Matching markers and unlabeled configurations in protein gels

TL;DR

This paper introduces an EM-based method for matching labeled markers and unlabeled points in protein gel configurations, effectively handling missing and misallocated markers in bioinformatics applications.

Contribution

It presents a novel statistical model and implementation for matching markers and unlabeled points, addressing marker misallocation and missing data in protein gel analysis.

Findings

Successfully identified and corrected a misallocated marker

Demonstrated effectiveness on real renal cancer gel data

Enhanced marker matching accuracy in unlabeled configurations

Abstract

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.