Impact of pixel intensity correlations on statistical inferences of expression levels in cDNA microarray experiments

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2015-05-01 DOI:10.1504/IJBRA.2015.069198

V. Binu, N. Nair, Prasad K. Manjunatha, M. Kalesh

引用次数: 0

Abstract

In a cDNA microarray experiment, the final measurement is intensity ratio at a spot in the microarray chip. The objective of the present study is to estimate the uncertainty associated with the final intensity ratio at each spot in cDNA microarray chips and also to explore the role of pixel intensity correlations in statistical inferences of gene expression levels. We estimate uncertainty at each spot using the theory of error propagation under two different situations: (1) when there is no correlation between pixel intensities and (2) when the pixel intensities are positively correlated. The inverses of these estimated uncertainties are used as weights in downstream analysis to test the significance of each gene. The analysis was verified on a data downloaded from the GEO database. Our study shows that the uncertainty and statistical inference of gene expression levels depend on correlation between pixel intensities within a spot.

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像素强度相关性对cDNA微阵列实验中表达水平统计推断的影响

在cDNA微阵列实验中，最后测量的是微阵列芯片上一个点的强度比。本研究的目的是估计与cDNA微阵列芯片中每个点的最终强度比相关的不确定性，并探索像素强度相关性在基因表达水平的统计推断中的作用。我们使用误差传播理论在两种不同的情况下估计每个点的不确定性:(1)当像素强度之间没有相关性时(2)当像素强度呈正相关时。这些估计的不确定性的倒数被用作下游分析的权重，以测试每个基因的显著性。从GEO数据库下载的数据验证了分析结果。我们的研究表明，基因表达水平的不确定性和统计推断取决于点内像素强度之间的相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.