Cong Ma, PhD
Assistant Professor of Computational Medicine and Bioinformatics
congma@umich.edu

Available to mentor

Cong Ma, PhD
Assistant Professor
  • About
  • Links
  • Qualifications
  • Research Overview
  • Recent Publications
  • About

    Professor Cong Ma develops mathematical, statistical, and machine learning models to study the spatial heterogeneity and organization of cell types including genetic and epigenetic states, in a variety of tissues, particularly in cancer tissues. Ma’s computational models can be applied to different types of tissues, either for foundational biology investigation or clinical research.

    Her work includes accurately identifying the tissue geometries and the associated gradient of epigenetic variation. These computational models bring insights into disease mechanisms and could lead to the discovery of diagnostic biomarkers. For example, in cancers, her computational analysis shows how tumorous tissues organization evolves in space and behavior, which can inform potential treatments.

    Links
    • https://sites.google.com/view/congmalab/home
    Qualifications
    • Postdoctoral Research Associate
      Princeton University, Department of Computer Science, 2024
    • PhD
      Carnegie Mellon University, Pittsburgh, 2020
    • BS
      Zhejiang University of Science and Technology, Hangzhou, 2015
    Research Overview

    Spatial transcriptomics

    Recent Publications See All Publications
    • Journal Article
      Epigenetic regulation during cancer transitions across 11 tumour types.
      Terekhanova NV, Karpova A, Liang W-W, Strzalkowski A, Chen S, Li Y, Southard-Smith AN, Iglesia MD, Wendl MC, Jayasinghe RG, Liu J, Song Y, Cao S, Houston A, Liu X, Wyczalkowski MA, Lu RJ-H, Caravan W, Shinkle A, Naser Al Deen N, Herndon JM, Mudd J, Ma C, Sarkar H, Sato K, Ibrahim OM, Mo C-K, Chasnoff SE, Porta-Pardo E, Held JM, Pachynski R, Schwarz JK, Gillanders WE, Kim AH, Vij R, DiPersio JF, Puram SV, Chheda MG, Fuh KC, DeNardo DG, Fields RC, Chen F, Raphael BJ, Ding L. Nature, 2023 Nov; 623 (7986): 432 - 441. DOI:10.1038/s41586-023-06682-5
      PMID: 37914932
    • Journal Article
      Spatial epigenome-transcriptome co-profiling of mammalian tissues.
      Zhang D, Deng Y, Kukanja P, Agirre E, Bartosovic M, Dong M, Ma C, Ma S, Su G, Bao S, Liu Y, Xiao Y, Rosoklija GB, Dwork AJ, Mann JJ, Leong KW, Boldrini M, Wang L, Haeussler M, Raphael BJ, Kluger Y, Castelo-Branco G, Fan R. Nature, 2023 Apr; 616 (7955): 113 - 122. DOI:10.1038/s41586-023-05795-1
      PMID: 36922587
    • Journal Article
      Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics.
      Ma C, Chitra U, Zhang S, Raphael BJ. Cell Syst, 2022 Oct 19; 13 (10): 786 - 797.e13. DOI:10.1016/j.cels.2022.09.002
      PMID: 36265465
    • Journal Article
      Deriving Ranges of Optimal Estimated Transcript Expression due to Nonidentifiability.
      Zheng H, Ma C, Kingsford C. J Comput Biol, 2022 Feb; 29 (2): 121 - 139. DOI:10.1089/cmb.2021.0444
      PMID: 35041494
    • Journal Article
      Exact transcript quantification over splice graphs.
      Ma C, Zheng H, Kingsford C. Algorithms Mol Biol, 2021 May 10; 16 (1): 5 DOI:10.1186/s13015-021-00184-7
      PMID: 33971903
    • Journal Article
      Detecting transcriptomic structural variants in heterogeneous contexts via the Multiple Compatible Arrangements Problem.
      Qiu Y, Ma C, Xie H, Kingsford C. Algorithms Mol Biol, 2020 15: 9 DOI:10.1186/s13015-020-00170-5
      PMID: 32467720
    • Journal Article
      Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification.
      Ma C, Kingsford C. Cell Syst, 2019 Dec 18; 9 (6): 589 - 599.e7. DOI:10.1016/j.cels.2019.10.005
      PMID: 31786209
    • Journal Article
      SQUID: transcriptomic structural variation detection from RNA-seq.
      Ma C, Shao M, Kingsford C. Genome Biol, 2018 Apr 12; 19 (1): 52 DOI:10.1186/s13059-018-1421-5
      PMID: 29650026