Mardia, Kanti V.; Petty, Emma M.; Taylor, Charles C. Matching markers and unlabeled configurations in protein gels. (English) Zbl 1401.92080 Ann. Appl. Stat. 6, No. 3, 853-869 (2012). Summary: Unlabeled shape analysis is a rapidly emerging and challenging area of statistics. This has been driven by various novel applications in bioinformatics. We consider here the situation where two configurations are matched under various constraints, namely, the configurations have a subset of manually located “markers” with high probability of matching each other while a larger subset consists of unlabeled points. We consider a plausible model and give an implementation using the EM algorithm. The work is motivated by a real experiment of gels for renal cancer and our approach allows for the possibility of missing and misallocated markers. The methodology is successfully used to automatically locate and remove a grossly misallocated marker within the given data set. Cited in 1 Document MSC: 92C40 Biochemistry, molecular biology 62P10 Applications of statistics to biology and medical sciences; meta analysis 92C50 Medical applications (general) Keywords:electrophoresis; shape; western blots Software:lpSolve; lp_solve PDFBibTeX XMLCite \textit{K. V. Mardia} et al., Ann. Appl. Stat. 6, No. 3, 853--869 (2012; Zbl 1401.92080) Full Text: DOI arXiv Euclid References: [1] Banks, R. E., Dunn, M. J., Hochstrasser, D. F., Sanchez, J. C., Blackstock, W., Pappin, D. J. and Selby, P. J. (2000). Proteomics: New perspectives, new biomedical opportunities. Lancet 356 1749-1756. [2] Berkelaar, M. (2008). Interface to lp_solve v. 5.5 to solve linear/integer programs, R package. [3] Besl, P. J. and McKay, N. D. (1992). A method for registration of 3-D shapes. IEE Trans. PAMI 14 239-256. [4] Chen, P. (2011). A novel kernel correlation model with the correspondence estimation. J. Math. Imaging Vision 39 100-120. · Zbl 1255.94010 [5] Chui, H. and Rangarajan, A. (2003). 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