Detection and Compensation of Cross Hybridization in DNA

Detection and Compensation of Cross. Hybridization in DNA Microarray Data Jim Huang (1), Joint work with Quaid Morris(1), Tim Hughes(2) and Brendan Frey(1) Probabilistic and Statistical Inference Group, University of Toronto (2) Banting & Best Department of Medical Research, University of Toronto

Description and Applications of DNA Microarrays l l l Microarrays consist of a 2 -D array of probes, each with a short DNA sequence attached. These sequences are called oligonucleotide sequences. The output of each probe is approximately proportional to the amount of DNA that binds to the probe from a given tissue; the data for each probe is an N-dimensional expression profile vector, where N is the number of tissues used on the array. DNA microarrays can be used to measure the level of gene expression across these N tissues.

Hybridization and crosshybridization DNA from tissue sample l l The process of 2 complementary DNA strands binding is called hybridization; Ideally, an oligonucleotide probe will only bind to the DNA sequence for which it was designed and to which it is complementary; However, many DNA sequences are similar to one another and can bind to other probes on the array; This phenomenon is called cross-hybridization; AT GC CG T A AT GC GC AT T A Hybridization AT TC CG TA AT GC AC AT TA Cross-hybridization Oligonucleotide Probe

The trouble with crosshybridization l l With cross-hybridization, each probe will signal the presence of multiple sequences other than that it was designed for; This skews the observed data from the expected data. = + Expected expression profile vector Observed expression profile vector (no hybridization) (cross-hybridized)

Detecting cross-hybridization (1) l l To test for whether cross-hybridization is impacting the gene expression data, we perform a BLAST sequence match on all oligonucleotide probe sequences used on the microarray; Many probes will be matched with sequences for which it wasn’t specifically designed.

Detecting cross-hybridization (2) l l l We compute the Pearson correlation coefficient ρ between matched probe sequence expression profiles and between the profiles of randomly-paired probes; Approximately 33% of the BLAST-matched probes have ρ > 0. 95, whereas only 2% of randomly-matched probes have ρ >0. 95; This difference in the 2 distributions indicates that cross-hybridization indeed has a significant impact on the observed gene expression data.

Compensating for crosshybridization l l l We model the observed, cross-hybridized expression profile vector x as a matrix product of a hybridization matrix Λ and an unobserved expression profile vector z in which there is no cross -hybridization. The elements λij of the Λ matrix are set as parameterized functions of the Gibbs free energy ΔGij between probes i and j. To compensate for cross-hybridization, we use a generalized Expectation-Maximization algorithm in which we solve for z and Λ iteratively.