Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting.

By A. Dupuy, R. Simon. Journal of the National Cancer Institute, 2007

652 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2d3e6f0aeffa552d45d1ead5cec830283cc9aad1

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Citations in the corpus

Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets (2008, background): “Common causes include improper analysis or validation, insufficient control of false positives, and inadequate reporting of methods [16,17].”
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models (2010, result): “, findings consistent with what was observed from a recent literature surve”
Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research (2013, background): “The availability of new analytical technologies has supported recent rapid growth in the use of ‘‘omic’’ [42,43] approaches to discover potential prognostic factors using DNA (genomics), RNA (expression products, transcriptomics) [5], proteins (proteomics), or metabolites (metabolomics).”
Group Faultlines (2012, neutral): “Traditional cluster analysis methods may have limited validity when the number of individual attributes is greater than the number of group members (Dupuy & Simon, 2007).”
Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment (2010, background): “Accompanying these studies, consensus on key issues of study design and analysis protocol emerged [3,4] However, despite these efforts, most of these signatures have not yet been introduced into clinical practice.”
Systems medicine: the future of medical genomics and healthcare (2009, background): “The introduction of standards and guidelines for complete microarray workflows [43] is helping to rectify the problem; these need to cover all aspects, from RNA integrity assessment [44,45] to data analysis and reporting [46,47].”
Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction (2008, neutral): “Although many interesting and promising findings have been reported in these studies, the reliabilities of the studies have been questioned with the concern on the unstable and inconsistent results in cross-validations and cross-platform comparisons due to the relatively small sample sizes in the st”
QIN “Radiomics: The Process and the Challenges” (2012, background): “[91], many genomics-based studies that have been published contain significant analytical errors.”
Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome (2012, background): “As has been shown for genotype [58] and gene expression [59] data, phenotype can be difficult to reproducibly associate with high-dimensional genomic or metagenomic fea-Metabolic tures, particularly in large cohorts with complex population structure or when using multivariate models [60].”
EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect (2012, methodology): “The data mining methodology and reporting is in keeping with recommended guidelines, [33] such as the proper construction of cross-validation, incorporation of feature selection within cross-validation folds, testing of multiple methods, and reporting of multiple metrics of performance, among…”
A comprehensive evaluation of multicategory classification methods for microbiomic data (2013, methodology): “We focused here on supervised classification methods because unsupervised methods (such as clustering and principal component analysis), which are designed to reveal structure of the data, provide visual summaries, and help quality control, are not optimal (and depending on application may also be c”
Analysis of Gene Expression Data Using BRB-Array Tools (2007, background): “The use of gene expression profi ling has increased dramatically but serious problems in the analysis of such data in publications are prevalent (Dupuy and Simon, 2007; Michiels et al. 2005).”
AACR Centennial Series: The Biology of Cancer Metastasis: Historical Perspective (2010, positive): “tistical analysis from the early studies suggested flaws in this experimental design (188).”