Error-Pooling Empirical Bayes Model for Enhanced Statistical Discovery of Differential Expression in Microarray Data

2008 ◽  
Vol 38 (2) ◽  
pp. 425-436 ◽  
Author(s):  
HyungJun Cho ◽  
J.K. Lee
Keyword(s):  
Differential Expression ◽  
Microarray Data ◽  
Empirical Bayes ◽  
Bayes Model

scholarly journals Differential expression of CNGB1 in cancer of the vulva.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Disease Progression ◽  
Differential Expression ◽  
Microarray Data ◽  
Transcriptome Analysis ◽  
Cyclic Nucleotide ◽  
Vulvar Cancer ◽  
Vulvar Carcinoma ◽  
Gated Channel ◽  
Tissue Of Origin

In these brief notes we document work using published microarray data (1, 2) to pioneer integrative transcriptome analysis comparing vulvar carcinoma to its tissue of origin, the vulva. We report the differential expression of cyclic nucleotide gated channel beta 1, encoded by CNGB1, in cancer of the vulva. CNGB1 may be of pertinence to understanding transformation and disease progression in vulvar cancer (3).

scholarly journals Differential expression of DCLK1 in cancer of the vulva.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Disease Progression ◽  
Differential Expression ◽  
Microarray Data ◽  
Transcriptome Analysis ◽  
Vulvar Cancer ◽  
Vulvar Carcinoma ◽  
Tissue Of Origin

In these brief notes we document work using published microarray data (1, 2) to pioneer integrative transcriptome analysis comparing vulvar carcinoma to its tissue of origin, the vulva. We report the differential expression of doublecortin-like kinase 1, encoded by DCLK1, in cancer of the vulva. DCLK1 may be of pertinence to understanding transformation and disease progression in vulvar cancer (3).

scholarly journals Analysis of Genes Involved in Persistent Atrial Fibrillation: Comparisons of ‘Trigger’ and ‘Substrate’ Differences

2018 ◽  
Vol 47 (3) ◽  
pp. 1299-1309 ◽  
Author(s):  
Rongjun Zou ◽  
Minglei Yang ◽  
Wanting Shi ◽  
Chengxi Zheng ◽  
Hui Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Signaling Pathways ◽  
Differential Expression ◽  
Microarray Data ◽  
Left Atrial ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Genetic Traits ◽  
Receptor Interactions

Background/Aims: Recent research has improved our understanding of the pulmonary vein and surrounding left atrial (LA-PV) junction and the left atrial appendage (LAA), which are considered the ‘trigger’ and ‘substrate’ in the development of atrial fibrillation (AF), respectively. Herein, with the aim of identifying the underlying potential genetic mechanisms, we compared differences in gene expression between LA-PV junction and LAA specimens via bioinformatic analysis. Methods: Microarray data of AF (GSE41177) were downloaded from the Gene Expression Omnibus database. In addition, linear models for microarray data limma powers differential expression analyses and weighted correlation network analysis (WGCNA) were applied. Results: From the differential expression analyses, 152 differentially expressed genes and hub genes, including LEP, FOS, EDN1, NMU, CALB2, TAC1, and PPBP, were identified. Our analysis revealed that the maps of extracellular matrix (ECM)-receptor interactions, PI3K-Akt and Wnt signaling pathways, and ventricular cardiac muscle tissue morphogenesis were significantly enriched. In addition, the WGCNA results showed high correlations between genes and related genetic clusters to external clinical characteristics. Maps of the ECM-receptor interactions, chemokine signaling pathways, and the cell cycle were significantly enriched in the genes of corresponding modules and closely associated with AF duration, left atrial diameter, and left ventricular ejection function, respectively. Similarly, mapping of the TNF signaling pathway indicated significant association with genetic traits of ischemic heart disease, hypertension, and diabetes comorbidity. Conclusions: The ECM-receptor interaction as a possible central node of comparison between LA-PV and LAA samples reflected the special functional roles of ‘triggers’ and ‘substrates’ and may be closely associated with AF duration. Furthermore, LEP, FOS, EDN1, NMU, CALB2, TAC1, and PPBP genes may be implicated in the occurrence and maintenance of AF through their interactions with each other.

scholarly journals EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples

2019 ◽  
Vol 18 (6) ◽  
Author(s):  
Tobias Madsen ◽  
Michał Świtnicki ◽  
Malene Juul ◽  
Jakob Skou Pedersen
Keyword(s):  
Differential Expression ◽  
Empirical Bayes ◽  
T Test ◽  
Joint Analysis ◽  
Small Data ◽  
Data Sets ◽  
Data Types ◽  
Fast Evaluation ◽  
Equality Of Means ◽  
Small Data Sets

Abstract DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

scholarly journals Analyzing 2D gel images using a two-component empirical bayes model

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Feng Li ◽  
Françoise Seillier-Moiseiwitsch
Keyword(s):  
Empirical Bayes ◽  
Bayes Model ◽  
Two Component ◽  
2D Gel

scholarly journals Variance stabilization applied to microarray data calibration and to the quantification of differential expression

2002 ◽  
Vol 18 (Suppl 1) ◽  
pp. S96-S104 ◽  
Author(s):  
W. Huber ◽  
A. von Heydebreck ◽  
H. Sultmann ◽  
A. Poustka ◽  
M. Vingron
Keyword(s):  
Differential Expression ◽  
Microarray Data ◽  
Variance Stabilization ◽  
Data Calibration

Empirical Bayes analysis of unreplicated microarray data

2008 ◽  
Vol 24 (3) ◽  
pp. 393-408
Author(s):  
HyungJun Cho ◽  
Jaewoo Kang ◽  
Jae K. Lee
Keyword(s):  
Microarray Data ◽  
Empirical Bayes ◽  
Bayes Analysis

scholarly journals Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data

2019 ◽  
Vol 13 ◽  
pp. 117793221986081 ◽  
Author(s):  
Takayuki Osabe ◽  
Kentaro Shimizu ◽  
Koji Kadota
Keyword(s):  
Differential Expression ◽  
Bayesian Methods ◽  
Count Data ◽  
Empirical Bayes ◽  
Expression Patterns ◽  
Rna Seq ◽  
Model Framework ◽  
Gene Ranking ◽  
Normalization Methods

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.

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