Standardizing global gene expression analysis between laboratories and across platforms

Bammler, Theodore; Beyer, Richard P.; Bhattacharya, Sanchita; Boorman, Gary A.; Boyles, Abee L.; Bradford, Blair U.; Bumgarner, Roger E.; Bushel, Pierre R.; Chaturvedi, Kabir; Choi, Dongseok; Cunningham, Michael L.; Deng, Shibing; Dressman, Holly K.; Fannin, Rick D.; Farin, Fredrico M.; Freedman, Jonathan H.; Fry, Rebecca C.; Harper, Angel; Humble, Michael C.; Hurban, Patrick; Kavanagh, Terrance J.; Kaufmann, William K.; Kerr, Kathleen F.; Li, Jing; Lapidus, Jodi; Lasarev, Michael; Li, Jianying; Li, Yi-Ju; Lobenhofer, Edward K.; Lu, Xinfang; Malek, Renae L.; Milton, Sean; Nagalla, Srinivasa R.; O’Malley, Jean P.; Palmer, Valerie S.; Pattee, Patrick; Paules, Richard S.; Perou, Charles M.; Phillips, Kenneth L.; Li, Qin; Yang, Qin; Quigley, Sean; Rodland, Matthew; Rusyn, Ivan; Samson, Leona D.; Schwartz, David A.; Shi, Yan; Shin, Jung Lim; Sieber, Stella O.; Slifer, Susan H.; Speer, Marcy C.; Spencer, Peter S.; Sproles, Dean I.; Swenberg, James A.; Suk, William A.; Sullivan, Robert; Tian, Ruhui; Tennant, Raymond W.; Todd, Signe A.; Tucker, Charles J.; Houten, Bennett Van; Weis, Brenda K.; Xuan, Shirley; Zarbl, Helmut

doi:10.1038/nmeth754

Cited by 263 publications

(57 citation statements)

References 20 publications

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“…The experiments were done in high volume laboratories following standard and formalized operating procedures, and all data were centrally and uniformly normalized. Our observations are consistent with other recent reports that also showed that shared platform, standard operating procedures, and central data processing dramatically improve the reproducibility of gene expression results across laboratories (19,20). However, even with this high level of global concordance, a substantial percentage (0.15-3%) of individual gene expression measurements show z2-fold variation in the replicate pairs of data.…”

Section: Discussionsupporting

confidence: 91%

Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments

Anderson

Hess

Kapoor

et al. 2006

Clinical Cancer Research

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Purpose: The goals of this analysis were to (a) determine concordance of gene expression results from replicate experiments, (b) examine prediction agreement of multigene predictors on replicate data, and (c) assess the robustness of prediction results in the face of noise. Patients and Methods: Affymetrix U133A gene chips were used for gene expression profiling of 97 fine-needle aspiration biopsies from breast cancer. Thirty-five cases were profiled in replicates: 17 within the same laboratory, 11 in two different laboratories, and 15 to assess manual and robotic labeling. We used data from 62 cases to develop 111 distinct pharmacogenomic predictors of response to therapy. These were tested on cases profiled in duplicates to determine prediction agreement and accuracy. To evaluate the robustness of the pharmacogenomic predictors, we also introduced random noise into the informative genes in one half of the replicates.

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Section: Discussionsupporting

confidence: 91%

Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments

Anderson

Hess

Kapoor

et al. 2006

Clinical Cancer Research

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“…However, it should be noted that different platforms and/or methodologies by which these molecular changes are evaluated often yield disparate results, and even distinct nomenclatures can have an impact on the final conclusions (da Costa et al, 2016). Consequently, it is of the utmost importance to create standardized methods for data acquisition and analysis, and, despite many attempts (Kohl et al, 2014; Sun et al, 2014; Weis, 2005; Zheng et al, 2015), these have so far failed to be universally implemented. The recent technological advances observed in – omics research allow for the simultaneous measurement of millions of biochemical entities (Zierer et al, 2015).…”

Section: Models Of Senescence—what Changes?mentioning

confidence: 99%

A synopsis on aging—Theories, mechanisms and future prospects

Costa

Vitorino

Silva³

et al. 2016

Ageing Research Reviews

339

170

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Answering the question as to why we age is tantamount to answering the question of what is life itself. There are countless theories as to why and how we age, but, until recently, the very definition of aging – senescence – was still uncertain. Here, we summarize the main views of the different models of senescence, with a special emphasis on the biochemical processes that accompany aging. Though inherently complex, aging is characterized by numerous changes that take place at different levels of the biological hierarchy. We therefore explore some of the most relevant changes that take place during aging and, finally, we overview the current status of emergent aging therapies and what the future holds for this field of research. From this multi-dimensional approach, it becomes clear that an integrative approach that couples aging research with systems biology, capable of providing novel insights into how and why we age, is necessary.

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“…This is important because the results of microarray experiments can vary depending on the array design and the selection and performance of gene probes on the array. Encouraging results on cross-platform comparisons and between-laboratory reproducibility are now emerging (Bammler et al 2005; Chu et al 2004; Irizarry et al 2005; Larkin et al 2005; Yauk et al 2004). Toxicogenomics studies conducted in parallel and comparative systems can demonstrate the biologic relevance of in vitro models as surrogates for in vivo models without the need to address cross-platform (technologic) issues (Boess et al 2003; Huang et al 2003).…”

Section: Validation Of Toxicogenomics: Focus On the Biological Systemsmentioning

confidence: 99%

Meeting Report: Validation of Toxicogenomics-Based Test Systems: ECVAM–ICCVAM/NICEATM Considerations for Regulatory Use

Corvi

Ahr

Albertini

et al. 2006

Environ Health Perspect

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This is the report of the first workshop “Validation of Toxicogenomics-Based Test Systems” held 11–12 December 2003 in Ispra, Italy. The workshop was hosted by the European Centre for the Validation of Alternative Methods (ECVAM) and organized jointly by ECVAM, the U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), and the National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM). The primary aim of the workshop was for participants to discuss and define principles applicable to the validation of toxicogenomics platforms as well as validation of specific toxicologic test methods that incorporate toxicogenomics technologies. The workshop was viewed as an opportunity for initiating a dialogue between technologic experts, regulators, and the principal validation bodies and for identifying those factors to which the validation process would be applicable. It was felt that to do so now, as the technology is evolving and associated challenges are identified, would be a basis for the future validation of the technology when it reaches the appropriate stage. Because of the complexity of the issue, different aspects of the validation of toxicogenomics-based test methods were covered. The three focus areas include a) biologic validation of toxicogenomics-based test methods for regulatory decision making, b) technical and bioinformatics aspects related to validation, and c) validation issues as they relate to regulatory acceptance and use of toxicogenomics-based test methods. In this report we summarize the discussions and describe in detail the recommendations for future direction and priorities.

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Standardizing global gene expression analysis between laboratories and across platforms

Cited by 263 publications

References 20 publications

Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments

Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments

A synopsis on aging—Theories, mechanisms and future prospects

Meeting Report: Validation of Toxicogenomics-Based Test Systems: ECVAM–ICCVAM/NICEATM Considerations for Regulatory Use

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