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1. Analysis of cancer metabolism with high-throughput technologies

Author

Herman Damir and Markovets Aleksandra A

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect – a long-standing puzzle in cancer energy metabolism – at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data. Results We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (ACO2) in the brain exceeded both citrate synthase (CS) and isocitrate dehydrogenase 2 (IDH2), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and enolase (ENO) were considerably increased when compared to the brain sample. Conclusions The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.

Published
2011
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2. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Su Zhenqiang, Shippy Richard, Puri Raj K, Peterson Ron L, Mei Nan, Ma Yunqing, Luo Yuling, Li Quan-Zhen, Kawasaki Ernest S, Hong Huixiao, Herman Damir, Han Jing, Guo Xu, Fuscoe James C, Frueh Felix W, Fan Xiao-hui, Collins Patrick J, Chu Tzu-Ming, Bertholet Vincent, Cao Xiaoxi, Bao Wenjun, Barbacioru Catalin C, Amur Shashi, Qian Feng, Fang Hong, Boysen Cecilie, Croner Lisa J, Guo Lei, Goodsaid Federico M, Perkins Roger G, Harris Stephen C, Jensen Roderick V, Jones Wendell D, Shi Leming, Sun Yongming, Sun Hongmei, Thorn Brett, Turpaz Yaron, Wang Charles, Wang Sue, Warrington Janet A, Willey James C, Wu Jie, Xie Qian, Zhang Liang, Zhang Lu, Zhong Sheng, Wolfinger Russell D, and Tong Weida

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan – the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. Conclusion We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Published
2008
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3. Using Topological Data Analysis for diagnosis pulmonary embolism

Author

Rucco, Matteo, Falsetti, Lorenzo, Herman, Damir, Petrossian, Tanya, Merelli, Emanuela, Nitti, Cinzia, and Salvi, Aldo

Subjects
Physics - Medical Physics, Quantitative Biology - Quantitative Methods, J.2, J.3
Abstract

Pulmonary Embolism (PE) is a common and potentially lethal condition. Most patients die within the first few hours from the event. Despite diagnostic advances, delays and underdiagnosis in PE are common.To increase the diagnostic performance in PE, current diagnostic work-up of patients with suspected acute pulmonary embolism usually starts with the assessment of clinical pretest probability using plasma d-Dimer measurement and clinical prediction rules. The most validated and widely used clinical decision rules are the Wells and Geneva Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE based on topological data analysis and artificial neural network. Filter or wrapper methods for features reduction cannot be applied to our dataset: the application of these algorithms can only be performed on datasets without missing data. Instead, we applied Topological data analysis (TDA) to overcome the hurdle of processing datasets with null values missing data. A topological network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE patient topology identified two ares in the pathological group and hence two distinct clusters of PE patient populations. Additionally, the topological netowrk detected several sub-groups among healthy patients that likely are affected with non-PE diseases. TDA was further utilized to identify key features which are best associated as diagnostic factors for PE and used this information to define the input space for a back-propagation artificial neural network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is greater than the AUCs of the scores (Wells and revised Geneva) used among physicians. The results demonstrate topological data analysis and the BP-ANN, when used in combination, can produce better predictive models than Wells or revised Geneva scores system for the analyzed cohort, Comment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with arXiv:cs/0308031 by other authors without attribution

