Name
Abstract | ||
|---|---|---|
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.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 similar confidence level at different length-scales of the transformed signal. We applied our algorithm to a number of representative ChIP-chip data sets, including those of Pol II and histone modifications, which have a diverse distribution of length-scales of biochemical activity, including some broad peaks.Finally, we benchmarked our method in comparison to other approaches for scoring ChIP-chip data using spike-ins on the ENCODE Nimblegen tiling array. This comparison demonstrated excellent performance, with wavelets getting the best overall score. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1186/1471-2105-12-57 | BMC Bioinformatics |
Keywords | Field | DocType |
histone modification,bioinformatics,confidence level,data analysis,wavelet transform,chip,chromatin immunoprecipitation,algorithms,wavelet analysis,length scale,log normal distribution,signal processing,microarrays | Signal processing,Wavelet decomposition,Noisy data,Coiflet,Tiling array,Computer science,Discrete wavelet transform,Bioinformatics,Wavelet basis functions,Wavelet | Journal |
Volume | Issue | ISSN |
12 | 1 | 1471-2105 |
Citations | PageRank | References |
11 | 0.41 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Karpikov | 1 | 17 | 1.73 |
| Joel Rozowsky | 2 | 76 | 6.43 |
| Mark Gerstein | 3 | 354 | 45.41 |