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1. Prognostic relevance and performance characteristics of serum IGFBP-2 and PAPP-A in women with breast cancer:a long-term Danish cohort study

Author

Espelund, Ulrick, Renehan, Andrew G., Cold, Søren, Oxvig, Claus, Lancashire, Lee, Su, Zhenqiang, Flyvbjerg, Allan, Frystyk, Jan, Espelund, Ulrick, Renehan, Andrew G., Cold, Søren, Oxvig, Claus, Lancashire, Lee, Su, Zhenqiang, Flyvbjerg, Allan, and Frystyk, Jan

Abstract

Measurement of circulating insulin-like growth factors (IGFs), in particular IGF-binding protein (IGFBP)-2, at the time of diagnosis, is independently prognostic in many cancers, but its clinical performance against other routinely determined prognosticators has not been examined. We measured IGF-I, IGF-II, pro-IGF-II, IGF bioactivity, IGFBP-2, -3, and pregnancy-associated plasma protein A (PAPP-A), an IGFBP regulator, in baseline samples of 301 women with breast cancer treated on four protocols (Odense, Denmark: 1993–1998). We evaluated performance characteristics (expressed as area under the curve, AUC) using Cox regression models to derive hazard ratios (HR) with 95% confidence intervals (CIs) for 10-year recurrence-free survival (RFS) and overall survival (OS), and compared those against the clinically used Nottingham Prognostic Index (NPI). We measured the same biomarkers in 531 noncancer individuals to assess multidimensional relationships (MDR), and evaluated additional prognostic models using survival artificial neural network (SANN) and survival support vector machines (SSVM), as these enhance capture of MDRs. For RFS, increasing concentrations of circulating IGFBP-2 and PAPP-A were independently prognostic [HRbiomarker doubling: 1.474 (95% CIs: 1.160, 1.875, P = 0.002) and 1.952 (95% CIs: 1.364, 2.792, P < 0.001), respectively]. The AUCRFS for NPI was 0.626 (Cox model), improving to 0.694 (P = 0.012) with the addition of IGFBP-2 plus PAPP-A. Derived AUCRFS using SANN and SSVM did not perform superiorly. Similar patterns were observed for OS. These findings illustrate an important principle in biomarker qualification—measured circulating biomarkers may demonstrate independent prognostication, but this does not necessarily translate into substantial improvement in clinical performance.

Published
2018

2. Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play

Author

Tan, Yongxi, Yang, Jin, Chen, Xin, Song, Qitao, Chen, Yunjun, Ye, Zhangxiang, Su, Zhenqiang, Tan, Yongxi, Yang, Jin, Chen, Xin, Song, Qitao, Chen, Yunjun, Ye, Zhangxiang, and Su, Zhenqiang

Abstract

Mobile network that millions of people use every day is one of the most complex systems in the world. Optimization of mobile network to meet exploding customer demand and reduce capital/operation expenditures poses great challenges. Despite recent progress, application of deep reinforcement learning (DRL) to complex real world problem still remains unsolved, given data scarcity, partial observability, risk and complex rules/dynamics in real world, as well as the huge reality gap between simulation and real world. To bridge the reality gap, we introduce a Sim-to-Real framework to directly transfer learning from simulation to real world via graph convolutional neural network (CNN) - by abstracting partially observable mobile network into graph, then distilling domain-variant irregular graph into domain-invariant tensor in locally Euclidean space as input to CNN -, domain randomization and multi-task learning. We use a novel self-play mechanism to encourage competition among DRL agents for best record on multiple tasks via simulated annealing, just like athletes compete for world record in decathlon. We also propose a decentralized multi-agent, competitive and cooperative DRL method to coordinate the actions of multi-cells to maximize global reward and minimize negative impact to neighbor cells. Using 6 field trials on commercial mobile networks, we demonstrate for the first time that a DRL agent can successfully transfer learning from simulation to complex real world problem with imperfect information, complex rules/dynamics, huge state/action space, and multi-agent interactions, without any training in the real world., Comment: Accepted by NIPS 2018 Workshop

