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Kelly Stratton
Biostatistician

Kelly Stratton is an accomplished biostatistician with more than 10 years of experience in applying statistical methods to biological and environmental questions. She currently works in the Earth and Biological Sciences Directorate and leads the Data Transformations Integrated Research Platform at EMSL. 

Kelly's expertise lies in analysis of proteomics, lipidomics, and metabolomics measurements obtained through liquid chromatography or gas chromatography mass spectrometry (MS), nuclear magnetic resonance spectroscopy, and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), as well as RNA-Seq data. She has collaborated on numerous projects, both internally and externally funded, to design experiments, perform statistical analyses, and develop visualizations for biomarker studies, as well as to enhance understanding of host response to infection. In addition, Kelly has served as a statistical lead, contributor, and advisor on various projects, where she has developed analysis pipelines for biological data that are deployed in cloud computing infrastructures. She is a key developer of pmartR and related R packages. 

Apart from these technical areas, Kelly has also served as the project manager for the Pacific Northwest National Laboratory (PNNL) based testing and evaluation team for the Intelligence Advanced Research Projects Activity FELIX program, which aimed to support the advancement of biodetection and biosurveillance capabilities. She was also the principal investigator on a related project, where she worked with DNA sequence data (e.g., Nanopore, Illumina, PacBio). In recent years, Kelly has been collaborating with EMSL users to provide statistical analyses and data visualization solutions. Throughout her time at PNNL, Kelly has mentored many interns, from high school students to postdocs. She also teaches an intern-focused R programming series, demonstrating her commitment to developing the next generation of statisticians. 

Education

  • MS Biostatistics, University of Washington, 2012 
  • BS Mathematics, Washington State University, 2008 
  • BA Spanish, Washington State University, 2008 

Awards and Recognition

  • PNNL Laboratory Director’s Pathway to Excellence Award, 2022
  • DOE Secretary Honors Award, 2021
  • PNNL Outstanding Performance Award, 2020
  • PNNL Outstanding Performance Award, 2018

Affiliations and Professional Service

  • American Statistical Association
  • American Society for Mass Spectrometry, member since 2023

Publications

2023 

Bramer, L.M., et al., Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections. Heliyon, 2023. 9(3). DOI: 10.1016/j.heliyon.2023.e13795 

Couvillion, S.P., et al., Rapid remodeling of the soil lipidome in response to a drying-rewetting event. Microbiome, 2023. 11(1): p. 1-20. DOI: 10.1186/s40168-022-01427-4  

Degnan, D.J., et al., pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes. Journal of Proteome Research, 2023. DOI: 10.1021/acs.jproteome.2c00610 

2022 

Bramer, L., et al., Multi-omics Characterization of Neutrophil Extracellular Trap Formation in Severe and Mild COVID-19 Infections (preprint). 2022. DOI: 10.1016/j.heliyon.2023.e13795 

Eloe-Fadrosh, E.A., et al., The National Microbiome Data Collaborative Data Portal: an integrated multi-omics microbiome data resource. Nucleic Acids Research, 2022. 50(D1): p. D828-D836. DOI: 10.1093/nar/gkab990 

Kyle, J.E., et al., Simulated night-shift schedule disrupts the plasma lipidome and reveals early markers of cardiovascular disease risk. Nature and Science of Sleep, 2022. 14: p. 981. DOI: 10.2147/NSS.S363437 

2021 

Feng, S., et al., Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC bioinformatics, 2021. 22(1): p. 1-21. DOI: 10.1186/s12859-021-04197-2 

Kyle, J.E., et al., A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases. Scientific data, 2021. 8(1): p. 114. 

Myatt, L., et al., Placental Proteomics Reveals Sexually Dimorphic Adaptive Changes to Maternal Obesity and Gestational Diabetes. Placenta, 2021. 112: p. e10. DOI:10.1016/j.placenta.2021.07.033 

Thompson, A.M., et al., Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR‐MS) peak intensity normalization for complex mixture analyses. Rapid Communications in Mass Spectrometry, 2021. 35(9): p. e9068. 

Wang, L.-B., et al., Proteogenomic and metabolomic characterization of human glioblastoma. Cancer cell, 2021. 39(4): p. 509-528. e20. DOI: 10.1016/j.ccell.2021.01.006 

2020 

Bramer, L.M., et al., ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data. PLoS computational biology, 2020. 16(3): p. e1007654. DOI: 10.1371/journal.pcbi.1007654 

Leier, H.C., et al., A global lipid map defines a network essential for Zika virus replication. Nature communications, 2020. 11(1): p. 3652. 

Odenkirk, M.T., et al., From prevention to disease perturbations: a multi-omic assessment of exercise and myocardial infarctions. Biomolecules, 2020. 11(1): p. 40. DOI: 10.3390/biom11010040 

Odenkirk, M.T., et al., Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools. Molecular omics, 2020. 16(6): p. 521-532. DOI: 10.1039/d0mo00074d 

Piehowski, P.D., et al., Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Nature communications, 2020. 11(1): p. 8. DOI: 10.1038/s41467-019-13858-z 

2019 

Bramer, L.M., et al., P-Mart: interactive analysis of ion abundance global proteomics data. Journal of proteome research, 2019. 18(3): p. 1426-1432. 

Clair, G., et al., Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data. Bioinformatics, 2019. 35(21): p. 4507-4508. 

