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774
Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
- Stat. Appl. Genet. Mol. Biol.
, 2004
"... Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. ..."
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Cited by 1321 (24 self)
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Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.
A novel signaling pathway impact analysis
- Bioinformatics
, 2009
"... Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by t ..."
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Cited by 85 (1 self)
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Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe. Results: We describe a novel Signaling Pathway Impact Analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance p-value, which combines the enrichment and perturbation p-values. We illustrate the capabilities of the novel method on 4 real data sets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods. Availability: SPIA was implemented as an R package which is available at
limma powers differential expression analyses for RNA-sequencing and microarray studies
- Nucleic Acids Res
, 2015
"... RNA-sequencing and microarray studies (Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. ..."
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Cited by 32 (4 self)
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RNA-sequencing and microarray studies (Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.
waves: improved detection of copy number variation from microarray-based comparative genomic hybridization
- R Redon, H Fiegler, T D Andrews, B E Stranger, A G Lynch, E T Dermitzakis, N P Carter, S Tavaré, and M E Hurles. Breaking the
, 2007
"... This is an open access article distributed under the terms of the Creative Commons Attribution License ..."
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Cited by 26 (0 self)
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This is an open access article distributed under the terms of the Creative Commons Attribution License
Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips. Nucleic acids research. 2010; 38:e204. [PubMed: 20929874
"... Five strategies for pre-processing intensities from Illumina expression BeadChips are assessed from the point of view of precision and bias. The strategies include a popular variance stabilizing transformation and model-based background cor-rections that either use or ignore the control probes. Four ..."
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Cited by 22 (8 self)
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Five strategies for pre-processing intensities from Illumina expression BeadChips are assessed from the point of view of precision and bias. The strategies include a popular variance stabilizing transformation and model-based background cor-rections that either use or ignore the control probes. Four calibration data sets are used to evaluate precision, bias and false discovery rate (FDR). The original algorithms are shown to have operating characteristics that are not easily com-parable. Some tend to minimize noise while others minimize bias. Each original algorithm is shown to have an innate intensity offset, by which unlogged intensities are bounded away from zero, and the size of this offset determines its position on the noise–bias spectrum. By adding extra offsets, a continuum of related algorithms with different noise–bias trade-offs is generated, allowing direct comparison of the performance of the strategies on equivalent terms. Adding a positive offset is shown to decrease the FDR of each original algo-rithm. The potential of each strategy to generate an algorithm with an optimal noise–bias trade-off is explored by finding the offset that minimizes its FDR. The use of control probes as part of the back-ground correction and normalization strategy is shown to achieve the lowest FDR for a given bias.
Tissue-specific splicing factor gene expression signatures
- Nucleic Acids Res
, 2008
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Draghici S. Analysis of microarray experiments of gene expression profiling
- Am J Obstet Gynecol
"... The study of gene expression profiling of cells and tissue has become a major tool for discovery in medicine. Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis ..."
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Cited by 15 (1 self)
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The study of gene expression profiling of cells and tissue has become a major tool for discovery in medicine. Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of disease in obstetrics and gynecology. Moreover, an unbiased and systematic study of gene expression profiling should allow the establishment of a new taxonomy of disease for obstetric and gynecologic syndromes. Thus, a new era is emerging in which reproductive processes and disorders could be characterized using molecular tools and fingerprinting. The design, analysis, and interpretation of microarray experiments require specialized knowledge that is not part of the standard curriculum of our discipline. This article describes the types of studies that can be conducted with microarray experiments (class comparison, class prediction, class discovery). We discuss key issues pertaining to experimental design, data preprocessing, and gene selection methods. Common types of data representation are illustrated. Potential pitfalls in the interpretation of microarray experiments, as well as the strengths and limitations of this technology, are highlighted. This article is intended to assist clinicians in appraising the quality of the scientific evidence now reported in the obstetric and gynecologic literature.
BATS: a Bayesian user-friendly software for Analyzing Time Series microarray experiments.
"... BATS is a user-friendly software for Bayesian Analysis of Time Series microarray experiments based on the novel, truly functional and fully Bayesian approach proposed in Angelini et at. (2006). The software is specifically designed for time series data. It allows an user to automatically identify an ..."
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Cited by 14 (1 self)
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BATS is a user-friendly software for Bayesian Analysis of Time Series microarray experiments based on the novel, truly functional and fully Bayesian approach proposed in Angelini et at. (2006). The software is specifically designed for time series data. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles. BATS successfully manages various technical difficulties which arise in microarray time-course experiments, such as a small number of observations, non-uniform sampling intervals, and presence of missing or multiple data. BATS can carry out analysis with both simulated and real experimental data. It also handles data from different platforms. 1 Availability: BATS is written in Matlab and executable in Windows (Macintosh and Linux version are currently under development). It is freely available upon request from the authors. 1
Estimating the proportion of microarray probes expressed in an RNA sample. Nucleic acids research 38
, 2010
"... A fundamental question in microarray analysis is the estimation of the number of expressed probes in different RNA samples. Negative control probes available in the latest microarray platforms, such as Illumina whole genome expression BeadChips, provide a unique opportunity to estimate the number of ..."
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Cited by 10 (6 self)
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A fundamental question in microarray analysis is the estimation of the number of expressed probes in different RNA samples. Negative control probes available in the latest microarray platforms, such as Illumina whole genome expression BeadChips, provide a unique opportunity to estimate the number of expressed probes without setting a threshold. A novel algorithm was proposed in this study to estimate the number of expressed probes in an RNA sample by utilizing these negative controls to measure background noise. The perfor-mance of the algorithm was demonstrated by comparing different generations of Illumina BeadChips, comparing the set of probes targeting well-characterized RefSeq NM transcripts with other probes on the array and comparing pure samples with heterogenous samples. Furthermore, hematopoietic stem cells were found to have a larger transcriptome than progenitor cells. Aire knockout medullary thymic epithelial cells were shown to have significantly less expressed probes than matched wild-type cells.
Adaptation of the MapMan ontology to biotic stress responses:
, 2007
"... application in solanaceous species ..."
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