Published
2018

3. Prognostic relevance and performance characteristics of serum IGFBP-2 and PAPP-A in women with breast cancer:a long-term Danish cohort study

Author

Espelund, Ulrick, Renehan, Andrew G., Cold, Søren, Oxvig, Claus, Lancashire, Lee, Su, Zhenqiang, Flyvbjerg, Allan, Frystyk, Jan, Espelund, Ulrick, Renehan, Andrew G., Cold, Søren, Oxvig, Claus, Lancashire, Lee, Su, Zhenqiang, Flyvbjerg, Allan, and Frystyk, Jan

Abstract

Measurement of circulating insulin-like growth factors (IGFs), in particular IGF-binding protein (IGFBP)-2, at the time of diagnosis, is independently prognostic in many cancers, but its clinical performance against other routinely determined prognosticators has not been examined. We measured IGF-I, IGF-II, pro-IGF-II, IGF bioactivity, IGFBP-2, -3, and pregnancy-associated plasma protein A (PAPP-A), an IGFBP regulator, in baseline samples of 301 women with breast cancer treated on four protocols (Odense, Denmark: 1993–1998). We evaluated performance characteristics (expressed as area under the curve, AUC) using Cox regression models to derive hazard ratios (HR) with 95% confidence intervals (CIs) for 10-year recurrence-free survival (RFS) and overall survival (OS), and compared those against the clinically used Nottingham Prognostic Index (NPI). We measured the same biomarkers in 531 noncancer individuals to assess multidimensional relationships (MDR), and evaluated additional prognostic models using survival artificial neural network (SANN) and survival support vector machines (SSVM), as these enhance capture of MDRs. For RFS, increasing concentrations of circulating IGFBP-2 and PAPP-A were independently prognostic [HRbiomarker doubling: 1.474 (95% CIs: 1.160, 1.875, P = 0.002) and 1.952 (95% CIs: 1.364, 2.792, P < 0.001), respectively]. The AUCRFS for NPI was 0.626 (Cox model), improving to 0.694 (P = 0.012) with the addition of IGFBP-2 plus PAPP-A. Derived AUCRFS using SANN and SSVM did not perform superiorly. Similar patterns were observed for OS. These findings illustrate an important principle in biomarker qualification—measured circulating biomarkers may demonstrate independent prognostication, but this does not necessarily translate into substantial improvement in clinical performance.

Published
2018

4. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Author

Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, Fischer, Matthias, Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, and Fischer, Matthias

Abstract

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. Conclusions: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Published
2015

5. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Author

Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, Fischer, Matthias, Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, and Fischer, Matthias

Abstract

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. Conclusions: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Published
2015

6. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Author

Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, Fischer, Matthias, Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, and Fischer, Matthias

Published
2015

7. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Author

Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, Fischer, Matthias, Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, and Fischer, Matthias

Published
2015

8. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

Author

Su, Zhenqiang, Fang, Hong, Hong, Huixiao, Shi, Leming, Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Dong, Zirui, Lancashire, Lee J., Bessarabova, Marina, Yang, Xi, Ning, Baitang, Gong, Binsheng, Meehan, Joe, Xu, Joshua, Ge, Weigong, Perkins, Roger, Fischer, Matthias, Tong, Weida, Su, Zhenqiang, Fang, Hong, Hong, Huixiao, Shi, Leming, Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Dong, Zirui, Lancashire, Lee J., Bessarabova, Marina, Yang, Xi, Ning, Baitang, Gong, Binsheng, Meehan, Joe, Xu, Joshua, Ge, Weigong, Perkins, Roger, Fischer, Matthias, and Tong, Weida

Abstract

Background: Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? Results: We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Conclusions: Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Published
2014

9. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Author

Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caiment, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., Shi, Leming, Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caiment, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., and Shi, Leming

