Methods and systems for differential clustering

• US 20050240357A1
• Filed: 04/26/2004
• Published: 10/27/2005
• Est. Priority Date: 04/26/2004
• Status: Abandoned Application

• Current Assignee: Agilent Technologies, Inc.
• Original Assignee: Agilent Technologies, Inc.
• Inventors: Minor, James M.
• Application Number:
  US10/831,866
• Publication Number:
  US 20050240357A1
• Time in Patent Office: Days
• US Class Current: 702/20
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation

Abstract

Methods, systems and computer readable media for differential clustering of gene expression response profiles for identification of potential functionally variant genes in a high throughput manner. Gene expression data is provided for a number of samples. Gene expression response profiles are generated for various sets of the samples and then differentially clustered across such sets to observe genes whose expression response profiles change cluster membership going from one set to another. Statistical analysis is performed with regard to the change from one cluster membership to another to determine whether the change from one cluster membership to another is statistically significant. If the change is determined to be statistically significant, the gene represented by the gene expression response profiles having been analyzed is identified as being a potential functionally variant gene. The nature of the function change may also be identified by the present systems, methods and computer readable media.

Claims

1. A high throughput method of identifying genes that are potentially functionally variant, said method comprising the steps of:

providing gene expression values for a plurality of genes for each of a number of tissues wherein the expression values are given for the same genes in each of the number of tissues;

dividing the number of tissues into at least first and second groups of tissues;

generating a gene expression response profile for each gene representative of all gene expression values for that gene across all tissue samples in the first group;

clustering the gene expression response profiles generated with respect to the first group;

generating a gene expression response profile for each gene representative of all gene expression values for that gene across all tissue samples in the second group;

clustering the gene expression response profiles generated with respect to the second group;

comparing gene expression response profile members in clusters generated with respect to the first group with gene expression response profile members in clusters generated with respect to the second group and identifying those members that change cluster membership in the second group relative to the first group;

statistically calculating whether the move of a member from membership in a first cluster to membership in a second cluster is significant relative to the variance within the first and second clusters and the variance between the first and second clusters; and

if the move is calculated to be significant, identifying the gene represented by the member as a potential functionally variant gene.

2. The method of claim 1, further comprising identifying small clusters having less than a predefined number of gene expression response signature members resultant from said clustering steps and identifying any genes represented by members in the small clusters as potential functionally variant genes.

3. The method of claim 1, further comprising identifying single gene expression response profiles that do not cluster with clusters produced by said clustering steps and identifying each gene represented by each identified single gene expression response profile as a potential functionally variant gene.

4. The method of claim 1, wherein said dividing comprises dividing the number of tissues into more than two groups of tissues, and wherein said clustering and said generating a gene expression response profile steps are performed with respect to each group, the results of which are compared with each of said first and second groups and with each other to identify those members that change cluster membership when comparing any one group to another, and wherein said statistical calculation is performed with respect to those groups considered, when a member changes cluster membership, to determine if the move was statistically significant.

5. The method of claim 1 wherein each group includes at least five tissue samples.

6. The method of claim 1, further comprising the steps of:

generating a gene expression response profile for each gene representative of all gene expression values for that gene across all tissue samples in the number of tissues;

clustering the gene expression response profiles generated with respect to the totality of the number of tissues;

comparing gene expression response profile members in clusters generated with respect to the totality of the number of tissues with gene expression response profile members in clusters generated with respect to each of said at least first and second groups, respectively, and identifying those members that change cluster membership when comparing clusters generated with respect to the totality of the number of tissues with clusters generated from one of said groups;

statistically calculating whether the move of a member from membership in a first cluster to membership in a second cluster is significant relative to the variance within the first and second clusters and the variance between the first and second clusters; and

if the move is calculated to be significant, identifying the gene represented by the member as a potential functionally variant gene.

7. The method of claim 1, further comprising:

regrouping the number of tissues into different groups of tissues comprising at least different first and different second groups of tissues which are different from said at least first and second groups of tissues; and

carrying out said clustering, generating comparing, statistically calculating and identifying steps with regard to said at least different first and different second groups of tissues to identify potential functionally variant genes.

8. The method of claim 7, further comprising comparing identifications made in claim 1 with identifications made in claim 7.

9. The method of claim 8, further comprising intersecting identifications made in claim 1 with identifications made in claim 7 to form an optimized list of potential functionally variant genes.

10. A method comprising forwarding a result obtained from the method of claim 1 to a remote location.

11. A method comprising transmitting data representing a result obtained from the method of claim 1 to a remote location.

