Fast Bead Detection and Inexact Microarray Pattern Matching for In-situ Encoded Bead-based Array

Abstract

This paper presents an automatic bead detection and bead array pattern matching technique developed for the In-situ Encoded Bead-based Array (IEBA) technology. A supervised learning based bead detection technique robust to irregular illumination variations and noise is developed. An efficient and effective graph matching technique that combines graph spectral analysis and sequence alignment is used to match bead array patterns. The matching algorithm proposed is rotation and scale-invariant. The pattern matching algorithm performs in-exact matching and is capable of handling very large numbers of outliers in the target graph as well as large number of occlusions in the template graph. The matching algorithm uses dynamic programming and can give good time performances dependent only on the number of nodes in the template and target graphs, irrespective of the number of outliers and occlusions. The algorithm can detect and match large number of beads in a few