QUALITY CONTROL AND REPRODUCIBILITY IN DNA MICROARRAY EXPERIMENTS

2009 ◽  
Author(s):  
ANDRÉ FUJITA ◽  
JOÃO R. SATO ◽  
FERNANDO H.L. DA SILVA ◽  
MARIA C. GALVÃO ◽  
MARI C. SOGAYAR ◽  
...  
Keyword(s):  
Quality Control ◽  
Dna Microarray ◽  
Microarray Experiments

Comparison of sample-labeling techniques in DNA microarray experiments

2004 ◽  
Vol 506 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Keith Baggerly ◽  
Rahul Mitra ◽  
Rachel Grier ◽  
Dina Medhane ◽  
Guillermina Lozano ◽  
...  
Keyword(s):  
Dna Microarray ◽  
Microarray Experiments

Quality Control for Microarray Experiments

2005 ◽  
Vol 44 (03) ◽  
pp. 408-413 ◽  
Author(s):  
O. Hartmann
Keyword(s):  
Quality Control ◽  
Poor Quality ◽  
Control Measures ◽  
Control Methods ◽  
Microarray Experiments ◽  
Affymetrix Genechips ◽  
Chip Experiment ◽  
Medical Researchers ◽  
Wet Lab ◽  
Process Level

Summary Objectives: In this paper we give an overview of post-hybridization quality control methods for gene expression chips, including methods for the gene/spot level, the hybridization/chip level and the process level. We present quality control methods that can be applied after hybridization and image analysis, i.e. that use data from the chip experiment itself. Wet lab quality control steps, which should be applied before the probe is measured on a chip, are not discussed. This review is aimed towards statisticians and data analysts. Methods: We give examples of some of the quality control measures available for spotted cDNA and Affymetrix GeneChips®, the most common chip types. As quality control measures are technology and design-dependent, we will stress on methods that have the potential to be applied platform-independently. Results: Quality control should identify poor quality chips or hybridizations, as well as faulty measurements for individual genes/spots. Additionally, high throughput laboratories processing several tens or hundreds of microarrays per week have the need for an appropriate process control to be able to identify changes in the production process as early as possible. Conclusion: Microarrays have become a standard research tool for biologists and medical researchers. As a consequence, there is a great need for standardized quality control, as false findings due to problem in data quality can lead to a substantial loss of resources.

True and false discovery in DNA microarray experiments: Transcriptome changes in the hippocampus of presenilin 1 mutant mice

Methods ◽  
2005 ◽  
Vol 37 (3) ◽  
pp. 261-273 ◽  
Author(s):  
Travis Unger ◽  
Zeljka Korade ◽  
Orly Lazarov ◽  
David Terrano ◽  
Sangram S. Sisodia ◽  
...  
Keyword(s):  
Dna Microarray ◽  
Mutant Mice ◽  
Presenilin 1 ◽  
Microarray Experiments ◽  
False Discovery

scholarly journals AtCAST, a Tool for Exploring Gene Expression Similarities among DNA Microarray Experiments Using Networks

2010 ◽  
Vol 52 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Eriko Sasaki ◽  
Chitose Takahashi ◽  
Tadao Asami ◽  
Yukihisa Shimada
Keyword(s):  
Gene Expression ◽  
Dna Microarray ◽  
Microarray Experiments

scholarly journals VARAN: a web server for Variability Analysis of DNA microarray experiments

2004 ◽  
Vol 20 (10) ◽  
pp. 1641-1643 ◽  
Author(s):  
G. Golfier ◽  
M. T. Dang ◽  
L. Dauphinot ◽  
E. Graison ◽  
J. Rossier ◽  
...  
Keyword(s):  
Dna Microarray ◽  
Web Server ◽  
Microarray Experiments ◽  
Variability Analysis

scholarly journals Using Copula to Model Dependence When Testing Multiple Hypotheses in DNA Microarray Experiments: A Bayesian Approximation

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1514
Author(s):  
Elisa C. J. Maria ◽  
Isabel Salazar ◽  
Luis Sanz ◽  
Miguel A. Gómez-Villegas
Keyword(s):  
Dna Microarray ◽  
Simulated Data ◽  
Real Data ◽  
Copula Functions ◽  
Microarray Experiments ◽  
Multiple Hypotheses ◽  
Bayesian Approximation ◽  
The Right ◽  
Dependency Structures ◽  
Available Information

Many experiments require simultaneously testing many hypotheses. This is particularly relevant in the context of DNA microarray experiments, where it is common to analyze many genes to determine which of them are differentially expressed under two conditions. Another important problem in this context is how to model the dependence at the level of gene expression. In this paper, we propose a Bayesian procedure for simultaneously testing multiple hypotheses, modeling the dependence through copula functions, where all available information, both objective and subjective, can be used. The approach has the advantage that it can be used with different dependency structures. Simulated data analysis was performed to examine the performance of the proposed approach. The results show that our procedure captures the dependence appropriately classifying adequately a high percentage of true and false null hypotheses when choosing a prior distribution beta skewed to the right for the initial probability of each null hypothesis, resulting in a very powerful procedure. The procedure is also illustrated with real data.

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