Kim, Y.-M., et al., Light-stress influences the composition of the murine gut microbiome, memory function, and plasma metabolome. Frontiers in Molecular Biosciences, 2019. 6: p. 108. 

Mitchell, H.D., et al., The role of EGFR in influenza pathogenicity: multiple network-based approaches to identify a key regulator of non-lethal infections. Frontiers in cell and developmental biology, 2019. 7: p. 200. 

Myatt, L., et al., Changes in Placental Lipidomics with Obesity and Gestational Diabetes: Sexual Dimorphism. Placenta, 2019. 83: p. e44-e45. DOI: 10.1016/j.placenta.2019.06.145 

Stratton, K.G., et al., pmartR: quality control and statistics for mass spectrometry-based biological data. Journal of proteome research, 2019. 18(3): p. 1418-1425. 

Webb-Robertson, B.-J.M., et al., Statistically driven metabolite and lipid profiling of patients from the undiagnosed diseases network. Analytical chemistry, 2019. 92(2): p. 1796-1803. 

2018 

Kedia, K., et al., Application of multiplexed ion mobility spectrometry towards the identification of host protein signatures of treatment effect in pulmonary tuberculosis. Tuberculosis, 2018. 112: p. 52-61. DOI: 10.1016/j.tube.2018.07.005 

Menachery, V.D., et al., MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape. Proceedings of the National Academy of Sciences, 2018. 115(5): p. E1012-E1021. DOI: 10.1073/pnas.1706928115 

Oláhová, M., et al., Biallelic mutations in ATP5F1D, which encodes a subunit of ATP synthase, cause a metabolic disorder. The American Journal of Human Genetics, 2018. 102(3): p. 494-504. DOI: 10.1016/j.ajhg.2018.01.020 

2017 

Burnum-Johnson, K.E., et al., MPLEx: a method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling. Analyst, 2017. 142(3): p. 442-448. DOI: 10.1039/c6an02486f 

Davie-Martin, C.L., et al., Implications of bioremediation of polycyclic aromatic hydrocarbon-contaminated soils for human health and cancer risk. Environmental science & technology, 2017. 51(17): p. 9458-9468. 

Eisfeld, A.J., et al., Multi-platform’omics analysis of human Ebola virus disease pathogenesis. Cell host & microbe, 2017. 22(6): p. 817-829. e8. DOI: 10.1016/j.chom.2017.10.011 

Kyle, J.E., et al., Comparing identified and statistically significant lipids and polar metabolites in 15‐year old serum and dried blood spot samples for longitudinal studies. Rapid communications in mass spectrometry, 2017. 31(5): p. 447-456. 

Menachery, V., et al., Middle East Respiratory syndrome coronavirus nonstructural protein 16 is necessary for interferon resistance and viral pathogenesis. mSphere. 2017; 2 (6): e00346-17. DOI. 10: p. 00346-17 

Menachery, V.D., et al., MERS-CoV accessory ORFs play key role for infection and pathogenesis. MBio, 2017. 8(4): p. e00665-17. DOI: https://doi.org/10.1128/mBio.00665-17 

Webb-Robertson, B.-J.M., et al., P-MartCancer–Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets. Cancer research, 2017. 77(21): p. e47-e50. DOI: 10.1158/0008-5472.CAN-17-0335 

2015 

Noonan, C.F. and K.G. Stratton. Improving scientific communication and publication output in a multidisciplinary laboratory: Changing culture through staff development workshops. in 2015 IEEE International Professional Communication Conference (IPCC). 2015. IEEE. 

2014 

Detterbeck, F.C., et al., The IASLC/ITMIG Thymic Epithelial Tumors Staging Project: proposal for an evidence-based stage classification system for the forthcoming (8th) edition of the TNM classification of malignant tumors. Journal of Thoracic Oncology, 2014. 9(9): p. S65-S72. DOI: 10.1097/JTO.0000000000000290 

Kondo, K., et al., The IASLC/ITMIG Thymic Epithelial Tumors Staging Project: proposals for the N and M components for the forthcoming (8th) edition of the TNM classification of malignant tumors. Journal of Thoracic Oncology, 2014. 9(9): p. S81-S87. DOI: 10.1097/JTO.0000000000000291 

Nicholson, A.G., et al., The IASLC/ITMIG Thymic Epithelial Tumors Staging Project: proposals for the T Component for the forthcoming (8th) edition of the TNM classification of malignant tumors. Journal of Thoracic Oncology, 2014. 9(9): p. S73-S80. DOI: 10.1097/JTO.0000000000000303 

Usmani, S., et al., Phase II study of pomalidomide in high-risk relapsed and refractory multiple myeloma. Leukemia, 2014. 28(12): p. 2413-2415. DOI: 10.1038/leu.2014.248 

2013 

Detterbeck, F.C., et al., The IASLC/ITMIG thymic malignancies staging project: development of a stage classification for thymic malignancies. Journal of Thoracic Oncology, 2013. 8(12): p. 1467-1473. DOI: 10.1097/JTO.0000000000000017 

Usmani, S.Z., et al., Final results of phase II study of pomalidomide (Pom) in GEP-defined high risk relapsed and refractory multiple myeloma (RRMM). Blood, 2013. 122(21): p. 3191. DOI: 10.1182/blood.V122.21.3191.3191 

2010 

Beagley, N., K.G. Stratton, and B.-J.M. Webb-Robertson, VIBE 2.0: visual integration for bayesian evaluation. Bioinformatics, 2010. 26(2): p. 280-282.