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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
Open accessAbstract
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
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“Using the common intra-site DEGs to validate the called inter-site DEGs, the MCC41, 42 showed that PEER also yields a higher accuracy than the original limmavoom method (Supplementary Fig. 22d,e), indicating that global data normalization analysis methods such as PEER can also be used to improve RNA-seq data across both Illumina and the PGM platforms.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [42]
In the citing paper
“for instance, the Food and Drug Administration (FDA) agency of the USA employed the MCC as the main evaluation measure in the MicroArray II / Sequencing Quality Control (MAQC/SEQC) projects [41, 42].”
From The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020) · cites this paper as [41]
In the citing paper
“Within the MicroArray Quality Control (MAQC)-II study [7], we observed, however, that the performance of gene expression models in predicting clinical outcome was limited and largely dependent on the respective clinical endpoint.”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Within the MicroArray Quality Control (MAQC)-II study [7], we observed, however, that the performance of gene expression models in predicting clinical outcome was limited and largely dependent on the respective clinical endpoint.”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Considering the fact that the nature of the clinical endpoint strongly affects a classifier’s performance [7, 18], we included endpoints that are known to cover a broad range of predictive difficulties (low difficulty, SEX, and CLASS LABEL; intermediate difficulty, EFS ALL, and OS ALL; high difficulty, EFS HR, and OS HR).”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Evaluation of the prediction performances in the validation set revealed that the endpoint represents the most relevant factor affecting model performances, which is well in line with the findings of the MAQC-II study [7].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
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MethodLow‐risk identification in multiple myeloma using a new 14‐gene model
Chen, T., Berno, T., & Zangari, M. (2012) · European Journal Of Haematology · doi:10.1111/j.1600-0609.2012.01792.x · Cited by 12
Pine, P. S., Rosenzweig, B. A., & Thompson, K. L. (2011) · BMC Biotechnology · doi:10.1186/1472-6750-11-38 · Cited by 11
sbv IMPROVER Diagnostic Signature Challenge
Rhrissorrakrai, K., Rice, J. J., Boué, S., Talikka, M., Bilal, E., Martin, F. P., Meyer, P., Norel, R., Xiang, Y., Stolovitzky, G. A., et al. (2013) · Systems Biomedicine · doi:10.4161/sysb.26325 · Cited by 11
Estimating differential expression from multiple indicators
Ilmjärv, S., Hundahl, C. A., Reimets, R., Niitsoo, M., Kolde, R., Vilo, J., Vasar, E., & Luuk, H. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku158 · Cited by 11
“As endorsed by the MAQC consortium (48), we used MCC (49) as the indicator for benchmarking performance on the MAQC reference samples, because it is informative when the distribution of the two classes in a dataset is skewed.”
MethodBayesian evolutionary hypergraph learning for predicting cancer clinical outcomes
Kim, S., Ha, J.-W., & Zhang, B. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.02.002 · Cited by 11
The Costs of Living Together: Immune Responses to the Microbiota and Chronic Gut Inflammation
Kirschman, L. J., & Milligan‐McClellan, K. (2018) · Applied and Environmental Microbiology · doi:10.1128/aem.02147-18 · Cited by 11
Prognosis value of RBBP8 expression in plasma cell myeloma
Zhang, W., Song, Y., He, X., Liu, X., Zhang, Y., Yang, Z., Yang, P., Wang, J., Hu, K., Liu, W., et al. (2019) · Cancer Gene Therapy · doi:10.1038/s41417-018-0069-3 · Cited by 11
Thakkar, S., Perkins, R., Hong, H., & Tong, W. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.64317-9 · Cited by 11
