Selected Preprints

• Identifying genomic features related to microbiome community composition using a sparse kernel RV coefficient.
  Song, H.*, Ma, N.*, Plantinga, A.M., Wu, M.C.

• New graph-based multi-sample tests for high-dimensional and non-Euclidean data.
  Song, H., Chen, H.   [R package: gTestsMulti]

• A fast and effective large-scale two-sample test based on kernels.
  Song, H., Chen, H.

Publications

• Practical and powerful kernel-based change-point detection.
  Song, H., Chen, H. (2024).
  IEEE Transactions on Signal Processing, 72:5174-5186.   [R package: kerSeg]

• Generalized kernel two-sample tests.
  Song, H., Chen, H. (2024).
  Biometrika, 111(3):755-770.   [R package: kerTests]

• Viral genomic variation and the severity of genital HSV-2 infection as quantified by shedding rate: a viral genome-wide association study.
  Casto, A.M., Song, H., Xie, H., Selke, S., Roychoudhury, P., Wu, M.C., Wald, A., Greninger, A.L., Johnston, C. (2024).
  Journal of Infectious Diseases, 230(6):1357-1366.

• Multivariate differential association analysis.
  Song, H., Wu, M.C. (2024).
  Stat, 13(2), e704.   [R package: kerDAA]

• Association between vaginal microbiota and vaginal inflammatory immune markers in postmenopause.
  Byrne, E.H., Song, H., Srinivasan, S., Fredricks, D.N., Reed, S.D., Guthrie, K., Wu, M.C.,
  Mitchell, C.M. (2024).
  Menopause: The Journal of The Menopause Society, 31(7):575-581.

• Limitation of permutation-based differential correlation analysis.
  Song, H., Wu, M.C. (2023).
  Genetic Epidemiology, 47(3):583-642.

• Accommodating multiple potential normalizations in microbiome associations studies.
  Song, H., Ling, W., Zhao, N., Plantinga, A.M., Broedlow, C.A., Klatt, N.R., Hensley-McBain, T.,
  Wu, M.C. (2023).
  BMC Bioinformatics, 24(1):22.

• A fast kernel independence test for cluster-correlated data.
  Song, H., Liu, H., Wu, M.C. (2022).
  Scientific Reports, 12(1):21659.

• Batch effects removal for microbiome data via conditional quantile regression.
  Ling, W., Lu, J., Zhao, N., Lulla, A., Plantinga, A., Fu, W., Zhang, A., Liu, H., Song, H., Li, Z.,
  Chen, J., Randolph, T., Koay, W., White, J., Launer, L., Fodor, A., Meyer, K., Wu, M.C. (2022).
  Nature Communications, 13(1):5418.

• Asymptotic distribution-free change-point detection for data with repeated observations.
  Song, H., Chen, H. (2022).
  Biometrika, 109(3):783-798.   [R package: gSeg]

• Preventing failures by dataset shift detection in safety-critical graph applications.
  Song, H., Thiagarajan, J., Kailkhura, B. (2021).
  Frontiers in Artificial Intelligence, 4:589632.