Structured correlation detection with application to colocalization analysis in dual-channel fluorescence microscopic imaging

Shulei Wang Jianqing Fan Ginger Pocock Ming Yuan

Statistics Theory and Methods mathscidoc:1912.43426

arXiv preprint arXiv:1604.02158, 2016.4
Motivated by the problem of colocalization analysis in fluorescence microscopic imaging, we study in this paper structured detection of correlated regions between two random processes observed on a common domain. We argue that although intuitive, direct use of the maximum log-likelihood statistic suffers from potential bias and substantially reduced power, and introduce a simple size-based normalization to overcome this problem. We show that scanning with the proposed size-corrected likelihood ratio statistics leads to optimal correlation detection over a large collection of structured correlation detection problems.
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@inproceedings{shulei2016structured,
  title={Structured correlation detection with application to colocalization analysis in dual-channel fluorescence microscopic imaging},
  author={Shulei Wang, Jianqing Fan, Ginger Pocock, and Ming Yuan},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114343338783986},
  booktitle={arXiv preprint arXiv:1604.02158},
  year={2016},
}
Shulei Wang, Jianqing Fan, Ginger Pocock, and Ming Yuan. Structured correlation detection with application to colocalization analysis in dual-channel fluorescence microscopic imaging. 2016. In arXiv preprint arXiv:1604.02158. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114343338783986.
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