Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections.
BACKGROUND
In situ hybridisation (ISH) combined with autoradiography is a standard method of measuring the amount of gene expression in histological sections, but the methods used to quantify gene expression in the resulting digital images vary greatly between studies and can potentially give conflicting results.


RESULTS
The present study examines commonly used methods for analysing ISH images and demonstrates that these methods are not optimal. Image segmentation based on thresholding can be subject to floor-effects and lead to biased results. In addition, including the area of the structure or region of interest in the calculation of gene expression can lead to a large loss of precision and can also introduce bias. Finally, converting grey level pixel intensities to optical densities or units of radioactivity is unnecessary for most applications and can lead to data with poor statistical properties. A modification of an existing method for selecting the structure or region of interest is introduced which performs better than alternative methods in terms of bias and precision.


CONCLUSIONS
Based on these results, suggestions are made to reduce bias, increase precision, and ultimately provide more meaningful results of gene expression data.
Lazic
Stanley E
SE
Department of Applied Mathematics and Theoretical Physics, Cambridge Computational Biology Institute, University of Cambridge, Cambridge, UK. stan.lazic@cantab.net
D000465:Algorithms; D000818:Animals; D001345:Autoradiography / Q000379:methods* / Q000706:statistics & numerical data; D015982:Bias; D001921:Brain / Q000378:metabolism; D002250:Carbon Radioisotopes; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D017403:In Situ Hybridization / Q000379:methods* / Q000706:statistics & numerical data; D008297:Male; D015233:Models, Statistical; D051381:Rats; D017207:Rats, Sprague-Dawley
D002250:Carbon Radioisotopes