ArticleBMC neuroscience2009
Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections.
Article in BMC neuroscience, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Differential maturation of vesicular glutamate and GABA transporter expression in the mouse auditory forebrain during the first weeks of hearing.Brain structure & function · 2016Article
- Rapid preparation of mutated influenza hemagglutinins for influenza virus pandemic prevention.AMB Express · 2016Article
- Age-dependent changes in the protein expression levels of Redd1 and mTOR in the gerbil hippocampus during normal aging.Molecular medicine reports · 2016Article
- Differentiation of forebrain and hippocampal dopamine 1-class receptors, D1R and D5R, in spatial learning and memory.Hippocampus · 2016Article
- Differential roles of the dopamine 1-class receptors, D1R and D5R, in hippocampal dependent memory.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?BMC neuroscience · 2010Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
backgroundIn 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.
resultsThe 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.
conclusionBased on these results, suggestions are made to reduce bias, increase precision, and ultimately provide more meaningful results of gene expression data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.