The proposal intends to advance both novel statistical methods for genomic research and discoveringcomplex molecular mechanisms on the teratoma initiation and progression after transplantation ofembryonic stem cells (ESCs). The biological aims are to investigate the mechanisms and biologic pathwaysof ESC-induced macrophage activation and teratoma development;and to unveil how the human MIFpolymorphisms associated with the serum MIF concentration in order to select patients for safe celltransplantation. To meet these challenges, high-throughput genomic tools and novel statistical methods willbe combined to enhance our understanding on the macrophage function on initiation and progression ofteratomas. The research will not only focus on identifying individual gene change on macrophages, but alsoanalyze underlying biologic pathways. In addition, time-course analysis in animal models will lead to identifygenes in different transition periods from early- to late-stage of teratoma development. These biologicalstudies prompt many new challenges in statistics. In particular, novel statistical techniques are proposed toanswer the following important questions: how to improve the power of existing tests for large-scale multipletesting problems with sparsity;how to detect whether genes have expressed over time course;how to usequantitative tools for discoveries of molecular mechanisms;how to control false discovery rate undergeneral dependence;what optimal detecting boundaries of signals are under arbitrary dependence;how toestimate residual variance in ultrahigh-dimensional regression. These topics have also importantimplications on compress sensing and computer security. In addition, several robust and effective methodsare proposed for ultrahigh-dimensional sparse regression, classification, and genetic networking. Thesenewly proposed methods will be applied to genome-wide association studies to identify single-nucleotidepolymorphisms associated with MIF expression in order to select patients for safe stem cell transplantation.An important feature of the proposal is the synthesis of PIs'extensive knowledge in statistics and cellbiology to gain better understanding of complicated biological problems.
The teratoma formation caused by ESCs can hamper clinical applications of stem cell based therapies. Theproposed research will advance our knowledge on the initiation and progression of teratomas after ESCtransplantation and lead to effective reduction of teratoma formation by inhibition of angiogenesis viamanipulating MIF expression and targeting M2 macrophages, making stem cell transplantation safe.
| Fan, Jianqing; Liu, Han; Wang, Weichen (2018) LARGE COVARIANCE ESTIMATION THROUGH ELLIPTICAL FACTOR MODELS. Ann Stat 46:1383-1414 |
| Chen, Zhao; Fan, Jianqing; Li, Runze (2018) Error Variance Estimation in Ultrahigh-Dimensional Additive Models. J Am Stat Assoc 113:315-327 |
| Li, Quefeng; Cheng, Guang; Fan, Jianqing et al. (2018) Embracing the Blessing of Dimensionality in Factor Models. J Am Stat Assoc 113:380-389 |
| Fan, Jianqing; Liu, Han; Sun, Qiang et al. (2018) I-LAMM FOR SPARSE LEARNING: SIMULTANEOUS CONTROL OF ALGORITHMIC COMPLEXITY AND STATISTICAL ERROR. Ann Stat 46:814-841 |
| Aït-Sahalia, Yacine; Fan, Jianqing; Laeven, Roger J A et al. (2017) Estimation of the Continuous and Discontinuous Leverage Effects. J Am Stat Assoc 112:1744-1758 |
| Wang, Weichen; Fan, Jianqing (2017) Asymptotics of empirical eigenstructure for high dimensional spiked covariance. Ann Stat 45:1342-1374 |
| Rolfe, Alyssa J; Bosco, Dale B; Broussard, Erynn N et al. (2017) In Vitro Phagocytosis of Myelin Debris by Bone Marrow-Derived Macrophages. J Vis Exp : |
| Fan, Jianqing; Han, Xu (2017) Estimation of the false discovery proportion with unknown dependence. J R Stat Soc Series B Stat Methodol 79:1143-1164 |
| Cheng, Zhijian; Bosco, Dale B; Sun, Li et al. (2017) Neural Stem Cell-Conditioned Medium Suppresses Inflammation and Promotes Spinal Cord Injury Recovery. Cell Transplant 26:469-482 |
| Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei (2017) Sufficient Forecasting Using Factor Models. J Econom 201:292-306 |
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