12. The method of claim 1, further comprising determining a specific function that is varying in the identified potentially functionally variant gene, based on at least one of:

characteristics of the first and second clusters in which the member representative of the potentially functionally variant gene had membership, and characteristics of the members in the first and second clusters in which the member representative of the potentially functionally variant gene had membership.

13. A method comprising receiving a result obtained from a method of claim 1 from a remote location.

14. A high throughput method of identifying genes that are potentially functionally variant, said method comprising the steps of:

providing gene expression values for a plurality of genes for each of a number of tissues wherein the expression values are given for the same genes in each of the number of tissues;

differentially clustering gene expression response profiles generated based upon at least a first set of tissues taken from the number of tissues and then from at least a second set of tissues taken from the number of tissues;

comparing gene expression response profile members in clusters generated with respect to one of said sets with gene expression response profile members in clusters generated with respect to another of said sets and identifying those members that change cluster membership in said another of said sets relative to said one of said sets;

statistically calculating whether the move of a member from membership in a first cluster to membership in a second cluster is significant relative to the variance within the first and second clusters and the variance between the first and second clusters; and

if the move is calculated to be significant, identifying the gene represented by the member as a potential functionally variant gene.

15. The method of claim 14, wherein said first set includes the total number of tissues and said second set is a subset of the total number of tissues.

16. The method of claim 14, wherein said first set comprises a subset of the total number of tissues and said second set comprises a different subset of the total number of tissues.

17. The method of claim 14, wherein one of sets includes the total number of tissues, and a plurality of sets further include different subsets of the total number of tissues.

18. The method of claim 14, further comprising determining a specific function that is varying in the identified potentially functionally variant gene, based on at least one of:

characteristics of the first and second clusters in which the member representative of the potentially functionally variant gene had membership, and characteristics of the members in the first and second clusters in which the member representative of the potentially functionally variant gene had membership.

19. A method comprising forwarding a result obtained from the method of claim 14 to a remote location.

20. A method comprising transmitting data representing a result obtained from the method of claim 14 to a remote location.

21. A method comprising receiving a result obtained from a method of claim 14 from a remote location.

22. A system for high throughput identification of genes that are potentially functionally variant, said system comprising:

means for differentially clustering gene expression response profiles generated from expression values taken from at least a first set of tissues taken from a dataset providing gene expression values for a number of tissues and then clustering gene expression response profiles generated from expression values taken from at least a second set of tissues taken from the number of tissues;

means for comparing gene expression response profile members in clusters generated with respect to one of said sets with gene expression response profile members in clusters generated with respect to another of said sets and identifying those members that change cluster membership in said another of said sets relative to said one of said sets;

means for statistically calculating whether the move of a member from membership in a first cluster to membership in a second cluster is significant relative to the variance within the first and second clusters and the variance between the first and second clusters; and

means for identifying a gene as a potential functionally variant gene if the move is calculated to be significant.

23. A computer readable medium carrying one or more sequences of instructions for high throughput identification of genes that are potentially functionally variant, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

dividing gene expression values, provided for a plurality of genes for each of a number of tissues wherein the expression values are given for the same genes in each of the number of tissues, into at least first and second groups of tissues;

generating a gene expression response profile for each gene representative of all gene expression values for that gene across all tissue samples in the first group;

clustering the gene expression response profiles generated with respect to the first group;

generating a gene expression response profile for each gene representative of all gene expression values for that gene across all tissue samples in the second group;

clustering the gene expression response profiles generated with respect to the second group;

comparing gene expression response profile members in clusters generated with respect to the first group with gene expression response profile members in clusters generated with respect to the second group and identifying those members that change cluster membership in the second group relative to the first group;

statistically calculating whether the move of a member from membership in a first cluster to membership in a second cluster is significant relative to the variance within the first and second clusters and the variance between the first and second clusters; and

if the move is calculated to be significant, identifying the gene represented by the member as a potential functionally variant gene.

24. A computer readable medium carrying one or more sequences of instructions for high throughput identification of genes that are potentially functionally variant, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

differentially clustering gene expression response profiles, provided for a plurality of genes for each of a number of tissues wherein the expression values are given for the same genes in each of the number of tissues, generated based upon at least a first set of tissues taken from the number of tissues and then from at least a second set of tissues taken from the number of tissues;

comparing gene expression response profile members in clusters generated with respect to one of said sets with gene expression response profile members in clusters generated with respect to another of said sets and identifying those members that change cluster membership in said another of said sets relative to said one of said sets;

statistically calculating whether the move of a member from membership in a first cluster to membership in a second cluster is significant relative to the variance within the first and second clusters and the variance between the first and second clusters; and

if the move is calculated to be significant, identifying the gene represented by the member as a potential functionally variant gene.