Clinical prognostic implications of EPB41L4A expression in multiple myeloma
Zhang, W., Lai, R., He, X., Liu, X., Zhang, Y., Yang, Z., Yang, P., Wang, J., Hu, K., Yuan, X., et al. (2019) · Journal of Cancer · doi:10.7150/jca.33805 · Cited by 12
Abnormal PTBP1 Expression Sustains the Disease Progression of Multiple Myeloma
Bai, H., & Chen, B. (2020) · Disease Markers · doi:10.1155/2020/4013658 · Cited by 11
Validation of Transcriptomics-Based In Vitro Methods
Corvi, R., Vilardell, M., Aubrecht, J., & Piersma, A. H. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-33826-2_10 · Cited by 11
Phylogenetic convolutional neural networks in metagenomics
Fioravanti, D., Giarratano, Y., Maggio, V., Agostinelli, C., Chierici, M., Jurman, G., & Furlanello, C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2033-5 · Cited by 19
Molecular and physiological characterization of grapevine rootstock adaptation to drought
Peccoux, A. (2011) · Oskar-Bordeaux (Universite de Bordeaux) · Cited by 11
Using biological constraints to improve prediction in precision oncology
Omar, M., Dinalankara, W., Mulder, L., Coady, T., Zanettini, C., Imada, E. L., Laurent Younes, B., Geman, D., & Marchionni, L. (2023) · iScience · doi:10.1016/j.isci.2023.106108 · Cited by 10
Omar, M., Nuzzo, P. V., Ravera, F., Bleve, S., Fanelli, G. N., Zanettini, C., Valencia, I., & Marchionni, L. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04713-3 · Cited by 10
“In addition to the testing set generated using the stratified sampling approach, we used an additional dataset [18, 19] for extra validation of the signature performance.”
MethodBricker, B. J. (2011) · Journal of Analytical & Bioanalytical Techniques · doi:10.4172/2155-9872.s10-001 · Cited by 10
Xie, F., He, M., He, L. F., Liu, K., Li, M., Hu, G., & Wen, Z. (2017) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2017.01.002 · Cited by 10
Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics
Niu, J., Yang, J., Guo, Y., Qian, K., & Wang, Q. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04758-z · Cited by 10
“For example, when Batch 1 is used as the training set and Batch 2 for test, the metrics of ACC, F_score, AUC, and MCC have increased by 13.6%, 9.7%, 15.3% and 26.9%, respectively.”
MethodInfluential
Metadata
- Authors
- MAQC Consortium
- Venue
- Nature Biotechnology
- Published
- 2010-07-30
- Volume
- 28
- Issue
- 8
- Pages
- 827-838
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3315840
- OpenAlex
- W2021560851
- Cited by
- 889
- References
- 63
- Citations by year
- 2026: 13 · 2025: 33 · 2024: 39 · 2023: 35 · 2022: 40 · 2021: 58 · 2020: 61 · 2019: 58 · 2018: 46 · 2017: 35 · 2016: 57 · 2015: 54 · 2014: 87 · 2013: 85 · 2012: 79
- FWCI
- 34.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Institute of Process Engineering, CAS Institutional Repository (submittedVersion, cc-by-nc-sa)
- IRIS (submittedVersion)
- Repository of Futwangen University of Applied Sciences (Furtwangen University) (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- National Institutes of Health (Z99 LM999999, Z99 HL999999, Z99 MH999999, Z99CL999999)
- National Cancer Institute (CA97513-01, CA55819-01A1, R00CA126184, P01CA055819, R33CA097513)
- National Institute of General Medical Sciences (T32GM074906, R01GM083084, R01GM083226, R01GM072611)
- National Center for Research Resources (R01RR021967)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- microarray quality control, gene expression profiling, prediction model, genomic classifier, model validation, pulmonary toxicity, hepatotoxicity, breast cancer, multiple myeloma, neuroblastoma, rodent toxicity, method comparison, reproducibility
- MeSH terms
- Animals, Breast Neoplasms, Disease Models, Animal, Female, Humans, Liver Diseases, Lung Diseases, Multiple Myeloma, Neoplasms, Neuroblastoma, Predictive Value of Tests, Quality Control, Survival Analysis, Guidelines as Topic, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Rats
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