Optimizing microarray in experimental hypertension

Shannon, M. Frances; McKenzie, Katja U.S.; Edgley, Amanda J.; Rao, Sudha; Peng, Kaiman; Shweta, Amany; Schyvens, Christopher G.; Anderson, Warwick P.; Wilson, Susan R.; Pittelkow, Yvonne; Ohms, Stephen; Ja, Whitworth

doi:10.1111/j.1523-1755.2005.00090.x

Kidney International

2005

DOI: 10.1111/j.1523-1755.2005.00090.x

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Optimizing microarray in experimental hypertension

M. Frances Shannon¹,

Katja U.S. McKenzie²,

Amanda J. Edgley³

et al.

Abstract: Despite the outbred nature of the rats used in this study, they are useful for gene expression profiling comparisons. The use of paired organs from an individual animal ensures even further genetic identity, allowing determination of genes modified by the treatment of interest.

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Cited by 3 publications

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“…Identifying the changes in gene expression in common, multifactorial diseases, such as obesity, type 2 diabetes, hypertension, dyslipidemia, and the metabolic syndrome is particularly challenging, because the changes are often of small magnitude and involve multiple pathways and multiple tissues (1)(2)(3). Nonetheless, existing work using nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, suggests that modest changes in gene expression in metabolic disease can be reproducible and of fundamental importance in unraveling new aspects of the pathogenesis of disease (4,5).…”

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confidence: 99%

Analysis of gene expression in pathophysiological states: Balancing false discovery and false negative rates

Norris

Kahn

2006

Proc. Natl. Acad. Sci. U.S.A.

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Nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, has become an important tool in the study of disease. In disorders such as malignancy, gene expression often undergoes broad changes of sizable magnitude, whereas in many common multifactorial diseases, such as diabetes, obesity, and atherosclerosis, the changes in gene expression are modest. In the latter circumstance, it is therefore challenging to distinguish the truly changing from nonchanging genes, especially because statistical significance must be considered in the context of multiple hypothesis testing. Here, we present a balanced probability analysis (BPA), which provides the biologist with an approach to interpret results in the context of the total number of genes truly differentially expressed and false discovery and false negative rates for the list of genes reaching any significance threshold. In situations where the changes are of modest magnitude, sole consideration of the false discovery rate can result in poor power to detect genes truly differentially expressed. Concomitant analysis of the rate of truly differentially expressed genes not identified, i.e., the false negative rate, allows balancing of the two error rates and a more thorough insight into the data. To this end, we have developed a unique, model-based procedure for the estimation of false negative rates, which allows application of BPA to real data in which changes are modest.metabolic disease ͉ microarray analysis ͉ multiple hypothesis testing ͉ statistics

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mentioning

confidence: 99%

Analysis of gene expression in pathophysiological states: Balancing false discovery and false negative rates

Norris

Kahn

2006

Proc. Natl. Acad. Sci. U.S.A.

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Differential gene expression profiling of large and small retinal ganglion cells

Ivanov

Dvoriantchikova

Barakat

et al. 2008

Journal of Neuroscience Methods

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Different sub-populations of retinal ganglion cells (RGCs) vary in their sensitivity to pathological conditions such as retinal ischemia, diabetic retinopathy and glaucoma. Comparative Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. transcriptomic analysis of such groups will likely reveal molecular determinants of differential sensitivity to stress. However, gene expression profiling of primary neuronal sub-populations represent a challenge due to the cellular heterogeneity of retinal tissue. In this manuscript, we report the use of a fluorescent neural tracer to specifically label and selectively isolate RGCs with different soma sizes by fluorescence-activated cell sorting (FACS) for the purpose of differential gene expression profiling. We identified 145 genes that were more active in the large RGCs and 312 genes in the small RGCs. Differential data were validated by quantitative RT-PCR, several corresponding proteins were confirmed by immunohistochemistry. Functional characterization revealed differential activity of genes implicated in synaptic transmission, neurotransmitter secretion, axon guidance, chemotaxis, ion transport and tolerance to stress. An in silico reconstruction of cellular networks suggested that differences in pathway activity between the two sub-populations of RGCs are controlled by networks interconnected by SP-1, Erk2(MAPK1), Egr1, Egr2 and, potentially, regulated via transcription factors C/EBPbeta, HSF1, STAT1-and cMyc. The results show that FACS-aided purification of retrogradely labeled cells can be effectively utilized for transcriptional profiling of adult retinal neurons. NIH Public Access

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Mining Gene Expression Profile with Missing Values: An Integration of Kernel PCA and Robust Singular Values Decomposition

Islam

Hoque

Islam

et al. 2018

CBIO

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Background: Gene expression profiling and transcriptomics provide valuable information about the role of genes that are differentially expressed between two or more samples. It is always important and challenging to analyse High-throughput DNA microarray data with a number of missing values under various experimental conditions. </P><P> Objectives: Graphical data visualizations of the expression of all genes in a particular cell provide holistic views of gene expression patterns, which improve our understanding of cellular systems under normal and pathological conditions. However, current visualization methods are sensitive to missing values, which are frequently observed in microarray-based gene expression profiling, potentially affecting the subsequent statistical analyses. Methods: We addressed in this study the problem of missing values with respect to different imputation methods using gene expression biplot (GE biplot), one of the most popular gene visualization techniques. The effects of missing values for mining differentially expressed genes in gene expression data were evaluated using four well-known imputation methods: Robust Singular Value Decomposition (Robust SVD), Column Average (CA), Column Median (CM), and K-nearest Neighbors (KNN). Frobenius norm and absolute distances were used to measure the accuracy of the methods. Results: Three numerical experiments were performed using simulated data (i) and publicly available colon cancer (ii) and leukemia data (iii) to analyze the performance of each method. The results showed that CM and KNN performed better than Robust SVD and CA for identifying the index gene profile in the biplot visualization in both the simulation study and the colon cancer and leukemia microarray datasets. Conclusion: The impact of missing values on the GE biplot was smaller when the data matrix was imputed by KNN than by CM. This study concluded that KNN performed satisfactorily in generating a GE biplot in the presence of missing values in microarray data.

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Optimizing microarray in experimental hypertension

Abstract: Despite the outbred nature of the rats used in this study, they are useful for gene expression profiling comparisons. The use of paired organs from an individual animal ensures even further genetic identity, allowing determination of genes modified by the treatment of interest.

Cited by 3 publications

References 20 publications

Analysis of gene expression in pathophysiological states: Balancing false discovery and false negative rates

Analysis of gene expression in pathophysiological states: Balancing false discovery and false negative rates

Differential gene expression profiling of large and small retinal ganglion cells

Mining Gene Expression Profile with Missing Values: An Integration of Kernel PCA and Robust Singular Values Decomposition

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