The long-term objective of this project is to develop statistical methods and computer programs for effective analysis of microarray data. The development will build on our previous work on the model-based approach to such analyses but will address several issues in order to significantly improve the performance.
Our specific aims are as follows.
Aim 1. To develop methods for the full modeling of the responses of perfect match and mismatch oligonucleotide probes to changes in mRNA concentration, and to exploit such models to construct estimates of improved expression indexes. By doing so, we hope to extend the linearity of the estimates to the low signal range where cross-hybridization effects must be accounted for in the estimation process. We will also investigate the feasibility of oligonucleotide expression arrays based on perfect match-only design.
Aim 2. To develop array-to-array normalization methods applicable to situations when the corresponding samples are expected to exhibit very different expression patterns.
Aim 3. To develop statistical methods for the analysis of tag-probe arrays for the study of mixtures of yeast mutants.
Aim 4. To develop computer program modules based on the above methods, and to incorporate them into the software dChip in order to extend its functionality for model-based analysis of oligonucleotide array data. We will also work closely with experimental groups in the analysis of real data, in order to ensure that the results from our research will be relevant to the need of the user community. We will aim to improve dChip not only by the methods directly resulting from Aim 1, but will also selectively implement and incorporate other useful analysis methods developed by our group and our collaborators into the dChip framework.
Aim 5. To collaborate with the R-based bioinformatics consortium to facilitate the transfer of our methodology to that platform, and to extend the functionality of dChip through R function calls.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
2R01HG002341-04
Application #
6630719
Study Section
Special Emphasis Panel (ZRG1-SSS-Y (10))
Program Officer
Good, Peter J
Project Start
2000-09-15
Project End
2006-06-30
Budget Start
2003-07-03
Budget End
2004-06-30
Support Year
4
Fiscal Year
2003
Total Cost
$511,476
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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Beroukhim, Rameen; Lin, Ming; Park, Yuhyun et al. (2006) Inferring loss-of-heterozygosity from unpaired tumors using high-density oligonucleotide SNP arrays. PLoS Comput Biol 2:e41
Zhang, Xuegong; Lu, Xin; Shi, Qian et al. (2006) Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics 7:197
Hong, Pengyu; Wong, Wing H (2005) GeneNotes--a novel information management software for biologists. BMC Bioinformatics 6:20

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