Open Access
September 2010 Analysis of spatial distribution of marker expression in cells using boundary distance plots
Kingshuk Roy Choudhury, Limian Zheng, John J. Mackrill
Ann. Appl. Stat. 4(3): 1365-1382 (September 2010). DOI: 10.1214/10-AOAS340

Abstract

Boundary distance (BD) plotting is a technique for making orientation invariant comparisons of the spatial distribution of biochemical markers within and across cells/nuclei. Marker expression is aggregated over points with the same distance from the boundary. We present a suite of tools for improved data analysis and statistical inference using BD plotting. BD is computed using the Euclidean distance transform after presmoothing and oversampling of nuclear boundaries. Marker distribution profiles are averaged using smoothing with linearly decreasing bandwidth. Average expression curves are scaled and registered by x-axis dilation to compensate for uneven lighting and errors in nuclear boundary marking. Penalized discriminant analysis is used to characterize the quality of separation between average marker distributions. An adaptive piecewise linear model is used to compare expression gradients in intra, peri and extra nuclear zones. The techniques are illustrated by the following: (a) a two sample problem involving a pair of voltage gated calcium channels (Cav1.2 and AB70) marked in different cells; (b) a paired sample problem of calcium channels (Y1F4 and RyR1) marked in the same cell.

Citation

Download Citation

Kingshuk Roy Choudhury. Limian Zheng. John J. Mackrill. "Analysis of spatial distribution of marker expression in cells using boundary distance plots." Ann. Appl. Stat. 4 (3) 1365 - 1382, September 2010. https://doi.org/10.1214/10-AOAS340

Information

Published: September 2010
First available in Project Euclid: 18 October 2010

zbMATH: 1202.62149
MathSciNet: MR2758332
Digital Object Identifier: 10.1214/10-AOAS340

Keywords: Curve registration , Euclidean distance transform , Functional data analysis , image texture , smoothing

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.4 • No. 3 • September 2010
Back to Top