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Genome Scale Measurements Mathematics (and common sense) matters ... |
'Over 20,000 ways to be mistaken' |
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Zoltan Szallasi Aaron Eklund Scott Carter Peter Park |
Genome-scale measurements have served as a disruptive technology for biomedical research in a the analytic approach that these new measurement modalities require and the risks inherent in misunderstanding their nature. Early on, we demonstrated an impressively poor correlation of expression of microarrays from different manufacturers, and even across the generations of a platform for a single manufacturer. Substantial difficulties in the annotations of common oligonucleotide microarrays were noted and methods to overcome these were provided. When combining these measurements to create prognostic algorithms (as the Food and Drug Administration has already done for breast cancer recurrence), we have identified challenges in using data from real-world trials (e.g. from censored data) and also subtleties in how to weight the contributions of pathways to particular pathologies. |
Publications Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 2002, 18(3):405-412. Nimgaonkar A, Sanoudou D, Butte AJ, Haslett JN, Kunkel LM, Beggs AH, Kohane IS: Reproducibility of gene expression across generations of Affymetrix microarrays. BMC Bioinformatics 2003, 4(1):27. Ramoni MF, Sebastiani P, Kohane IS: Cluster analysis of gene expression dynamics. Proc Natl Acad Sci U S A 2002, 99(14):9121-9126. Carter SL, Eklund AC, Mecham BH, Kohane IS, Szallasi Z: Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC Bioinformatics 2005, 6(1):107.
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