Wenpin Hou, PhD

  • Assistant Professor of Biostatistics

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Overview

As an Assistant Professor (tenure-track) in the Department of Biostatistics and an affiliated member of the Data Science Institute at Columbia University, Dr. Wenpin Hou is dedicated to advancing statistical and machine learning methods. Her research primarily focuses on single-cell genomics, epigenomics, and spatial transcriptomics. Dr. Hou is particularly interested in developing mathematical models of gene regulatory networks, and applying Generative Pre-trained Transformer (GPT) models to genomics, aiming to better understand gene regulation and spatial patterns of cells.

Dr. Hou's contributions to the field have been recognized with awards such as the Maximizing Investigators’ Research Award (MIRA) for Early Stage Investigators (R35) from NIH/NIGMS in September 2023 and the NIH Pathway to Independence Award (K99/R00) from NIH/NHGRI in March 2021. She collaborates across various disciplines, including cancer, immunology, and infectious diseases, and is contributing to the ENCODE4 consortium. Dr. Hou holds a Ph.D. in Mathematics from The University of Hong Kong, where she received University Postgraduate Fellowships and a Postgraduate Scholarship. Her postdoctoral training at Johns Hopkins University, mentored by Drs. Suchi Saria, Aravinda Chakravarti, Stephanie Hicks, Hongkai Ji, and Andy Feinberg, focused on developing computational methods for single-cell DNA methylation and spatial analysis. Dr. Hou's work continues to contribute to the fields of biostatistics and computational genomics.

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

Committees, Societies, Councils

  • Institute of Mathematical Statistics (IMS), Member
  • International Chinese Statistical Association (ICSA), Member
  • American Statistical Association (ASA), Member
  • Biostatistics Department, PhD Admission Committee, Member
  • Biostatistics Department, Application Qualifying Exams Committee, Member
  • Biostatistics Department, Curriculum Committee, Member

Honors & Awards

  • 2023: Maximizing Investigators Research Award (MIRA) for Early Stage Investigators, NIH/NIGMS
  • 2021: Emerging Leaders in Computational Oncology Award, Memorial Sloan Kettering Cancer Center 
  • 2021: NIH Pathway to Independence Award (K99/R00), NIH/NHGRI 
  • 2021: 2021 Women in Statistics and Data Science Conference Award, American Statistical Association 
  • 2013-2017: University Postgraduate Fellowships, Philip K H Wong Foundation, The University of Hong Kong
  • 2013-2017: Postgraduate Scholarship, The University of Hong Kong
  • 2016: Excellent Teaching Award, Department of Mathematics, The University of Hong Kong
  • 2016: Doris Chen Postgraduate Travel Grants 
 

Research

Computational methods for inferring single-cell DNA methylation and its spatial landscape
Genomic and epigenomic abnormalities collaborate in disease initiation and progression, as exempli?ed by frequent mutations in protein-coding genes which can vary spatially in separated sub-regions of tissues. This project will develop computational methods to infer DNA methylation landscape and develop novel statistical methods to identify differential epigenomic signals across patients or cell populations. 

Methods for inferring and analyzing gene regulatory networks using single-cell multiomics and spatial genomics data
How gene expression is regulated can inform the development of complex biological systems and help design treatment strategies for diseases by manipulating cell states; however, to accurately infer gene regulatory networks, we urgently need to develop robust computational methods that incorporate rich transcriptomics and epigenomics data measured in single cells. This research program will develop computational methods for inferring and analyzing gene regulatory networks using single-cell multiomics and spatial genomics data, and build network control strategies to manipulate networks to desired states (such as cellular healthy states). 

Research Interests

  • Biostatistical Methods
  • Chronic disease
  • Genetics
  • Genomics and Epigenomics

Grants

Present Grants

R35GM150887, NIH/NIGMS, 9/2023-8/2028
Title: Methods for inferring and analyzing gene regulatory networks using single-cell multiomics and spatial genomics data
Role: Principal Investigator

K99/R00HG011468, NIH/NHGRI, 3/2021-6/2025
Title: Computational methods for inferring single-cell DNA methylation and its spatial landscape
Role: Principal Investigator 

Selected Publications

Hou W, Ji Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat Methods. 2024 Mar 25;. doi: 10.1038/s41592-024-02235-4. [Epub ahead of print] PubMed PMID: 38528186.

Hou W, Ji Z, Chen Z, Wherry EJ, Hicks SC, Ji H. A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples. Nat Commun. 2023 Nov 10;14(1):7286. doi: 10.1038/s41467-023-42841-y. PubMed PMID: 37949861; PubMed Central PMCID: PMC10638410.

Hou W, Ji Z. Palo: spatially aware color palette optimization for single-cell and spatial data. Bioinformatics. 2022 Jul 11;38(14):3654-3656. doi: 10.1093/bioinformatics/btac368. PubMed PMID: 35642896; PubMed Central PMCID: PMC9272793.

Hou W, Ji Z. Unbiased visualization of single-cell genomic data with SCUBI. Cell Rep Methods. 2022 Jan 24;2(1). doi: 10.1016/j.crmeth.2021.100135. Epub 2022 Jan 4. PubMed PMID: 35224531; PubMed Central PMCID: PMC8871596.

Caushi JX, Zhang J, Ji Z, Vaghasia A, Zhang B, Hsiue EH, Mog BJ, Hou W, Justesen S, Blosser R, Tam A, Anagnostou V, Cottrell TR, Guo H, Chan HY, Singh D, Thapa S, Dykema AG, Burman P, Choudhury B, Aparicio L, Cheung LS, Lanis M, Belcaid Z, El Asmar M, Illei PB, Wang R, Meyers J, Schuebel K, Gupta A, Skaist A, Wheelan S, Naidoo J, Marrone KA, Brock M, Ha J, Bush EL, Park BJ, Bott M, Jones DR, Reuss JE, Velculescu VE, Chaft JE, Kinzler KW, Zhou S, Vogelstein B, Taube JM, Hellmann MD, Brahmer JR, Merghoub T, Forde PM, Yegnasubramanian S, Ji H, Pardoll DM, Smith KN. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature. 2021 Aug;596(7870):126-132. doi: 10.1038/s41586-021-03752-4. Epub 2021 Jul 21. PubMed PMID: 34290408; PubMed Central PMCID: PMC8338555.

Hou W, Ji Z, Ji H, Hicks SC. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biol. 2020 Aug 27;21(1):218. doi: 10.1186/s13059-020-02132-x. PubMed PMID: 32854757; PubMed Central PMCID: PMC7450705.

Ji Z, Zhou W, Hou W, Ji H. Single-cell ATAC-seq signal extraction and enhancement with SCATE. Genome Biol. 2020 Jul 3;21(1):161. doi: 10.1186/s13059-020-02075-3. PubMed PMID: 32620137; PubMed Central PMCID: PMC7333383.

Hou W, Ruan P, Ching WK, Akutsu T. On the number of driver nodes for controlling a Boolean network when the targets are restricted to attractors. J Theor Biol. 2019 Feb 21;463:1-11. doi: 10.1016/j.jtbi.2018.12.012. Epub 2018 Dec 11. PubMed PMID: 30543810.