&lt;title&gt;Statistical inference methods for gene expression arrays&lt;/title&gt;

Nadon, Robert; Shi, Peide; Skandalis, Adonis; Woody, Erik Z.; Hubschle, Hermann; Susko, Edward; Rghei, Nezar; Ramm, Peter

doi:10.1117/12.427999

Cited by 17 publications

(8 citation statements)

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“…Raw intensity values (computed via the "volume" principal measure in ArrayVision) were corrected on an individual basis using local background estimates (median intensity value of the pixels in four valley regions surrounding each spot, adjusted to the size of the spot), and duplicate background corrected intensities were averaged for each gene. These data were preprocessed and analyzed according to the methods of ArrayStat V.1.2 (Imaging Research) (16,17). Briefly, the data were log-transformed and centered within conditions.…”

Section: Methodsmentioning

confidence: 99%

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

Polacek¹,

Passerini²,

Shi³

et al. 2003

Physiological Genomics

104

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Although mRNA amplification is necessary for microarray analyses from limited amounts of cells and tissues, the accuracy of transcription profiles following amplification has not been well characterized. We tested the fidelity of differential gene expression following linear amplification by T7-mediated transcription in a well-established in vitro model of cytokine [tumor necrosis factor alpha (TNFalpha)]-stimulated human endothelial cells using filter arrays of 13,824 human cDNAs. Transcriptional profiles generated from amplified antisense RNA (aRNA) (from 100 ng total RNA, approximately 1 ng mRNA) were compared with profiles generated from unamplified RNA originating from the same homogeneous pool. Amplification accurately identified TNFalpha-induced differential expression in 94% of the genes detected using unamplified samples. Furthermore, an additional 1,150 genes were identified as putatively differentially expressed using amplified RNA which remained undetected using unamplified RNA. Of genes sampled from this set, 67% were validated by quantitative real-time PCR as truly differentially expressed. Thus, in addition to demonstrating fidelity in gene expression relative to unamplified samples, linear amplification results in improved sensitivity of detection and enhances the discovery potential of high-throughput screening by microarrays.

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Section: Methodsmentioning

confidence: 99%

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

Polacek¹,

Passerini²,

Shi³

et al. 2003

Physiological Genomics

104

View full text Add to dashboard Cite

show abstract

“…The fitted spline was then used to predict standard errors from PPM values for all of the tests for differential expression as if the predicted values were estimates from two biological replicates (R Development Core Team 2005). Like a previous use of a spline fit (Nadon et al 2001) and similar procedures (Jain et al 2003) our use of the spline fit was based on the observation that standard error estimates are much more variable than mean estimates, especially when there are few biological replicates.…”

Section: Statistical Hypothesis Testingmentioning

confidence: 99%

Genome-wide allele-specific expression analysis using Massively Parallel Signature Sequencing (MPSS™) Reveals cis- and trans-effects on gene expression in maize hybrid meristem tissue

et al. 2008

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Allelic differences in expression are important genetic factors contributing to quantitative trait variation in various organisms. However, the extent of genome-wide allele-specific expression by different modes of gene regulation has not been well characterized in plants. In this study we developed a new methodology for allele-specific expression analysis by applying Massively Parallel Signature Sequencing (MPSS), an open ended and sequencing based mRNA profiling technology. This methodology enabled a genome-wide evaluation of cis- and trans-effects on allelic expression in six meristem stages of the maize hybrid. Summarization of data from nearly 400 pairs of MPSS allelic signature tags showed that 60% of the genes in the hybrid meristems exhibited differential allelic expression. Because both alleles are subjected to the same trans-acting factors in the hybrid, the data suggest the abundance of cis-regulatory differences in the genome. Comparing the same allele expressed in the hybrid versus its inbred parents showed that 40% of the genes were differentially expressed, suggesting different trans-acting effects present in different genotypes. Such trans-acting effects may result in gene expression in the hybrid different from allelic additive expression. With this approach we quantified gene expression in the hybrid relative to its inbred parents at the allele-specific level. As compared to measuring total transcript levels, this study provides a new level of understanding of different modes of gene regulation in the hybrid and the molecular basis of heterosis.

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“…For example, Rocke et al [27] and Newton et al [13] have presented models of measurement error in microarrays that can explicitly take into account higher variance at lower expression levels. More general approaches to variance pooling have been implemented in a variety of ways, using loess-based curve fits [15], robust nonparametric spline fits [28] and sliding windows for calculating either local averages [26,29,30] or interquartile ranges [24]. These more reliable estimates of the standard deviation can be used directly to calculate Z-statistics, which are calculated according to the same formula as the standard t-statistic, but correspond to lower p-values [26,31].…”

Section: Introductionmentioning

confidence: 99%

Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation

et al. 2004

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Background: Gene microarray technology provides the ability to study the regulation of thousands of genes simultaneously, but its potential is limited without an estimate of the statistical significance of the observed changes in gene expression. Due to the large number of genes being tested and the comparatively small number of array replicates (e.g., N = 3), standard statistical methods such as the Student's t-test fail to produce reliable results. Two other statistical approaches commonly used to improve significance estimates are a penalized t-test and a Z-test using intensity-dependent variance estimates.

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<title>Statistical inference methods for gene expression arrays</title>

Cited by 17 publications

References 0 publications

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

Genome-wide allele-specific expression analysis using Massively Parallel Signature Sequencing (MPSS™) Reveals cis- and trans-effects on gene expression in maize hybrid meristem tissue

Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation

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