ReviewClinical and translational science2011
Statistical considerations for analysis of microarray experiments.
Review in Clinical and translational science, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Microtransplantation in older patients with AML: A pilot study of safety, efficacy and immunologic effects.American journal of hematology · 2020Article
- Differential expression of immune related genes in high-grade ovarian serous carcinoma.Gynecologic oncology · 2020Article
- fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.BMC bioinformatics · 2019Article
- Primary and recurrent ovarian high-grade serous carcinomas display similar microRNA expression patterns relative to those of normal ovarian tissue.Oncotarget · 2016Article
- A blood RNA signature for tuberculosis disease risk: a prospective cohort study.Lancet (London, England) · 2016Article
- FAS Death Receptor: A Breast Cancer Subtype-Specific Radiation Response Biomarker and Potential Therapeutic Target.Radiation research · 2015Article
- Prognostic significance of differential expression of angiogenic genes in women with high-grade serous ovarian carcinoma.Gynecologic oncology · 2015Article
- An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.Microarrays (Basel, Switzerland) · 2015Article
- Genomic profiling in locally advanced and inflammatory breast cancer and its link to DCE-MRI and overall survival.International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group · 2015Article
- Up-to-Date Applications of Microarrays and Their Way to Commercialization.Microarrays (Basel, Switzerland) · 2015Review
- Combinatorial code governing cellular responses to complex stimuli.Nature communications · 2015Article
- A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.PloS one · 2015Article
- How to get the most from microarray data: advice from reverse genomics.BMC genomics · 2014Article
- Statistical Issues in the Design and Analysis of nCounter Projects.Cancer informatics · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Microarray technologies enable the simultaneous interrogation of expressions from thousands of genes from a biospecimen sample taken from a patient. This large set of expressions generates a genetic profile of the patient that may be used to identify potential prognostic or predictive genes or genetic models for clinical outcomes. The aim of this article is to provide a broad overview of some of the major statistical considerations for the design and analysis of microarrays experiments conducted as correlative science studies to clinical trials. An emphasis will be placed on how the lack of understanding and improper use of statistical concepts and methods will lead to noise discovery and misinterpretation of experimental results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.