ArticleNucleic acids research2007
Three methods for optimization of cross-laboratory and cross-platform microarray expression data.
Article in Nucleic acids research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed.
- Integrated gene expression profiling analysis revealsJournal of thoracic disease · 2022Article
- Development and Validation of a Prognostic Autophagy-Related Gene Pair Index Related to Tumor-Infiltrating Lymphocytes in Early-Stage Lung Adenocarcinoma.Frontiers in cell and developmental biology · 2021Article
- Aptamer-guided gene targeting in yeast and human cells.Nucleic acids research · 2014Article
- A comparative analysis of transcription factor expression during metazoan embryonic development.PloS one · 2013Article
- Multipurpose instantaneous microarray detection of acute encephalitis causing viruses and their expression profiles.Current microbiology · 2012Review
- Comparative study of classification algorithms for immunosignaturing data.BMC bioinformatics · 2012Article
- Physical characterization of the "immunosignaturing effect".Molecular & cellular proteomics : MCP · 2012Article
- Evaluation of biological sample preparation for immunosignature-based diagnostics.Clinical and vaccine immunology : CVI · 2012Article
- Multiscale integration of -omic, imaging, and clinical data in biomedical informatics.IEEE reviews in biomedical engineering · 2012Review
- StRAP: an integrated resource for profiling high-throughput cancer genomic data from stress response studies.PloS one · 2012Article
- Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction.TheScientificWorldJournal · 2012Article
- Evaluating methods for ranking differentially expressed genes applied to microArray quality control data.BMC bioinformatics · 2011Article
- The ketogenic diet reverses gene expression patterns and reduces reactive oxygen species levels when used as an adjuvant therapy for glioma.Nutrition & metabolism · 2010Article
- Cross-platform analysis of global microRNA expression technologies.BMC genomics · 2010Article
- Quality assessment of transcriptome data using intrinsic statistical properties.Genomics, proteomics & bioinformatics · 2010Article
- A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability.BMC bioinformatics · 2009Article
- Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides.Molecular & cellular proteomics : MCP · 2009Article
- Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips.Nucleic acids research · 2009Article
- Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells.BMC genomics · 2008Article
- Evolution of gene expression in the Drosophila olfactory system.Molecular biology and evolution · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microarray gene expression data becomes more valuable as our confidence in the results grows. Guaranteeing data quality becomes increasingly important as microarrays are being used to diagnose and treat patients (1-4). The MAQC Quality Control Consortium, the FDA's Critical Path Initiative, NCI's caBIG and others are implementing procedures that will broadly enhance data quality. As GEO continues to grow, its usefulness is constrained by the level of correlation across experiments and general applicability. Although RNA preparation and array platform play important roles in data accuracy, pre-processing is a user-selected factor that has an enormous effect. Normalization of expression data is necessary, but the methods have specific and pronounced effects on precision, accuracy and historical correlation. As a case study, we present a microarray calibration process using normalization as the adjustable parameter. We examine the impact of eight normalizations across both Agilent and Affymetrix expression platforms on three expression readouts: (1) sensitivity and power, (2) functional/biological interpretation and (3) feature selection and classification error. The reader is encouraged to measure their own discordant data, whether cross-laboratory, cross-platform or across any other variance source, and to use their results to tune the adjustable parameters of their laboratory to ensure increased correlation.
Indexed as
Identifiers
What OpenQuestion holds
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.