Tiling array data analysis: a multiscale approach using wavelets

Background: Tiling array data is hard to interpret due to noise. The wavelet transformation is a widely used technique in signal processing for elucidating the true signal from noisy data. Consequently, we attempted to denoise representative tiling array datasets for ChIP-chip experiments using wavelets. In doing this, we used specific wavelet basis functions, Coiflets, since their triangular shape closely resembles the expected profiles of true ChIP-chip peaks. Results: In our wavelet-transformed data, we observed that noise tends to be confined to small scales while the useful signal-of-interest spans multiple large scales. We were also able to show that wavelet coefficients due to non-specific cross-hybridization follow a log-normal distribution, and we used this fact in developing a thresholding procedure. In particular, wavelets allow one to set an unambiguous, absolute threshold, which has been hard to define in ChIP-chip experiments. One can set this threshold by requiring a si...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Alexander Karpikov, Joel S. Rozowsky, Mark Gerstein
• Keywords: Artificial Intelligence, BMCBI 2011, Tiling Array Datasets, Tiling Arrays, Wavelets