Chi Wang

Bioinformatics

Cancer Genomic Analysis

I am interested in developing bioinformatics methods to identify novel driver mutations and to delineate the temporal order of driver mutations during carcinogenesis.

Selected publications

  1. Liu S, Liu J, Xie Y, Zhai T, Hinderer EW, Stromberg AJ, Canderford NL, Kolesar JM, Moseley HNB, Chen L, Liu C, Wang C. MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations. Bioinformatics, btaa957, 2020.
  2. Wang M, Yu T, Liu J, Chen L, Stromberg AJ, Villano JL, Arnold SM, Liu C, Wang C. A probabilistic method for leveraging functional annotations to enhance estimation of the temporal order of pathway mutations during carcinogenesis. BMC bioinformatics, 20:620, 2019.

Transcriptomic Analysis

I am interested in developing new statsitical methods to compare gene expression profiles between groups of samples with different characteristics based on data generated from next-generation sequencing and NanoString nCounter.

Selected publications

  1. Wang H, Horbinski C, Wu H, Liu Y, Sheng S, Liu J, Weiss H, Stromberg A, Wang C. NanoStringDiff: A Novel Statistical Method for Differential Expression Analysis Based on NanoString nCounter Data. Nucleic Acids Research, 44(20): e151, 2016.
  2. Chen L, Wang C, Qin Z and Wu H. A novel statistical method for quantitative comparison of multiple ChIP-seq datasets. Bioinformatics, 31(12):1889-96, 2015.
  3. Wu H, Wang C and Wu Z. PROPER: Comprehensive Power Evaluation for Differential Expression using RNA-seq. Bioinformatics, 31(2):233-41, 2015.
  4. Wu H*, Wang C* and Wu Z. A New Shrinkage Estimator for Dispersion Improves Differential Expression Detection in RNA-seq. Biostatistics, 14(2):232-243, 2013. *Authors with equal contribution.

Proteomic/Metabolomic Analysis

I am interested in developing new statistical methods for identifying differentially abundant proteomic and metabolomic features between experimental groups based on mass spectrometry data. I am also interested in Bayesian kinetic modeling analysis to characterize the dynamic behavior of metabolic networks.

Selected publications

  1. Huang Z, Lane AN, Fan TW, Higashi RM, Weiss HL, Yin X, Wang C. Differential Abundance Analysis with Bayes Shrinkage Estimation of Variance (DASEV) for Zero-Inflated Proteomic and Metabolomic Data. Scientific Reports, 10(1):876, 2020.
  2. Li Y, Fan TWM, Lane AN, Kang WK, Arnold SM, Stromberg AJ, Wang C*, Chen Li*. SDA: A semi-parametric differential abundance analysis method for metabolomics and proteomics data. BMC Bioinformatics, 20(1):501, 2019. * Co-corresponding authors

Causal Inference

I am interested in developing novel statistical methods to estimate the causal effect of an exposure on an outcome with proper adjustment of confounding factors. We have developed a general Bayesian Adjustment of Confounding (BAC) method to estimate the average causal effect in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included.

Selected publications

  1. Wang C, Liu J and Fardo DW. Causal effect estimation in sequencing studies: A Bayesian method to account for confounder adjustment uncertainty. BMC Proceedings, 10(7): 411-415, 2016.
  2. Wang C, Parmigiani G, Dominici F and Zigler CM. Accounting for Uncertainty in Confounder and Effect Modifier Selection when Estimating Average Causal Effects in Generalized Linear Models. Biometrics, 71(3):654-65, 2015.
  3. Wang C, Parmigiani G and Dominici F. Bayesian Effect Estimation Accounting for Adjustment Uncertainty (with discussion). Biometrics, 68(3):661-71, 2012.
  4. Dominici F, Wang C, Crainiceanu C, Parmigiani P. Model Selection and Health Effect Estimation in Envionmental Epidemiology. Epidemiology 19:558-560, 2008.

Team Science

I am actively involved in cross-disciplinary, team-based cancer research. I have collaborated with investigators on a variety of basic science, translational, clinical and population-based studies for computational processing and customized bioinformatics analysis.

Selected publications

  1. Guo Y, Ye Q, Deng P, Cao Y, He D, Zhou Z, Wang C, Zaytseva YY, Schwartz CE, Lee EY, Mark Evers B, Morris AJ, Liu S, She QB. Spermine synthase and MYC cooperate to maintain colorectal cancer cell survival by repressing Bim expression. Nature Communications, 11(1):3243, 2020.
  2. Lin N, Liu J, Castle J, Wan J, Shendre A, Liu Y, Wang C, He C. Genome-wide DNA methylation profiling in human breast tissue by illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray. Epigenetics, 2020.
  3. Liu J, Murali T, Yu T, Liu C, Sivakumaran TA, Moseley HNB, Zhulin IB, Weiss HL, Durbin EB, Ellingson SR, Liu J, Huang B, Hallahan BJ, Horbinski CM, Hodges K, Napier DL, Bocklage T, Mueller J, Vanderford NL, Fardo DW, Wang C* and Arnold SM*. Characterization of Squamous Cell Lung Cancers from Appalachian Kentucky. Cancer Epidemiology, Biomarkers & Prevention, 28(2):348-356, 2019. *Co-corresponding authors