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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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Plasma pTau217 predicts continuous brain amyloid levels in preclinical and early Alzheimer's disease
Devanarayan, V., Doherty, T., Charil, A., Sachdev, P., Ye, Y., Murali, L. K., Llano, D. A., Zhou, J., Reyderman, L., Hampel, H. J., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14073 · Cited by 61
“52 Subsequently, themodelswereevaluatedinthetwovalidationcohorts,VC-1andVC-2.”
Systems biological approaches to measure and understand vaccine immunity in humans
Li, S., Nakaya, H. I., Kazmin, D. A., Oh, J. Z., & Pulendran, B. (2013) · Seminars in Immunology · doi:10.1016/j.smim.2013.05.003 · Cited by 61
High dimensional biological data retrieval optimization with NoSQL technology
Wang, S., Pandis, I., Wu, C., He, S., Johnson, D., Emam, I., Guitton, F., & Guo, Y. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s8-s3 · Cited by 59
“In our experiment we took a database dump of the DEAPP schema that tranSMART uses to store patient microarray data and extracted a public Multiple Myeloma (MULTMYEL) [20,21] dataset [GEO:GSE24080] [22].”
MethodEvaluating reproducibility of AI algorithms in digital pathology with DAPPER
Bizzego, A., Bussola, N., Chierici, M., Maggio, V., Francescatto, M., Cima, L., Cristoforetti, M., Jurman, G., & Furlanello, C. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006269 · Cited by 60
Circulating microRNAs: Challenges with their use as liquid biopsy biomarkers
Takizawa, S., Matsuzaki, J., & Ochiya, T. (2022) · Cancer Biomarkers · doi:10.3233/cbm-210223 · Cited by 58
Zhang, M., Chen, M., & Tong, W. (2011) · Chemical Research in Toxicology · doi:10.1021/tx200320e · Cited by 60
Building Multivariate Systems Biology Models
Kirwan, G. M., Johansson, E., Kleemann, R., Verheij, E. R., Wheelock, Å. M., Goto, S., Trygg, J., & Wheelock, C. E. (2012) · Analytical Chemistry · doi:10.1021/ac301269r · Cited by 57
Zhou, W.-X., Bose, K., B, J. F. A., & Liu, H. (2018) · The Annals of Statistics · doi:10.1214/17-aos1606 · Cited by 57
Microarray analysis in pulmonary hypertension
Hoffmann, J., Wilhelm, J., Olschewski, A., & Kwapiszewska, G. (2016) · European Respiratory Journal · doi:10.1183/13993003.02030-2015 · Cited by 57
A generic Transcriptomics Reporting Framework (TRF) for ‘omics data processing and analysis
Gant, T. W., Sauer, U. G., Zhang, S., Chorley, B. N., Hackermüller, J., Perdichizzi, S., Tollefsen, K. E., van Ravenzwaay, B., Yauk, C. L., Tong, W., et al. (2017) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2017.11.001 · Cited by 58
“The quality control (QC) of the generation of gene expression data has been dealt with previously by the U.S. Food and Drug Administration (FDA)-led MicroArray Quality Control (MAQC) consortium (MAQC Consortium et al., 2006; Shi et al., 2005, 2008, 2010; Xu et al., 2016).”
MethodAdvantages of orthogonal inspection in chemometrics
Pinto, R. C., Trygg, J., & Gottfries, J. (2012) · Journal of Chemometrics · doi:10.1002/cem.2441 · Cited by 55
Bai, H., Zhu, H., Yan, Q., Shen, X., Lu, X., Wang, J., Li, J., & Chen, L. (2018) · Cell Communication and Signaling · doi:10.1186/s12964-018-0280-8 · Cited by 54
Difficulties and challenges in the development of precision medicine
Liu, X., Luo, X., Jiang, C., & Zhao, H. (2019) · Clinical Genetics · doi:10.1111/cge.13511 · Cited by 58
“standards will help to promote large-scale biological data collection that is standardized for precision medicine.(29) At present, the current technologies that can be applied to preci-”
BackgroundZhou, W., Bose, K., B, J. F. A., & Liu, H. (2018) · eScholarship (California Digital Library) · Cited by 55
Jones, W. D., Gong, B., Novoradovskaya, N., Li, D., Kusko, R. L., Richmond, T., Johann, D. J., Bişğin, H., Sahraeian, S. M. E., Bushel, P. R., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02316-z · Cited by 54
Liu, Q., Hu, Y., Stucky, A., Li, F., Zhong, J. F., & Wang, K. (2020) · BMC Genomics · doi:10.1186/s12864-020-07207-4 · Cited by 56
Nakaya, H. I., & Pulendran, B. (2011) · Current Opinion in HIV and AIDS · doi:10.1097/coh.0b013e32834dc37b · Cited by 53
Utilizing toxicogenomic data to understand chemical mechanism of action in risk assessment
Wilson, V. S., Keshava, N., Hester, S., Segal, D., Chiu, W. A., Thompson, C. M., & Euling, S. Y. (2011) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2011.01.017 · Cited by 57
Boja, E. S., Fehniger, T. E., Baker, M. S., Marko‐Varga, G., & Rodriguez, H. (2014) · Journal of Proteome Research · doi:10.1021/pr500753r · Cited by 53
Modeling risk stratification in human cancer
Reme, T. X., Hose, D., Theillet, C. G., & Klein, B. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt124 · Cited by 54
Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease
Mason, M. J., Schinke, C., Eng, C. L. P., Towfic, F., Gruber, F., Dervan, A., White, B. S., Pratapa, A., Guan, Y., Chen, H., et al. (2020) · Leukemia · doi:10.1038/s41375-020-0742-z · Cited by 52
Tokumaru, Y., Oshi, M., Katsuta, E., Yan, L., Huang, J. L., Nagahashi, M., Matsuhashi, N., Futamura, M., Yoshida, K., & Takabe, K. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21165744 · Cited by 53
“We analyzed three independent neoadjuvant chemotherapy (NAC) cohorts that underwent different chemotherapy regimens; GSE25066 (n = 508; taxane and anthracycline) [44], GSE32646 (n = 115; 5-FU, epirubicin, cyclophosphamide, and paclitaxel) [45], and GSE20194 (n = 278; paclitaxel, 5-FU, cyclophosphamide and doxorubicin)…”
MethodЛи, Б., Shin, H., Gulbekyan, G., Pustovalova, O., Nikolsky, Y., Hope, A. J., Bessarabova, M., Schu, M. C., Kolpakova-Hart, E., Merberg, D., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130700 · Cited by 53
Applied metabolomics in drug discovery
Cuperlovic-Culf, M., & Culf, A. S. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1195365 · Cited by 54
Radpour, R., Barekati, Z., Kohler, C., Schumacher, M. M., Grussenmeyer, T., Jenoe, P., Hartmann, N., Moes, S., Letzkus, M., Bitzer, J., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027355 · Cited by 52
Botta, C., Martino, M. T. D., Ciliberto, D., Cucè, M., Correale, P., Rossi, M., Tagliaferri, P., & Tassone, P. (2016) · Blood Cancer Journal · doi:10.1038/bcj.2016.118 · Cited by 54
Oshi, M., Tokumaru, Y., Angarita, F. A., Lee, L., Yan, L., Matsuyama, R., Endo, I., & Takabe, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-91897-7 · Cited by 52
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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