Research

Preprints

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• Song, H., Wu, M.C.
  Limitation of permutation-based differential correlation analysis.

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

• Song, H., Chen, H.
  Practical and powerful kernel-based change-point detection.
  arXiv   [R package: kerSeg]

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

• Song, H., Chen, H.
  Generalized kernel two-sample tests.
  arXiv   [R package: kerTests]

Publications

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

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

• 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).
  Batch effects removal for microbiome data via conditional quantile regression.
  Nature Communications, 13(1):5418.

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

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