Quantized correlation coefficient for measuring reproducibility of ChIP-chip data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-12

AUTHORS

Shouyong Peng, Mitzi I Kuroda, Peter J Park

ABSTRACT

BACKGROUND: Chromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) is used to study protein-DNA interactions and histone modifications on a genome-scale. To ensure data quality, these experiments are usually performed in replicates, and a correlation coefficient between replicates is used often to assess reproducibility. However, the correlation coefficient can be misleading because it is affected not only by the reproducibility of the signal but also by the amount of binding signal present in the data. RESULTS: We develop the Quantized correlation coefficient (QCC) that is much less dependent on the amount of signal. This involves discretization of data into set of quantiles (quantization), a merging procedure to group the background probes, and recalculation of the Pearson correlation coefficient. This procedure reduces the influence of the background noise on the statistic, which then properly focuses more on the reproducibility of the signal. The performance of this procedure is tested in both simulated and real ChIP-chip data. For replicates with different levels of enrichment over background and coverage, we find that QCC reflects reproducibility more accurately and is more robust than the standard Pearson or Spearman correlation coefficients. The quantization and the merging procedure can also suggest a proper quantile threshold for separating signal from background for further analysis. CONCLUSIONS: To measure reproducibility of ChIP-chip data correctly, a correlation coefficient that is robust to the amount of signal present should be used. QCC is one such measure. The QCC statistic can also be applied in a variety of other contexts for measuring reproducibility, including analysis of array CGH data for DNA copy number and gene expression data. More... »

PAGES

399

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-11-399

DOI

http://dx.doi.org/10.1186/1471-2105-11-399

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038767565

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20663215


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