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Articles by C Cheng
Total Records ( 3 ) for C Cheng
  C Cheng , K Shen , C Song , J Luo and G. C. Tseng
 

Motivation: Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored.

Results: In this article, we investigate the effect of gene-wise normalization in microarray inter-study prediction. Gene-specific intensity discrepancies across studies are commonly found even after proper sample-wise normalization. We explore the rationale and necessity of gene-wise normalization. We also show that the ratio of sample sizes in normal versus diseased groups can greatly affect the performance of gene-wise normalization and an analytical method is developed to adjust for the imbalanced ratio effect. Both simulation results and applications to three lung cancer and two prostate cancer data sets, considering both binary classification and survival risk predictions, showed significant and robust improvement of the new adjustment. A calibration scheme is developed to apply the ratio-adjusted gene-wise normalization for prospective clinical trials. The number of calibration samples needed is estimated from existing studies and suggested for future applications. The result has important implication to the translational research of microarray as a practical disease diagnosis and prognosis prediction tool.

  S Pounds , C Cheng , X Cao , K. R Crews , W Plunkett , V Gandhi , J Rubnitz , R. C Ribeiro , J. R Downing and J. Lamba
 

Motivation: In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables.

Results: Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis.

  X Kong , H Gan , Y Hao , C Cheng , J Jiang , Y Hong , J Yang , H Zhu , Y Chi , X Yun and J. Gu
 

CDK11p58, a CDK11 family Ser/Thr kinase, is a G2/M specific protein and contributed to regulation of cell cycle, transcription and apoptotic signal transduction. Recently, CDK11p58 has been reported to exert important functions in mitotic process, such as the regulation of bipolar spindle formation and sister chromatid cohesion. Here, we identified p21 activated kinase 1 (PAK1) as a new CDK11p58 substrate and we mapped a new phosphorylation site of Ser174 on PAK1. By mutagenesis, we created PAK1174A and PAK1174E, which mimic the dephosphorylated and phosphorylated form of PAK1; further analysis showed PAK1174E could be recruited to myosin V motor complex through binding to dynein light chain 2 (DLC2). PAK1174E could accelerate the mitosis progression in a nocodazole blocked cell model, while PAK1174A exhibited an opposite role. Our results indicated PAK1 may serve as a downstream effector of CDK11p58 during mitosis progression.

 
 
 
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