ArticleBMC bioinformatics2013
Investigating the concordance of Gene Ontology terms reveals the intra- and inter-platform reproducibility of enrichment analysis.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson's disease: a comparison of 33 human and animal studies.BMC neurology · 2017Pooled it
- CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients.Cancers · 2023Article
- Transcriptome analysis in LRRK2 and idiopathic Parkinson's disease at different glucose levels.NPJ Parkinson's disease · 2021Article
- Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein.PLoS computational biology · 2020Article
- Atlas of RNA sequencing profiles for normal human tissues.Scientific data · 2019Article
- Shambhala: a platform-agnostic data harmonizer for gene expression data.BMC bioinformatics · 2019Article
- Bioinformatics Analysis Reveals the Altered Gene Expression of Patients with Postmenopausal Osteoporosis Using Liuweidihuang Pills Treatment.BioMed research international · 2019Article
- Article
- Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.Cell cycle (Georgetown, Tex.) · 2017Article
- How consistent are we? Interlaboratory comparison study in fathead minnows using the model estrogen 17α-ethinylestradiol to develop recommendations for environmental transcriptomics.Environmental toxicology and chemistry · 2017Article
- Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age.PloS one · 2016Article
- Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals.Regulatory toxicology and pharmacology : RTP · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundReliability and Reproducibility of differentially expressed genes (DEGs) are essential for the biological interpretation of microarray data. The microarray quality control (MAQC) project launched by US Food and Drug Administration (FDA) elucidated that the lists of DEGs generated by intra- and inter-platform comparisons can reach a high level of concordance, which mainly depended on the statistical criteria used for ranking and selecting DEGs. Generally, it will produce reproducible lists of DEGs when combining fold change ranking with a non-stringent p-value cutoff. For further interpretation of the gene expression data, statistical methods of gene enrichment analysis provide powerful tools for associating the DEGs with prior biological knowledge, e.g. Gene Ontology (GO) terms and pathways, and are widely used in genome-wide research. Although the DEG lists generated from the same compared conditions proved to be reliable, the reproducible enrichment results are still crucial to the discovery of the underlying molecular mechanism differentiating the two conditions. Therefore, it is important to know whether the enrichment results are still reproducible, when using the lists of DEGs generated by different statistic criteria from inter-laboratory and cross-platform comparisons. In our study, we used the MAQC data sets for systematically accessing the intra- and inter-platform concordance of GO terms enriched by Gene Set Enrichment Analysis (GSEA) and LRpath.
resultsIn intra-platform comparisons, the overlapped percentage of enriched GO terms was as high as ~80% when the inputted lists of DEGs were generated by fold change ranking and Significance Analysis of Microarrays (SAM), whereas the percentages decreased about 20% when generating the lists of DEGs by using fold change ranking and t-test, or by using SAM and t-test. Similar results were found in inter-platform comparisons.
conclusionsOur results demonstrated that the lists of DEGs in a high level of concordance can ensure the high concordance of enrichment results. Importantly, based on the lists of DEGs generated by a straightforward method of combining fold change ranking with a non-stringent p-value cutoff, enrichment analysis will produce reproducible enriched GO terms for the biological interpretation.
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.