Analyzing factorial designed microarray experiments

Scholtens, Denise M.; Miron, Alexander; Merchant, Faisal M.; Miller, Arden; Miron, Penelope; Iglehart, J. Dirk; Gentleman, Robert

doi:10.1016/j.jmva.2004.02.004

Cited by 18 publications

(14 citation statements)

References 53 publications

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“…Linear modeling was carried out on a per transcript basis within Bioconductor. For each probe set, the factDesign package (version 1.1.4) was used to compare a model including only graft day (number of days post‐transplant −4, 14 or 25) to a model including graft day and graft type, as well as the interaction between those variables (19). Specifically, the linear model was compared to the linear model separately for each gene i, where y is the measure of transcript abundance (GCRMA), j refers to the sample, μ is the baseline value (a day 4 isograft in this arrangement), x is an indicator function for a given condition (e.g.…”

Section: Methodsmentioning

confidence: 99%

Gene Expression Profiling in Murine Obliterative Airway Disease

Lande

Dalheimer

Mueller

et al. 2005

American Journal of Transplantation

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Lung and heart-lung transplantation are effective treatments for many diseases unresponsive to other therapy. However, long-term survival of recipients is limited by the development of obliterative bronchiolitis (OB). In this study, microarray analysis of a heterotopic mouse model of obliterative airway disease (OAD) was used to test the hypothesis that the expression and patterns of genes will correlate with specific changes in tracheal tissue developing a response to allotransplantation and the infiltrating cells manifesting these changes. Expression profiles observed were in accordance with the current paradigm of a predictable sequence of events, beginning with airway injury; an innate immune response followed by an adaptive immune response, including both cell-mediated and humoral components; and eventual loss of airway epithelial cells. These observations confirm and expand the list of genes and molecular processes that can be studied as potential surrogate markers or targets for intervention of OB.

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Section: Methodsmentioning

confidence: 99%

Gene Expression Profiling in Murine Obliterative Airway Disease

Lande

Dalheimer

Mueller

et al. 2005

American Journal of Transplantation

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“…The single-channel microarray data set used here to illustrate our new methods is from an experiment applying an estrogen treatment to cells of a human breast cancer cell line [31]. The Affymetrix human genome U-95Av2 genechip data are from four samples from an estrogen receptor positive breast cancer cell line.…”

Section: Resultsmentioning

confidence: 99%

Empirical Bayes estimation of posterior probabilities of enrichment: A comparative study of five estimators of the local false discovery rate

Yang

Bickel

2013

BMC Bioinformatics

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BackgroundIn investigating differentially expressed genes or other selected features, researchers conduct hypothesis tests to determine which biological categories, such as those of the Gene Ontology (GO), are enriched for the selected features. Multiple comparison procedures (MCPs) are commonly used to prevent excessive false positive rates. Traditional MCPs, e.g., the Bonferroni method, go to the opposite extreme: strictly controlling a family-wise error rate, resulting in excessive false negative rates. Researchers generally prefer the more balanced approach of instead controlling the false discovery rate (FDR). However, the q-values that methods of FDR control assign to biological categories tend to be too low to reliably estimate the probability that a biological category is not enriched for the preselected features. Thus, we study an application of the other estimators of that probability, which is called the local FDR (LFDR).ResultsWe considered five LFDR estimators for detecting enriched GO terms: a binomial-based estimator (BBE), a maximum likelihood estimator (MLE), a normalized MLE (NMLE), a histogram-based estimator assuming a theoretical null hypothesis (HBE), and a histogram-based estimator assuming an empirical null hypothesis (HBE-EN). Since NMLE depends not only on the data but also on the specified value of Π0, the proportion of non-enriched GO terms, it is only advantageous when either Π0 is already known with sufficient accuracy or there are data for only 1 GO term. By contrast, the other estimators work without specifying Π0 but require data for at least 2 GO terms. Our simulation studies yielded the following summaries of the relative performance of each of those four estimators. HBE and HBE-EN produced larger biases for 2, 4, 8, 32, and 100 GO terms than BBE and MLE. BBE has the lowest bias if Π0 is 1 and if the number of GO terms is between 2 and 32. The bias of MLE is no worse than that of BBE for 100 GO terms even when the ideal number of components in its underlying mixture model is unknown, but has high bias when the number of GO terms is small compared to the number of estimated parameters. For unknown values of Π0, BBE has the lowest bias for a small number of GO terms (2-32 GO terms), and MLE has the lowest bias for a medium number of GO terms (100 GO terms).ConclusionsFor enrichment detection, we recommend estimating the LFDR by MLE given at least a medium number of GO terms, by BBE given a small number of GO terms, and by NMLE given either only 1 GO term or precise knowledge of Π0.

