Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
Zeya Wang,
Shaolong Cao,
Jeffrey S. Morris,
Jaeil Ahn,
Rongjie Liu,
Svitlana Tyekucheva,
Fan Gao,
Bo Li,
Wei Lu,
Ximing Tang,
Ignacio I. Wistuba,
Michaela Bowden,
Lorelei Mucci,
Massimo Loda,
Giovanni Parmigiani,
Chris C. Holmes,
Wenyi Wang
Affiliations
Zeya Wang
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Statistics, Rice University, Houston, TX 77005, USA
Shaolong Cao
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Jeffrey S. Morris
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Jaeil Ahn
Department of Biostatistics and Bioinformatics, Georgetown University, Washington, DC 20057, USA
Rongjie Liu
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Svitlana Tyekucheva
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Fan Gao
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Statistics, Rice University, Houston, TX 77005, USA
Bo Li
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Statistics, Harvard University, Cambridge, MA 02138, USA
Wei Lu
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Ximing Tang
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Ignacio I. Wistuba
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Michaela Bowden
Department of Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA 02215, USA
Lorelei Mucci
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Massimo Loda
Department of Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
Giovanni Parmigiani
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Chris C. Holmes
Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
Wenyi Wang
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Corresponding author
Summary: Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials. : Computational Bioinformatics; Cancer; Transcriptomics Subject Areas: Computational Bioinformatics, Cancer, Transcriptomics
