Siyuan Ma Assistant Professor at Vanderbilt University Medical Center

Publications

[10] Ma, S., Ren, B., Mallick, H., et al. (2021), A statistical model for describing and simulating microbial community profiles. PLOS Computational Biology

[9] Kondo, A., Ma, S., Lee, M.M.Y., et al. (2021), Highly multiplexed image analysis of intestinal tissue sections in patients with inflammatory bowel disease. Gastroenterology

[8] Mallick, H., Rahnavard, A., McIver, L.J., Ma, S., et al. (2021), Multivariable Association Discovery in Population-scale Meta-omics Studies. PLOS Computational Biology

[7] Dantzler, K., Ma, S., Ngotho, P., et al. (2019), Naturally acquired immunity against immature Plasmodium falciparum gametocytes. Science Translational Medicine

[6] Ma, S.*, Ogino, S.*, Parsana, P., et al. (2018), Continuity of transcriptomes among colorectal cancer subtypes, based on meta-analys. Genome Biology

[5] Obaldia, N., Meibalan, E., Sa, J. M., Ma, S., et al. (2018), Bone marrow is a major parasite reservoir in Plasmodium vivax infection. MBio

[4] De Niz, M., Meibalan, E., Mejia, P., Ma, S., et al. (2018), Plasmodium gametocytes display homing and vascular transmigration in the host bone marrow. Science advances

[3] Mallick, H.*, Ma, S.*, Franzosa, E.A., et al. (2017). Experimental design and quantitative analysis of microbial community multi’omics. Genome Biology

[2] Sinha, R., Abu-Ali, G., Vogtmann, E., Fodor, A.A., Ren, B., Amir, A., Schwager, E., Crabtree, J., Ma, S., et al (2017), Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium, Nature Biotechnology

[1] Pelle, K.G., Oh, K., Buchholz, K., Narasimhan, V., Joice, R., Milner, D.A., Brancucci, N.M.B., Ma, S., et al (2015), Transcriptional profiling defines dynamics of parasite tissue sequestration during malaria infection, Genome Medicine

Working papers

[4] Ma, S., Li, H., A tensor decomposition model for longitudinal microbiome studies, in review

[3] Ma, S., Shungin, D., Mallick, H., et al., Population structure discovery in meta-analyzed microbial mommunities and inflammatory bowel disease, in review

[2] Ma, S., Li, H., Flexible Ising modeling for co-colonization in longitudinal microbiomes, in preparation

[1] Ma, S., Huttenhower, C., Janson, L., A flexible framework for novel health-microbiome associations and controlling false discoveries, in preparation

Invited talks

[5] A Statistical Model for Simulating and Testing for Microbiomes, Biostatistics Seminar Series, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania. Philadelphia, PA, 2021

[4] Testing Cell-Cell Interactions in Imaging Mass Cytometry Data, IMC Working Group Meeting, Perelman School of Medicine, University of Pennsylvania. Philadelphia, PA, 2021

[3] Meta-Analysis of Population Structure in the IBD Gut Microbiome, Microbiome Working Group Meeting, Harvard Chan Microbiome in Public Health Center. Boston, MA, 2019

[2] Meta-Analysis of Population Heterogeneity in IBD Patients’ Gut Microbiome, CMIT Work-in-Progress Meeting, Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology. Cambridge, MA, 2017

[1] Subtype Continuity Revealed by Meta-analysis of the Colorectal Cancer Transcriptome, Department of Biostatistics Genomics Meeting, Dana-Farber Cancer Institute. Boston, MA, 2015

Conference presentations

[5] Tensor Decomposition of Longitudinal Microbiomes, Joint Statistical Meetings. Virtual Conference, 2021

[4] Tensor Decomposition of Longitudinal Microbiomes, ENAR Spring Meeting. Virtual Conference, 2021

[3] SparseDOSSA: A Statistical Model for Simulating Realistic Microbial Community Profiles, Joint Statistical Meetings. Virtual Conference, 2020

[2] Population Structure Discovery in Meta-Analyzed Microbial Communities, Confernece on Intelligent Systems for Molecular Biology. Chicago, Il, 2018

[1] Population Structure Discovery in Meta-Analyzed Microbial Communities, Joint Statistical Meetings. Baltimore, MD, 2017

Softwares

[2] SparseDOSSA 2: A statistical model for describing and simulating microbial community profiles

[1] MMUPHin: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies