Browsing by Author "Zhang, Lu"

Now showing 1 - 4 of 4
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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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    Increased efficacy of metformin corresponds to differential metabolic effects in the ovarian tumors from obese versus lean mice
    Han, Jianjun; Wysham, Weiya Z.; Zhong, Yan; Guo, Hui; Zhang, Lu; Malloy, Kim M.; Dickens, Hallum K.; Huh, Gene; Lee, Douglas; Makowski, Liza; Zhou, Chunxiao; Bae-Jump, Victoria L. (2017-12-19)
    Obesity is a significant risk factor for ovarian cancer (OC) and associated with worse outcomes for this disease. We assessed the anti-tumorigenic effects of metformin in human OC cell lines and a genetically engineered mouse model of high grade serous OC under obese and lean conditions. Metformin potently inhibited growth in a dose-dependent manner in all four human OC cell lines through AMPK/mTOR pathways. Treatment with metformin resulted in G1 arrest, induction of apoptosis, reduction of invasion and decreased hTERT expression. In the K18-gT(121)(+/-); p53(fl/fl); Brca1(fl/fl) (KpB) mouse model, metformin inhibited tumor growth in both lean and obese mice. However, in the obese mice, metformin decreased tumor growth by 60%, whereas tumor growth was only decreased by 32% in the lean mice (p=0.003) compared to vehicle-treated mice. The ovarian tumors from obese mice had evidence of impaired mitochondrial complex 2 function and energy supplied by omega fatty acid oxidation rather than glycolysis as compared to lean mice, as assessed by metabolomic profiling. The improved efficacy of metformin in obesity corresponded with inhibition of mitochondrial complex 1 and fatty acid oxidation, and stimulation of glycolysis in only the OCs of obese versus lean mice. In conclusion, metformin had anti-tumorigenic effects in OC cell lines and the KpB OC pre-clinical mouse model, with increased efficacy in obese versus lean mice. Detected metabolic changes may underlie why ovarian tumors in obese mice have heightened susceptibility to metformin.
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    Polymorphisms in the Perilipin Gene May Affect Carcass Traits of Chinese Meat-type Chickens
    Zhang, Lu; Zhu, Qing; Liu, Yi-Ping; Gilbert, Elizabeth R.; Li, Diyan; Yin, Huadong; Wang, Yan; Yang, Zhiqin; Wang, Zhen; Yuan, Yuncong; Zhao, Xiaoling (Asian-Australasian Assoc Animal Production Soc, 2015-06-01)
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    Runtime Verification and Debugging of Concurrent Software
    Zhang, Lu (Virginia Tech, 2016-07-29)
    Our reliance on software has been growing fast over the past decades as the pervasive use of computer and software penetrated not only our daily life but also many critical applications. As the computational power of multi-core processors and other parallel hardware keeps increasing, concurrent software that exploit these parallel computing hardware become crucial for achieving high performance. However, developing correct and efficient concurrent software is a difficult task for programmers due to the inherent nondeterminism in their executions. As a result, concurrency related software bugs are among the most troublesome in practice and have caused severe problems in recent years. In this dissertation, I propose a series of new and fully automated methods for verifying and debugging concurrent software. They cover the detection, prevention, classification, and repair of some important types of bugs in the implementation of concurrent data structures and client-side web applications. These methods can be adopted at various stages of the software development life cycle, to help programmers write concurrent software correctly as well as efficiently.