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Analysis of DNA Microarrays in Clinical Trials
Published in Ding-Geng (Din) Chen, Karl E. Peace, Pinggao Zhang, Clinical Trial Data Analysis Using R and SAS, 2017
Ding-Geng (Din) Chen, Karl E. Peace, Pinggao Zhang
The GEO supports MIAME (Minimum Information About a Microarray Experiment)-compliant data submissions. The MIAME guidelines outline the minimum information that should be included when describing a microarray experiment. Many journals and funding agencies require microarray data to comply with MIAME. GEO deposited procedures enable and encourage submitters to supply MIAME compliant data. Further in GEO, tools are provided to help users query and download experiments and curated gene expression profiles. Additional information about the features and functionalities of GEO may be found by accessing the link:
RNA Expression Profiling
Published in Attila Lorincz, Nucleic Acid Testing for Human Disease, 2016
Payman Hanifi-Moghaddam, Curt W. Burger, Theo J.M. Helmerhorst, Leen J. Blok
A good microarray experimental design is the first step toward obtaining interpretable data and should contain the following elements: A well thought-through biological question should be investigated. It may seem that control experiments unnecessarily make an investigation much more expensive; this is not true. Omitting controls will harm the interpretability of results and journal reviewers will ask for comparisons with controls.The experimental treatment of samples should be as little affected by systematic and experimental errors as possible. For example, treatment of cultured cells with hormones dissolved in different solvents introduces an extra parameter that should be controlled.Biological samples should be of the best quality and purity. Human tissue samples obtained at surgery should, for example, be snap-frozen in liquid nitrogen and stored below −80°C. Alternatively the tissues can be stored for a limited time in RNAlater® (Ambion, Inc., Austin, Texas, USA).3Isolated RNA should be of superb quality. It is not a question whether the RNA is as intact as possible; RNA should always be 100% intact. Variations in RNA quality will appear in the final analysis and are likely to lead to wrong answers.An experiment should be in agreement with the Minimum Information about a Microarray Experiment (MIAME) guidelines4 of the international Microarray Gene Expression Data (MGED) Society.5 The guidelines are specific about experimental design, including the number of replicates, and this allows researchers to interpret one another’s data more easily. The MGED Society has effectively developed data reporting guidelines, but has not addressed issues of data generation and interpretation. The latter are more intimately coupled to specific experimental platforms. In addition, microarray manufacturers such as Affymetrix have implemented MIAME-compliant data output in their new software releases.One should take great care in choosing appropriate statistical methods for low level analysis (image analysis, data quality check, and data normalization) and high level analysis (estimation of magnitude and significance of differential gene expression) to reach biological conclusions.
High-throughput tool to discriminate effects of NMs (Cu-NPs, Cu-nanowires, CuNO3, and Cu salt aged): transcriptomics in Enchytraeus crypticus
Published in Nanotoxicology, 2018
Susana I. L. Gomes, Carlos P. Roca, Natália Pegoraro, Tito Trindade, Janeck J. Scott-Fordsmand, Mónica J. B. Amorim
Fluorescence intensity data were obtained with Agilent Feature Extraction Software v. 10.7.3.1 (Agilent Technologies). Quality control was done by inspecting the reports on the Agilent Spike-in control probes. Background correction was provided by Agilent Feature Extraction software v. 10.7.3.1, using recommended protocol GE1 107 Sep09. To ensure an optimal comparison between the different normalization methods, only gene probes with good signal quality (flag IsPosAndSignif = True) in all samples were included in the analyzes. Analyses were performed with R (R-Project 2015) v. 3.3.1 and Bioconductor (Huber et al. 2015) v. 3.3 package limma (Ritchie et al. 2015) v. 3.28.20. Data was normalized with SVCD normalization Roca et al. (2017). Differential expression between control and treated samples was assessed with limma methodology. The Benjamini–Hochberg's (BH) method (Benjamini and Hochberg, 1995) was used for multiple testing correction between genes, controlling the false discovery rate below 5% (adjusted p value <0.05, independently for each comparison of treatment versus control). The Minimum Information About a Microarray Experiment (MIAME) compliant data from this experiment was submitted to the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) website (platform: GPL20310; series: GSE69792).