Published
2014

4. A Solvable Regime of Disorder and Interactions in Ballistic Nanostructures, Part I: Consequences for Coulomb Blockade

Author

Murthy, Ganpathy, Shankar, R., Herman, Damir, and Mathur, Harsh

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We provide a framework for analyzing the problem of interacting electrons in a ballistic quantum dot with chaotic boundary conditions within an energy $E_T$ (the Thouless energy) of the Fermi energy. Within this window we show that the interactions can be characterized by Landau Fermi liquid parameters. When $g$, the dimensionless conductance of the dot, is large, we find that the disordered interacting problem can be solved in a saddle-point approximation which becomes exact as $g\to\infty$ (as in a large-N theory). The infinite $g$ theory shows a transition to a strong-coupling phase characterized by the same order parameter as in the Pomeranchuk transition in clean systems (a spontaneous interaction-induced Fermi surface distortion), but smeared and pinned by disorder. At finite $g$, the two phases and critical point evolve into three regimes in the $u_m-1/g$ plane -- weak- and strong-coupling regimes separated by crossover lines from a quantum-critical regime controlled by the quantum critical point. In the strong-coupling and quantum-critical regions, the quasiparticle acquires a width of the same order as the level spacing $\Delta$ within a few $\Delta$'s of the Fermi energy due to coupling to collective excitations. In the strong coupling regime if $m$ is odd, the dot will (if isolated) cross over from the orthogonal to unitary ensemble for an exponentially small external flux, or will (if strongly coupled to leads) break time-reversal symmetry spontaneously., Comment: 33 pages, 14 figures. Very minor changes. We have clarified that we are treating charge-channel instabilities in spinful systems, leaving spin-channel instabilities for future work. No substantive results are changed

Published
2003
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5. Diamagnetic Persistent Currents and Spontaneous Time-Reversal Symmetry Breaking in Mesoscopic Structures

Author

Herman, Damir, Mathur, Harsh, and Murthy, Ganpathy

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Recently, new strongly interacting phases have been uncovered in mesoscopic systems with chaotic scattering at the boundaries by two of the present authors and R. Shankar. This analysis is reliable when the dimensionless conductance of the system is large, and is nonperturbative in both disorder and interactions. The new phases are the mesoscopic analogue of spontaneous distortions of the Fermi surface induced by interactions in bulk systems and can occur in any Fermi liquid channel with angular momentum $m$. Here we show that the phase with $m$ even has a diamagnetic persistent current (seen experimentally but mysterious theoretically), while that with $m$ odd can be driven through a transition which spontaneously breaks time-reversal symmetry by increasing the coupling to dissipative leads., Comment: 4 pages, three eps figures

Published
2003
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8. Using RNA sample titrations to assess microarray platform performance and normalization techniques

Author

Shippy, Richard, Fulmer-Smentek, Stephanie, Jensen, Roderick V, Jones, Wendell D, Wolber, Paul K, Johnson, Charles D, Pine, P Scott, Boysen, Cecilie, Guo, Xu, Chudin, Eugene, Sun, Yongming Andrew, Willey, James C, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Setterquist, Robert A, Wilson, Mike, Lucas, Anne Bergstrom, Novoradovskaya, Natalia, Papallo, Adam, Turpaz, Yaron, Baker, Shawn C, Warrington, Janet A, Shi, Leming, and Herman, Damir

Published
2006
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11. The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies

Author

Shi, Leming, Jones, Wendell, Jensen, Roderick, Harris, Stephen, Perkins, Roger, Goodsaid, Federico, Guo, Lei, Croner, Lisa, Boysen, Cecilie, Fang, Hong, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin, Bertholet, Vincent, Cao, Xiaoxi Megan, Chu, Tzu-Ming, Collins, Patrick, Fan, Xiao-hui, Frueh, Felix, Fuscoe, James, Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest, Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron, Puri, Raj, Qian, Feng, Shippy, Richard, Su, Zhenqiang, Sun, Yongming Andrew, Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue-Jane, Warrington, Janet, Willey, James, Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Chen, James, Wolfinger, Russell, and Tong, Weida

Published
2007
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12. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Shi, Leming, Jones, Wendell D., Jensen, Roderick V., Harris, Stephen C., Perkins, Roger G., Goodsaid, Federico M., Guo, Lei, Croner, Lisa J., Boysen, Cecilie, Fang, Hong, Qian, Feng, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin C., Bertholet, Vincent, Cao, Xiaoxi M., Chu, Tzu-Ming, Collins, Patrick J., Fan, Xiao-hui, Frueh, Felix W., Fuscoe, James C., Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest S., Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron L., Puri, Raj K., Shippy, Richard, Su, Zhenqiang, Sun, Yongming A., Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue J., Warrington, Janet A., Willey, James C., Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Wolfinger, Russell D., Tong, Weida, and Biological Sciences

Subjects
genetic structures
Abstract

Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. Conclusion We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity. Published version

