Noise reduction from genotyping microarrays using probe level information

Genomic copy number change is one of the important phenomenon observed in cancer and other genetic disorders. Recently oligonucleotide microarrays have been used to analyze changes in the copy number. Although high density microarrays provide genome wide useful data on copy number, they are often associated with substantial amount of experimental noise that could affect the performance of the analyses. We used the high density oligonucleotide genotyping microarrays in our experiments that uses redundant probe tiling approach for individual SNPs. We found that the noise in the genotyping microarray data is associated with several experimental steps during target preparation and devised an algorithm that takes into account those experimental parameters. Additionally, defective probes that do not hybridize well to the target and therefore could not be modified inherently were detected and omitted automatically by using the algorithm. When we applied the algorithm to actual datasets, we could reduce the noise substantially without compressing the dynamic range. Additionally, combinatorial use of our noise reduction algorithm and conventional breakpoint detection algorithm successfully detected a microamplification of c-myc which was overlooked in the raw data. The algorithm described here is freely available with the software upon request to all non-profit researchers.