Gene expression microarray technology has been responsible for much of the functional genomics data generated in recent years. These data promise to help researchers investigate the regulation and transcription of genes, which is vital for understanding the ultimate roles that proteins play within cells as well as developing diagnostic tests and finding new drug targets. Microarray data can be particularly difficult to analyze and comprehend due to unusual noise characteristics and variation between protocols and technologies. We are developing methods to harness large collections of microarray data that make it more accessible to researchers. Also, our approaches are readily adaptable to new technologies that can measure transcription, such as deep sequencing approaches.
- Huttenhower C, Hibbs MA, Myers CL, Troyanskaya OG. A scalable method for integration and functional analysis of multiple microarray datasets. Bioinformatics, 2006
- Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics, 2007
- Huttenhower C, Flamholz AI, Landis JN, Sahi S, Myers CL, Olszewski KL, Hibbs MA, Siemers NO, Troyanskaya OG, Coller HA. Nearest Neighbor Networks: clustering expression data based on gene neighborhoods. BMC Bioinformatics, 2007
- Hibbs MA, Wallace G, Dunham M, Li K, Troyanskaya OG. Viewing the Larger Context of Genomic Data through Horizontal Integration. 11th International Conference Information Visualization (IV07), 2007
- Hibbs MA, Dirksen NC, Li K, Troyanskaya OG. Visualization methods for statistical analysis of microarray clusters. BMC Bioinformatics, 2005