Abstract

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Published
2014

10. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

Author

Su, Zhenqiang, Fang, Hong, Hong, Huixiao, Shi, Leming, Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Dong, Zirui, Lancashire, Lee J., Bessarabova, Marina, Yang, Xi, Ning, Baitang, Gong, Binsheng, Meehan, Joe, Xu, Joshua, Ge, Weigong, Perkins, Roger, Fischer, Matthias, Tong, Weida, Su, Zhenqiang, Fang, Hong, Hong, Huixiao, Shi, Leming, Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Dong, Zirui, Lancashire, Lee J., Bessarabova, Marina, Yang, Xi, Ning, Baitang, Gong, Binsheng, Meehan, Joe, Xu, Joshua, Ge, Weigong, Perkins, Roger, Fischer, Matthias, and Tong, Weida

Abstract

Background: Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? Results: We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Conclusions: Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Published
2014

11. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Author

Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caiment, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., Shi, Leming, Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caiment, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., and Shi, Leming

Abstract

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Published
2014

12. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Author

Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caimet, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., Shi, Leming, Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caimet, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., and Shi, Leming

Published
2014

13. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Author

Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caimet, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., Shi, Leming, Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caimet, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., and Shi, Leming

Published
2014

14. 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
Full Text
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15. Evaluation of gene expression data generated from expired Affymetrix GeneChip(R) microarrays using MAQC reference RNA samples

Author

Wen, Zhining, Wen, Zhining, Wang, Charles, Shi, Quan, Huang, Ying, Su, Zhenqiang, Hong, Huixiao, Tong, Weida, Shi, Leming, Wen, Zhining, Wen, Zhining, Wang, Charles, Shi, Quan, Huang, Ying, Su, Zhenqiang, Hong, Huixiao, Tong, Weida, and Shi, Leming

Abstract

Background The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip® microarrays unprocessed before their manufacturer’s expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions. Results Microarray data were generated in 2009 in three replicates for each of the two MAQC samples with either expired Affymetrix U133A or unexpired U133Plus2 microarrays. These results were compared with data obtained in 2005 on the U133Plus2 microarray. The percentage of overlap between the lists of differentially expressed genes (DEGs) from U133Plus2 microarray data generated in 2009 and in 2005 was 97.44%. While there was some degree of fold change compression in the expired U133A microarrays, the percentage of overlap between the lists of DEGs from the expired and unexpired microarrays was as high as 96.99%. Moreover, the microarray data generated using the expired U133A microarrays in 2009 were highly concordant with microarray and TaqMan® data generated by the MAQC project in 2005. Conclusions Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturer’s expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the MAQC reference RNA samples, s

Published
2010

16. 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

17. Evaluation of gene expression data generated from expired Affymetrix GeneChip(R) microarrays using MAQC reference RNA samples

Author

Wen, Zhining, Wen, Zhining, Wang, Charles, Shi, Quan, Huang, Ying, Su, Zhenqiang, Hong, Huixiao, Tong, Weida, Shi, Leming, Wen, Zhining, Wen, Zhining, Wang, Charles, Shi, Quan, Huang, Ying, Su, Zhenqiang, Hong, Huixiao, Tong, Weida, and Shi, Leming

Abstract

Background The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip® microarrays unprocessed before their manufacturer’s expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions. Results Microarray data were generated in 2009 in three replicates for each of the two MAQC samples with either expired Affymetrix U133A or unexpired U133Plus2 microarrays. These results were compared with data obtained in 2005 on the U133Plus2 microarray. The percentage of overlap between the lists of differentially expressed genes (DEGs) from U133Plus2 microarray data generated in 2009 and in 2005 was 97.44%. While there was some degree of fold change compression in the expired U133A microarrays, the percentage of overlap between the lists of DEGs from the expired and unexpired microarrays was as high as 96.99%. Moreover, the microarray data generated using the expired U133A microarrays in 2009 were highly concordant with microarray and TaqMan® data generated by the MAQC project in 2005. Conclusions Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturer’s expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the MAQC reference RNA samples, s

Published
2010

18. 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, 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
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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

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