.

3. The system of claim 1 wherein the input component receives a protein expression data file.

4. The system of claim 1 wherein the input component receives two or more data files selected from the list consisting of a gene expression data file, a protein expression data file and a patient profile data file.

5. The system of claim 1 wherein the pre-conditioning component includes k-means clustering for assigning each of the sets of measurement values to one of a plurality of nonhierarchical clusters.

6. The system of claim 1 wherein the test matrix has over 10,000 rows of data.

7. The system of claim 1 wherein the data file is in ASCII file format.

8. The system of claim 1 wherein the pre-conditioning component uses Euclidean distance determinations in assigning each of the sets of measurement values to one of the plurality of nonhierarchical clusters.

9. The system of claim 1 wherein the hierarchical clustering component uses HClust of an R statistical package to order the sets of representative values into hierarchical clusters.

10. The system of claim 1 wherein an average agglomeration method is used in conjunction with the hierarchical clustering component.

11. The system of claim 1 wherein the hierarchical clustering component orders the sets of measurement values according to the ordering of the sets of representative values into hierarchical clusters.

12. The system of claim 1, further comprising a display component, the display component configured to display a portion of the sets of representative values in various colors selected according to each value of a set of representative values.

13. The system of claim 1 wherein each set of the representative values are the mean values of the sets of measurement values assigned to the nonhierarchical cluster associated with the each set of representative values.

14. The system of claim 1 wherein the plurality of nonhierarchical clusters is defined before the pre-conditioning component assigns each of the sets of measurement values to one of the plurality of nonhierarchical values.

16. The analysis system of claim 15 wherein the biological subjects are gene portions and the biological data is related to gene activity.

17. The analysis system of claim 15 wherein the biological data is associated with both control and study groups.

18. The analysis system of claim 15, further comprising a display configured to display each of the representative values in a particular color according to their value.

20. The data conditioning system of claim 19 wherein the nonhierarchical clustering system uses unsupervised clustering and the hierarchical clustering system uses an agglomeration method.

21. The data conditioning system of claim 19 wherein the input component receives biological data based upon gene arrays.

22. The data conditioning system of claim 19 wherein the conversion component generates each set of representative data based on a mean value of the received nonhierarchically ordered biological data of the respective nonhierarchical cluster of biological samples.

23. The data conditioning system of claim 19 wherein the conversion component uses Perl script language.

25. The medium of claim 24 wherein the computer-readable medium is a CD-ROM or hard drive.

26. The medium of claim 24 wherein receiving uses a computer network.

27. The medium of claim 24, further comprising viewing in color the hierarchically clustered biological data according their values.

28. The medium of claim 24 wherein receiving a data file of biological data uses gene arrays as a data source.

29. The medium of claim 24 wherein the biological samples include study and control groups.

30. The medium of claim 24 wherein the ordering involves Perl script language for aspects including data formatting.

31. The medium of claim 24 wherein the assigning biological sample data includes biological sample data from study and control groups.

32. The medium of claim 24 wherein the assigning to nonhierarchical clusters uses distances determinations based upon at least one of the following methods:

Euclidean, squared Euclidean, Cosine, Pearson correlation, Chebychev, Block, and Minkowski.

33. The medium of claim 24 wherein the assigning to nonhierarchical clusters involves at least one of the following cluster formation methods:

sequential threshold, parallel threshold, or optimization.

34. The medium of claim 24 wherein the assigning to nonhierarchical clusters involves a K-means method or an Isodata method.

35. The medium of claim 24 wherein the ordering the nonhierarchical clusters according to a hierarchical clustering uses distance determination of the representative values, the distance determination being at least one of the following methods:

Euclidean, Squared Euclidean, City-block, Manhattan distance, Chebychev distance, Power distance, or Percent disagreement.

36. The medium of claim 24 wherein the biological samples include study and control groups.

38. The system of claim 37 wherein the software program instructs the computer system to display nonhierarchically clustered values of same nonhierarchical clustering in adjacent rows of the table and to display one row of a first nonhierarchical clustering adjacent to a row of a second nonhierarchical clustering, the first and second nonhierarchical clusters being in the same hierarchical cluster.

39. The system of claim 37 wherein a portion of the software program is a database or spreadsheet program wherein the table is part of a database or spreadsheet respectively.

40. The system of claim 37 wherein a portion of the software program is a web browser.

15. An analysis system for biological data, the system comprising:

a receiver configured to receive the biological data on biological subjects, the biological subjects assigned to nonhierarchical clusters; and

a clustering component configured to hierarchically cluster the nonhierarchical clusters according to values representative of the nonhierarchical clusters.

