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Articles by Jing Qiu
Total Records ( 2 ) for Jing Qiu
  William L. Franck , Woo- Suk Chang , Jing Qiu , Masayuki Sugawara , Michael J. Sadowsky , Stephanie A. Smith and Gary Stacey
  Bradyrhizobium japonicum is a facultative chemoautotroph capable of utilizing hydrogen gas as an electron donor in a respiratory chain terminated by oxygen to provide energy for cellular processes and carbon dioxide assimilation via a reductive pentose phosphate pathway. A transcriptomic analysis of B. japonicum cultured chemoautotrophically identified 1,485 transcripts, representing 17.5% of the genome, as differentially expressed when compared to heterotrophic cultures. Genetic determinants required for hydrogen utilization and carbon fixation, including the uptake hydrogenase system and components of the Calvin-Benson-Bassham cycle, were strongly induced in chemoautotrophically cultured cells. A putative isocitrate lyase (aceA; blr2455) was among the most strongly upregulated genes, suggesting a role for the glyoxylate cycle during chemoautotrophic growth. Addition of arabinose to chemoautotrophic cultures of B. japonicum did not significantly alter transcript profiles. Furthermore, a subset of nitrogen fixation genes was moderately induced during chemoautotrophic growth. In order to specifically address the role of isocitrate lyase and nitrogenase in chemoautotrophic growth, we cultured aceA, nifD, and nifH mutants under chemoautotrophic conditions. Growth of each mutant was similar to that of the wild type, indicating that the glyoxylate bypass and nitrogenase activity are not essential components of chemoautotrophy in B. japonicum.
  Jing Qiu and Xiangqin Cui
  Microarray technology is commonly used to identify differentially expressed (DE) genes across conditions. A related issue that has rarely been discussed but is equally important is to identify commonly expressed genes or constantly expressed genes across different organs, tissues, or species. A common practice in the literature for such studies is to apply the differential expression analysis and conclude that a gene is unchanged if there is no statistical evidence to conclude for differential expression. However, genes that are not statistically significantly DE could be (1) truly non-DE genes or (2) truly DE genes not detected by the statistical test of differential expression due to lack of power resulted from high noise level or lack of replication. Therefore, the practice of treating non-statistically significantly DE genes as non-DE genes has the risk of including genes that are truly DE without controlling such errors. We argue that if one wants to identify genes that are truly non-DE, one needs to show statistical evidence through valid statistical tests with the appropriate type I error rate control. In this paper, we consider the identification of non-DE genes through statistical equivalence tests under the framework of multiple testing. In particular, we consider the average equivalence criterion and study the power and false discovery rate (FDR) of the standard average equivalence test, the “two one-sided tests” (TOST), through extensive simulation studies based on real microarray data sets. We study the effects of various factors that can affect the power and FDR of the equivalence test including the proportion of non-DE genes. We also compare the ROC curves of the equivalence test with those of the naive method of selecting genes that are not statistically significant DE.
 
 
 
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