Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections

<p>Abstract</p> <p>Background</p> <p>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...

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Main Author: Lazic Stanley E
Format: Article
Language:English
Published: BMC 2009-01-01
Series:BMC Neuroscience
Online Access:http://www.biomedcentral.com/1471-2202/10/5
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spelling doaj-2f21bf0fcc534415b11bb69066a503e82020-11-25T00:33:28ZengBMCBMC Neuroscience1471-22022009-01-01101510.1186/1471-2202-10-5Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sectionsLazic Stanley E<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>Based on these results, suggestions are made to reduce bias, increase precision, and ultimately provide more meaningful results of gene expression data.</p> http://www.biomedcentral.com/1471-2202/10/5
collection DOAJ
language English
format Article
sources DOAJ
author Lazic Stanley E
spellingShingle Lazic Stanley E
Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections
BMC Neuroscience
author_facet Lazic Stanley E
author_sort Lazic Stanley E
title Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections
title_short Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections
title_full Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections
title_fullStr Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections
title_full_unstemmed Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections
title_sort statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections
publisher BMC
series BMC Neuroscience
issn 1471-2202
publishDate 2009-01-01
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>Based on these results, suggestions are made to reduce bias, increase precision, and ultimately provide more meaningful results of gene expression data.</p>
url http://www.biomedcentral.com/1471-2202/10/5
work_keys_str_mv AT lazicstanleye statisticalevaluationofmethodsforquantifyinggeneexpressionbyautoradiographyinhistologicalsections
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