Browsing by Author "Zhang, Liang"

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    The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies
    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 (2008-08-12)
    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.
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    Design Verification for Sequential Systems at Various Abstraction Levels
    Zhang, Liang (Virginia Tech, 2005-01-27)
    With the ever increasing complexity of digital systems, functional verification has become a daunting task to circuit designers. Functional verification alone often surpasses 70% of the total development cost and the situation has been projected to continue to worsen. The most critical limitations of existing techniques are the capacity issue and the run-time issue. This dissertation addresses the functional verification problem using a unified approach, which utilizes different core algorithms at various abstraction levels. At the logic level, we focus on incorporating a set of novel ideas to existing formal verification approaches. First, we present a number of powerful optimizations to improve the performance and capacity of a typical SAT-based bounded model checking framework. Secondly, we present a novel method for performing dynamic abstraction within a framework for abstraction-refinement based model checking. Experiments on a wide range of industrial designs have shown that the proposed optimizations consistently provide between 1-2 orders of magnitude speedup and can be extremely useful in enhancing the efficacy of existing formal verification algorithms. At the register transfer level, where the formal verification is less likely to succeed, we developed an efficient ATPG-based validation framework, which leverages the high-level circuit information and an improved observability-enhanced coverage to generate high quality validation sequences. Experiments show that our approach is able to generate high quality validation vectors, which achieve both high tag coverage and high bug coverage with extremely low computational cost.
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    Do Peers Affect Undergraduates’ Decisions to Switch Majors?
    Pu, Shi; Yan, Yu; Zhang, Liang (Annenberg Institute at Brown University, 2020-06-01)
    This study used college dormitory room and social group assignment data to investigate the peer effect on the probability of college students switching their major fields of study. The results revealed strong evidence of peer effects on students’ decisions to switch majors. In particular, the number of a student’s peers who have the same major significantly reduces the student’s likelihood of switching majors; however, when a same-major peer switches majors, it significantly increases a student’s probability of switching majors. This study also found that peers’ majors affected students’ choice of destination majors. Students in the same peer group are more likely to choose the same destination majors, compared to non-peers. Finally, the authors found that in general peer effects at the dormitory room level, both in choice and persistence of major, were stronger than were peer effects at the social group level.