Wenpin Hou, PhD

  • Assistant Professor of Biostatistics

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Overview

Dr. Hou's research focuses on developing novel statistical machine learning methods to tackle the challenges in the intersection of statistics and data science (data in single-cell genomics, epigenomics, and spatial transcriptomics), mathematical modeling of gene regulatory networks, and ultimately understanding gene regulatory mechanisms. Dr. Hou is particularly interested in developing methods for modeling the spatio-temporal patterns in single-cell and spatial genomics and epigenomics data using Bayesian framework with functional data analysis. Dr. Hou actively collaborates in various aspects of cancer, immunology, infectious disease, developmental processes, obesity, maternal and child health, and health disparity. The outcomes are expected to lead to a deeper understanding of regulatory mechanisms and new targeted therapy for various diseases that help the broad community.

Academic Appointments

  • Assistant Professor of Biostatistics

Credentials & Experience

Education & Training

  • BS, 2013 Sun Yat-sen University
  • PhD, 2017 The University of Hong Kong
  • Fellowship: 2022 Johns Hopkins University

Research

Research Interests

  • Biostatistical Methods
  • Chronic disease
  • Genetics

Selected Publications

Hou, W. and Ji, Z. 2023. Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis. Preprint in bioRxiv. Software: GPTCelltype. PMID: 37205379. PMCID: PMC10187429. (In Journal Revision)

Hou, W., Ji, Z., Chen, Z., Wherry, E.J., Hicks, S.C. and Ji, H. 2023. A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples. Nature Communications 14, 7286. Software: Lamian.

Hou, W. and Ji, Z. 2022. Decomposing spatial heterogeneity of cell trajectories with Paella. Preprint in bioRxiv. Software: Paella. PMID/PMCID to be announced. (In Journal Review)

Hou, W. and Ji, Z., 2022. Single-cell unbiased visualization with SCUBI. Cell Reports Methods, 100135, 2022. Software: SCUBI. PMID: 35224531. PMCID: PMC8871596.

Hou, W., Ji, Z., Ji, H. and Hicks, S.C., 2020. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biology, 21, 218. PMID: 32854757. PMCID: PMC7450705.

Ji, Z., Zhou, W., Hou, W. and Ji, H., 2020. SCATE: single-cell ATAC-seq signal extraction and enhancement. Genome Biology, 21,161. PMID: 32620137. PMCID: PMC7333383. Software: SCATE. SCATEData.