19. A data conditioning system comprising:

an input component configured to receive biological data on biological samples, the biological data nonhierarchically ordered according to nonhierarchical clusters of the biological samples, the nonhierarchical clusters generated by the nonhierarchical clustering system; and

a conversion component configured to generate sets of representative data for input into the hierarchical clustering system, one of the sets of representative data being generated for each nonhierarchical cluster of biological samples, each set of representative data based on the received nonhierarchically ordered biological data of the respective nonhierarchical cluster of biological samples.

24. A computer-readable medium for storing computer-readable instructions, the instructions written to program a computer to perform a method, the method comprising:

receiving a data file of biological data for biological samples;

assigning biological sample data to nonhierarchical clusters;

generating representative values for each nonhierarchical cluster; and

ordering the nonhierarchical clusters of biological data according to a hierarchical clustering based on the representative values.

37. A system for displaying hierarchically clustered biological data comprising:

a color monitor;

a computer system coupled to the color monitor; and

a software program configured to instruct the computer system to display on the color monitor values representative of nonhierarchical clusters of biological data in a table having hierarchical cluster order, portions of the table colored according to the representative values.

41. A data structure stored on a computer-readable medium, the data structure having a plurality of records containing information generated from biological samples, each of the records comprising:

a section containing the information generated from the biological samples;

a section containing a number or label indicating a nonhierarchical assignment; and

a section containing a number or label indicating a hierarchical assignment.

42. The data structure of claim 41 wherein the section containing the information generated from the biological samples includes information from a gene array.

43. A method for generating information based on biological samples, the method comprising:

receiving a data file of a test matrix containing sets of measurement values, each set of measurement values containing a measurement of each measurement type for one of the plurality of biological test subjects;

assigning each of the sets of measurement values to one of a plurality of nonhierarchical clusters, at least one of the nonhierarchical clusters having more than one set of measurement values assigned; and

generating a data file of a reduced test matrix from the data file of the test matrix, the reduced test matrix containing one set of representative values associated with each nonhierarchical cluster, each set of representative values based on the sets of measurement values assigned to the nonhierarchical cluster associated with the each set of representative values; and

ordering the sets of representative values into hierarchical clusters.

44. The method of claim 43 wherein receiving a data file of a test matrix is based on receiving a gene array.

45. The method of claim 43 wherein assigning each of the sets of measurement values to one of a plurality of nonhierarchical clusters is based on k-means clustering.

46. The method of claim 43 wherein receiving a data file of a test matrix is based on receiving an ASCII formatted file.

47. The method of claim 43 wherein assigning each of the sets of measurement values to one of a plurality of nonhierarchical clusters uses Euclidean distance determinations.

48. The method of claim 43 wherein ordering the sets of representative values into hierarchical clusters uses HClust of R statistical package.

49. The method of claim 43 wherein ordering the sets of representative values into the hierarchical clusters further includes ordering the sets of measurement values according to the ordering of the sets of representative values.

50. The method of claim 43, further comprising displaying a portion of the sets of representative values in various colors selected according to each value of a set of representative values.

51. The method of claim 43 wherein generating a data file of a reduced test matrix is based on each set of the representative values being mean values of the sets of measurement values assigned to the nonhierarchical cluster associated with the each set of representative values.

52. A method of displaying biological data comprising:

receiving biological data records or representative records, each biological data record associated with a biological sample, each representative record representing at least one biological data record, at least one of the representative records representing a nonhierarchical cluster of biological data records;

assigning the biological data records or representative records to a table having fields for values of the representative records respectively, each field containing one value;

ordering each placed biological data record in the table according to the nonhierarchically ordered cluster of its associated biological sample;

arranging each placed representative record or each ordered placed biological data record in the table according to a hierarchically ordered clustering based on the placed representative record or the representative record associated with the ordered placed biological data record; and

displaying portions of the table containing values of the arranged ordered placed biological data records or arranged placed representative records, the displaying of portions of the table according to predetermined key with respect to each displayed value.

53. The method of claim 52 wherein placing biological data records or representative records uses gene array data.

54. The method of claim 52 wherein placing biological data records or representative records is done into a table of a spreadsheet or a database program.

55. The method of claim 52 wherein regarding arranging according to a hierarchically ordered clustering, the placed representative record and the representative record associated with the ordered placed biological data record are based on mean values of the biological data records as associated with the biological samples assigned to the particular nonhierarchical ordered cluster associated with the representative record.