Published
2008

14. CHST11 gene expression and DNA methylation in breast cancer

Author

HERMAN, DAMIR, primary, LEAKEY, TATIANA I., additional, BEHRENS, ALICE, additional, YAO-BORENGASSER, AIWEI, additional, COONEY, CRAIG A., additional, JOUSHEGHANY, FARIBA, additional, PHANAVANH, BOUNLEUT, additional, SIEGEL, ERIC R., additional, SAFAR, A. MAZIN, additional, KOROURIAN, SOHEILA, additional, KIEBER-EMMONS, THOMAS, additional, and MONZAVI-KARBASSI, BEHJATOLAH, additional

Published
2015
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17. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Author

Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjö, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjö, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, and Wolfinger, Russell D

Abstract

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.

Published
2010
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18. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Author

DNAVision SA, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjo, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, MAQC Consortium, DNAVision SA, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjo, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, and MAQC Consortium

Abstract

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.

Published
2010

19. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Biological Sciences, Shi, Leming, Jones, Wendell D., Jensen, Roderick V., Harris, Stephen C., Perkins, Roger G., Goodsaid, Federico M., Guo, Lei, Croner, Lisa J., Boysen, Cecilie, Fang, Hong, Qian, Feng, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin C., Bertholet, Vincent, Cao, Xiaoxi M., Chu, Tzu-Ming, Collins, Patrick J., Fan, Xiao-hui, Frueh, Felix W., Fuscoe, James C., Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest S., Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron L., Puri, Raj K., Shippy, Richard, Su, Zhenqiang, Sun, Yongming A., Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue J., Warrington, Janet A., Willey, James C., Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Wolfinger, Russell D., Tong, Weida, Biological Sciences, Shi, Leming, Jones, Wendell D., Jensen, Roderick V., Harris, Stephen C., Perkins, Roger G., Goodsaid, Federico M., Guo, Lei, Croner, Lisa J., Boysen, Cecilie, Fang, Hong, Qian, Feng, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin C., Bertholet, Vincent, Cao, Xiaoxi M., Chu, Tzu-Ming, Collins, Patrick J., Fan, Xiao-hui, Frueh, Felix W., Fuscoe, James C., Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest S., Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron L., Puri, Raj K., Shippy, Richard, Su, Zhenqiang, Sun, Yongming A., Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue J., Warrington, Janet A., Willey, James C., Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Wolfinger, Russell D., and Tong, Weida

Abstract

Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. Conclusion We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more repro

Published
2008

24. Changes in the Expression of Proteasome Genes in Tumor Cells Following Short-Term Proteasome Inhibitor Therapy Predicts Survival in Multiple Myeloma Treated with Bortezomib-Containing Multi-Agent Chemotherapy

Author

Shaughnessy, John D., primary, Qu, Pingping, additional, Edmondson, Ricky, additional, Herman, Damir, additional, Zhou, Yiming, additional, Tian, Erming, additional, van Rhee, Frits, additional, Pineda-Roman, Mauricio, additional, Alsayed, Yazan, additional, Anaissie, Elias, additional, Qiang, Ya-wei, additional, Epstein, Joshua, additional, Yaccoby, Shmuel, additional, Williams, David, additional, Zhan, Fenghuang, additional, Stephens, Owen, additional, Hoering, Antje, additional, Crowley, John, additional, and Barlogie, Bart, additional

Published
2008
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25. Integration of DNA Copy Number and Gene Expression Alterations Reveal Novel Insights into the Molecular Pathogenesis and Prognosis of Multiple Myeloma

Author

Zhou, Yiming, primary, Barlogie, Bart, primary, Herman, Damir, primary, Stephens, Owen, primary, Tian, Erming, primary, Williams, David, primary, Cartron, Marie-astrid, primary, van Rhee, Frits, primary, Pineda-Roman, Mauricio, primary, Alsayed, Yazan, primary, Anaissie, Elias, primary, Sawyer, Jeffrey, primary, and Shaughnessy, John D., primary