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“…The data from the current study were derived from samples of the MCF‐7 cell line, an estrogen receptor‐positive BC cell line . Four independent samples were exposed to estrogen and another 4 samples were maintained in the absence of the hormone, and after 10 or 48 hours, each cell culture was harvested for gene expression analysis with Affymetrix human genome U‐95Av2 chips, analyzing a total of 12 625 genes …”

Section: Methodsmentioning

confidence: 99%

Using network and functional enrichment clustering analyses to find therapeutic targets for breast cancer: The role of cyclin‐dependent kinase 2

Valenzuela

Assar

2018

Math Methods in App Sciences

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MSC Classification: 62H30; 62F03Population aging is rapidly increasing in developing countries; thus, covering medical needs for breast cancer diagnosis and treatment is a priority in Latin America. We describe an approach for integrating differential expression analysis, biological pathway enrichment, in silico transcription-binding sites and network topology, to find key genes that may be used as biomarkers or therapeutic targets. This approach is based on publicly available data from microarrays of the MCF-7 breast cancer cell line treated with estrogen. We found significant estrogen-responsive genes, which were used as nodes to construct networks based on protein-protein interactions reported in the literature. Then, we conducted a topology analysis of the networks, revealing the most-connected nodes, ie, those responsible for maintaining the network structure corresponding to genes with well-acknowledged functions in G1/S cell cycle transition, such as cyclin-dependent kinase 2 (CDK2), which has been proposed as a therapeutic target in classical biochemical studies. In addition, analyses of biological pathway enrichment and in silico transcription-binding sites support the biological meaning and importance of these key genes and help to decide the best target genes. Therefore, we postulate that the integrative bioinformatics approach shown here, unlike the classical bioinformatics approach that only includes differentially expressed genes and enriched biological pathways, can be applied as an approach for finding novel biomarkers and/or therapeutic target genes for nonresponsive treatments. KEYWORDS breast cancer biomarkers, functional enrichment analysis, gene expression clustering, gene network analysis 8514 VALENZUELA AND ASSAR 8515to new cases, and the second-leading cancer with respect to mortality in women. According to BC data, 3 over half of the deaths occur in South America and Africa. 3 Studying BC is difficult because it is genetically, morphologically, and clinically heterogeneous. To obtain better diagnoses and treatments, many studies have tried to classify this cancer into subtypes, using morphological and molecular approaches. Gene expression analyses have identified at least 4 intrinsic subtypes of BC: Luminal A, Luminal B, HER2-enriched, and a triple-negative group with significant differences in terms of risk, response to therapies, and prognoses. 4 The histological characteristics of BC are currently used to determine subtype, size, lymph node status, and the cytoplasmic and nuclear grade.Worldwide, the use of the indicated markers is widely recommended to define subtypes of BC and to even rank them according to their severity. 5,6 These differences between tumor subtypes are often associated with clinical features, such as relapse-free and overall survival 7 and also treatments. The triple-negative subtype has the worst survival rates, and tamoxifen is used against ER+ cells. However, there is a controversy over whether ER, PR, HER2, and KI67 proteins can effectively capture the complex ...

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Analyzing factorial designed microarray experiments

Cited by 18 publications

References 53 publications

Gene Expression Profiling in Murine Obliterative Airway Disease

Gene Expression Profiling in Murine Obliterative Airway Disease

Empirical Bayes estimation of posterior probabilities of enrichment: A comparative study of five estimators of the local false discovery rate

Using network and functional enrichment clustering analyses to find therapeutic targets for breast cancer: The role of cyclin‐dependent kinase 2

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