1841 Neil Ave.
280E Cunz Hall
Columbus, OH 43210
Background:
Dr. Song has a broad research interest in both methodological and collaborative research. He is interested in developing methods for statistical genetics and genomics, as well as methods for meta-analysis or integrative analysis of high-throughput data (e.g. ’omics data). He collaborated with physicians and biologists on various topics including cancer biology, mental health and reproductive medicine.
Research Interests:
Developing methods for statistical genetics and genomics and methods for meta-analysis or integrative analysis of high-throughput data
Education:
PhD, Biostatistics, University of Pittsburgh, 2012
MS, Biology, Tsinghua University, 2007
BS, Biology, Tsinghua University, 2004
Selected Publications:
Methodology
C. Song and H. Zhang. TARV: Tree-based analysis of rare variants identifying risk modifying variants in CTNNA2 and CNTNAP2 for alcohol addiction. Genetic Epidemiology, 38(6):552–559, 2014.
Y. Xu, Y.Wu, C. Song, and H. Zhang. Simulating realistic genomic data with rare variants. Genetic Epidemiology, 37(2):163–172, 2013.
C. Song and G. C. Tseng. Hypothesis setting and order statistic for robust genomic metaanalysis. The Annals of Applied Statistics, 8(2):777–800, 2014.
X.Wang, D. Kang, K. Shen, C. Song, S. Lu, L. C. Chang, S. G. Liao, Z. Huo, S. Tang, Y. Ding, et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics, 28(19):2534–2536, 2012.
Z. Mi, K. Shen, N. Song, C. Cheng, C. Song, N. Kaminski, and G.C. Tseng. Module-based prediction approach for robust inter-study predictions in microarray data. Bioinformatics, 26(20):2586, 2010.
S. Lu, J. Li, C. Song, K. Shen, and G.C. Tseng. Biomarker detection in the integration of multiple multi-class genomic studies. Bioinformatics, 26(3):333–340, 2010.
C. Cheng, K. Shen, C. Song, J. Luo, and G.C. Tseng. Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction. Bioinformatics, 25(13):1655, 2009.
F. Tian, H. Zhang, C. Song, Y. Xia, Y.Wu, and X. Liu. miRAS: a data processing system for miRNA expression profiling study. BMC Bioinformatics, 8:285, 2007.
L.A. Qiao, J. Zhu, Q. Liu, T. Zhu, C. Song,W. Lin, G.Wei, L. Mu, J. Tao, N. Zhao, et al. BOD: a customizable bioinformatics on demand system accommodating multiple steps and parallel tasks. Nucleic Acids Research, 32(14):4175–4181, 2004.
Collaboration
H. Zhang, D. A. Baldwin, R. K. Bukowski, S. Parry, Y. Xu, C. Song, W. W. Andrews, G. R. Saade, M. S. Esplin, Y. Sadovsky, U. M. Reddy, J. Ilekis, M. Varner, and J. R. Biggio for the Eunice Kennedy Shriver NICHD GPN-PBR. A genomewide association study of early spontaneous preterm delivery. Genetic Epidemiology, 39(3):217-226, 2015.
Y. P. Yu*, C. Song*, G. C. Tseng, B. G. Ren, W. LaFramboise, G. Michalopoulos, J. Nelson, and J. H. Luo. Genome abnormalities precede prostate cancer and predict clinical relapse. The American Journal of Pathology, 180(6):2240–2248, 2012. (* Equal contribution).
X. Wang, Y. Lin, C. Song, E. Sibille, and G. C. Tseng. Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: with application to major depressive disorder. BMC Bioinformatics, 13(1):1–15, 2012.
L.A. Niemeier, H. Kuffner Akatsu, C. Song, S.E. Carty, S.P. Hodak, L. Yip, R.L. Ferris, G.C. Tseng, R.R. Seethala, S.O. LeBeau, et al. A combined molecular-pathologic score improves risk stratification of thyroid papillary microcarcinoma. Cancer, 118(8):2069–2077, 2012.
W. Li, C. Song, D.J. Bailey, G.C. Tseng, J.J. Coon, and V.H.Wysocki. Statistical analysis of electron transfer dissociation pairwise fragmentation patterns. Analytical Chemistry, 83(24):9540–9545, 2011.
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