Honors & Awards


  • SSPA Best Poster/Presentation Award, JSM 2016, ASA, Section for Programmers and Analysts (August 2016)
  • Fuller Graduate Scholarship, Department of Mathematics and Statistics, Texas Tech University (2015-2016)
  • John White Graduate Scholarship, Department of Mathematics and Statistics, Texas Tech University (2014 -2016)
  • TEACH (Teaching Effectiveness And Career enHancement) Fellowship, Teaching, Learning, & Professional Development Center, Texas Tech University (2014-2015)
  • Graduate Student Scholarship, SIAM at Texas Tech University (2013-2014)
  • Summer Research Thesis Award, Graduate school, Texas Tech University (June 2013)
  • Award for Academic Excellence, University of Peradeniya, Sri Lanka (2010)

Professional Education


  • Doctor of Philosophy, Texas Tech University (2016)
  • Master of Science, Texas Tech University (2013)
  • Bachelor of Science, University Of Peradeniya, Sri Lanka (2011)

Stanford Advisors


Current Research and Scholarly Interests


Statistical methods for longitudinal human microbiome data

All Publications


  • Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment PLOS Computational Biology Fukuyama, J., Rumker, L., Sankaran, K., Jeganathan, P., Relman, D. A., Holmes, S. P. 2017; 13 (8): e1005706

    Abstract

    Our work focuses on the stability, resilience, and response to perturbation of the bacterial communities in the human gut. Informative flash flood-like disturbances that eliminate most gastrointestinal biomass can be induced using a clinically-relevant iso-osmotic agent. We designed and executed such a disturbance in human volunteers using a dense longitudinal sampling scheme extending before and after induced diarrhea. This experiment has enabled a careful multidomain analysis of a controlled perturbation of the human gut microbiota with a new level of resolution. These new longitudinal multidomain data were analyzed using recently developed statistical methods that demonstrate improvements over current practices. By imposing sparsity constraints we have enhanced the interpretability of the analyses and by employing a new adaptive generalized principal components analysis, incorporated modulated phylogenetic information and enhanced interpretation through scoring of the portions of the tree most influenced by the perturbation. Our analyses leverage the taxa-sample duality in the data to show how the gut microbiota recovers following this perturbation. Through a holistic approach that integrates phylogenetic, metagenomic and abundance information, we elucidate patterns of taxonomic and functional change that characterize the community recovery process across individuals. We provide complete code and illustrations of new sparse statistical methods for high-dimensional, longitudinal multidomain data that provide greater interpretability than existing methods.

    View details for DOI 10.1371/journal.pcbi.1005706

    View details for PubMedCentralID PMC5576755

  • Hematological Effects, Serum, and Pulmonary Cytokine Profiles in a Melanoma Mouse Model Treated with GK1 CANCER BIOTHERAPY AND RADIOPHARMACEUTICALS Perez-Torres, A., Vera-Aguilera, J., Sahaza, J. H., Vera-Aguilera, C., Moreno-Aguilera, E., Pulido-Camarillo, E., Nunez-Ochoa, L., Jeganathan, P. 2015; 30 (6): 247-254

    Abstract

    In a previous study, we demonstrated the therapeutic efficacy of a subcutaneous injection of GK1 peptide in a melanoma mouse model, effectively increasing the mean survival time by 42.58%, delaying tumor growth, and increasing intratumoral necrosis compared with the control. As a first approach to investigate the anti-melanoma effect of GK1, this study was carried out to determine the hematological effects along with both serum and lung cytokine profiles in a melanoma lung metastatic model.Thirteen C57BL6 female mice were transfected in the lateral tail vein with 2×10(5) B16-F0 melanoma cells. After 7 days, mice were separated in two different groups and treatments were initiated (day 0): The GK1-treated group (seven mice) were injected every 5 days intravenously with GK1 (10 μg) in the lateral tail vein, and the control group (six mice) were injected every 5 days with intravenous saline solution. Blood samples were collected every 5 days from day 0; tumor samples were obtained for cytokine measurements on the day of sacrifice.In the peripheral blood, mice treated with GK1 presented a statistically significant decrease in IFN-γ (p<0.05), and lymphocytes tended to be lower compared with the control mice (p=0.06). Lung metastatic analysis demonstrated a significant increase in IFN-γ and IL-12p70 (p<0.05); a significant decrease in IL-17, IL-4, IL-22, IL-23, and IL-12p40 (p<0.05); and a marginal decrease in IL-1β (p=0.07) compared with the control.Our results suggest that an intratumoral increase of cytokines with antitumor activity along with an intratumoral decrease of cytokines with protumor activity could explain, in part, the anti-melanoma effects of GK1 in a lung metastatic melanoma mouse model. Further studies must be performed to elucidate the precise mechanisms of action for GK1 peptide against melanoma, and their eventual application in humans.

    View details for DOI 10.1089/cbr.2015.1835

    View details for Web of Science ID 000363904000003

    View details for PubMedID 26181852

  • Saddlepoint-based bootstrap inference for the spatial dependence parameter in the lattice process SPATIAL STATISTICS Jeganathan, P., Paige, R. L., Trindade, A. A. 2015; 12: 1-14