Published
2008
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27. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Shi, Leming, primary, Jones, Wendell D, additional, Jensen, Roderick V, additional, Harris, Stephen C, additional, Perkins, Roger G, additional, Goodsaid, Federico M, additional, Guo, Lei, additional, Croner, Lisa J, additional, Boysen, Cecilie, additional, Fang, Hong, additional, Qian, Feng, additional, Amur, Shashi, additional, Bao, Wenjun, additional, Barbacioru, Catalin C, additional, Bertholet, Vincent, additional, Cao, Xiaoxi Megan, additional, Chu, Tzu-Ming, additional, Collins, Patrick J, additional, Fan, Xiao-hui, additional, Frueh, Felix W, additional, Fuscoe, James C, additional, Guo, Xu, additional, Han, Jing, additional, Herman, Damir, additional, Hong, Huixiao, additional, Kawasaki, Ernest S, additional, Li, Quan-Zhen, additional, Luo, Yuling, additional, Ma, Yunqing, additional, Mei, Nan, additional, Peterson, Ron L, additional, Puri, Raj K, additional, Shippy, Richard, additional, Su, Zhenqiang, additional, Sun, Yongming Andrew, additional, Sun, Hongmei, additional, Thorn, Brett, additional, Turpaz, Yaron, additional, Wang, Charles, additional, Wang, Sue Jane, additional, Warrington, Janet A, additional, Willey, James C, additional, Wu, Jie, additional, Xie, Qian, additional, Zhang, Liang, additional, Zhang, Lu, additional, Zhong, Sheng, additional, Wolfinger, Russell D, additional, and Tong, Weida, additional

Published
2008
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31. Rat toxicogenomic study reveals analytical consistency across microarray platforms.

Author

Lei Guo, Lobenhofer, Edward K., Wang, Charles, Shippy, Richard, Harris, Stephen C., Lu Zhang, Nan Mei, Tao Chen, Herman, Damir, Goodsaid, Federico M., Hurban, Patrick, Phillips, Kenneth L., Jun Xu, Xutao Deng, Sun, Yongming Andrew, Weida Tong, Yvonne P.Dragan, and Leming Shi

Subjects
RATS, RNA, DNA microarrays, MOLECULAR genetics, GENE expression, GENETIC toxicology
Abstract

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed. [ABSTRACT FROM AUTHOR]

Published
2006
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32. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Author

Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy JD Jr, Oberthuer A, Thomas RS, Paules RS, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C, Gallas BD, Ge X, Megherbi DB, Symmans WF, Wang MD, Zhang J, Bitter H, Brors B, Bushel PR, Bylesjo M, Chen M, Cheng J, Cheng J, Chou J, Davison TS, Delorenzi M, Deng Y, Devanarayan V, Dix DJ, Dopazo J, Dorff KC, Elloumi F, Fan J, Fan S, Fan X, Fang H, Gonzaludo N, Hess KR, Hong H, Huan J, Irizarry RA, Judson R, Juraeva D, Lababidi S, Lambert CG, Li L, Li Y, Li Z, Lin SM, Liu G, Lobenhofer EK, Luo J, Luo W, McCall MN, Nikolsky Y, Pennello GA, Perkins RG, Philip R, Popovici V, Price ND, Qian F, Scherer A, Shi T, Shi W, Sung J, Thierry-Mieg D, Thierry-Mieg J, Thodima V, Trygg J, Vishnuvajjala L, Wang SJ, Wu J, Wu Y, Xie Q, Yousef WA, Zhang L, Zhang X, Zhong S, Zhou Y, Zhu S, Arasappan D, Bao W, Lucas AB, Berthold F, Brennan RJ, Buness A, Catalano JG, Chang C, Chen R, Cheng Y, Cui J, Czika W, Demichelis F, Deng X, Dosymbekov D, Eils R, Feng Y, Fostel J, Fulmer-Smentek S, Fuscoe JC, Gatto L, Ge W, Goldstein DR, Guo L, Halbert DN, Han J, Harris SC, Hatzis C, Herman D, Huang J, Jensen RV, Jiang R, Johnson CD, Jurman G, Kahlert Y, Khuder SA, Kohl M, Li J, Li L, Li M, Li QZ, Li S, Li Z, Liu J, Liu Y, Liu Z, Meng L, Madera M, Martinez-Murillo F, Medina I, Meehan J, Miclaus K, Moffitt RA, Montaner D, Mukherjee P, Mulligan GJ, Neville P, Nikolskaya T, Ning B, Page GP, Parker J, Parry RM, Peng X, Peterson RL, Phan JH, Quanz B, Ren Y, Riccadonna S, Roter AH, Samuelson FW, Schumacher MM, Shambaugh JD, Shi Q, Shippy R, Si S, Smalter A, Sotiriou C, Soukup M, Staedtler F, Steiner G, Stokes TH, Sun Q, Tan PY, Tang R, Tezak Z, Thorn B, Tsyganova M, Turpaz Y, Vega SC, Visintainer R, von Frese J, Wang C, Wang E, Wang J, Wang W, Westermann F, Willey JC, Woods M, Wu S, Xiao N, Xu J, Xu L, Yang L, Zeng X, Zhang J, Zhang L, Zhang M, Zhao C, Puri RK, Scherf U, Tong W, and Wolfinger RD

Subjects
Animals, Breast Neoplasms diagnosis, Breast Neoplasms genetics, Disease Models, Animal, Female, Gene Expression Profiling methods, Gene Expression Profiling standards, Guidelines as Topic, Humans, Liver Diseases etiology, Liver Diseases pathology, Lung Diseases etiology, Lung Diseases pathology, Multiple Myeloma diagnosis, Multiple Myeloma genetics, Neoplasms diagnosis, Neuroblastoma diagnosis, Neuroblastoma genetics, Predictive Value of Tests, Quality Control, Rats, Survival Analysis, Liver Diseases genetics, Lung Diseases genetics, Neoplasms genetics, Neoplasms mortality, Oligonucleotide Array Sequence Analysis methods, Oligonucleotide Array Sequence Analysis standards
Abstract

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.

Published
2010
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33. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

Author

Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, Luo Y, Sun YA, Willey JC, Setterquist RA, Fischer GM, Tong W, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Schrf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK, Zhang L, Amur S, Bao W, Barbacioru CC, Lucas AB, Bertholet V, Boysen C, Bromley B, Brown D, Brunner A, Canales R, Cao XM, Cebula TA, Chen JJ, Cheng J, Chu TM, Chudin E, Corson J, Corton JC, Croner LJ, Davies C, Davison TS, Delenstarr G, Deng X, Dorris D, Eklund AC, Fan XH, Fang H, Fulmer-Smentek S, Fuscoe JC, Gallagher K, Ge W, Guo L, Guo X, Hager J, Haje PK, Han J, Han T, Harbottle HC, Harris SC, Hatchwell E, Hauser CA, Hester S, Hong H, Hurban P, Jackson SA, Ji H, Knight CR, Kuo WP, LeClerc JE, Levy S, Li QZ, Liu C, Liu Y, Lombardi MJ, Ma Y, Magnuson SR, Maqsodi B, McDaniel T, Mei N, Myklebost O, Ning B, Novoradovskaya N, Orr MS, Osborn TW, Papallo A, Patterson TA, Perkins RG, Peters EH, Peterson R, Philips KL, Pine PS, Pusztai L, Qian F, Ren H, Rosen M, Rosenzweig BA, Samaha RR, Schena M, Schroth GP, Shchegrova S, Smith DD, Staedtler F, Su Z, Sun H, Szallasi Z, Tezak Z, Thierry-Mieg D, Thompson KL, Tikhonova I, Turpaz Y, Vallanat B, Van C, Walker SJ, Wang SJ, Wang Y, Wolfinger R, Wong A, Wu J, Xiao C, Xie Q, Xu J, Yang W, Zhang L, Zhong S, Zong Y, and Slikker W Jr

Subjects
Equipment Design, Equipment Failure Analysis, Gene Expression Profiling methods, Quality Control, Reproducibility of Results, Sensitivity and Specificity, United States, Gene Expression Profiling instrumentation, Oligonucleotide Array Sequence Analysis instrumentation, Quality Assurance, Health Care methods
Abstract

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Published
2006
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