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We are pleased to announce the promotion of Onur M. Orun to principal biostatistician, effective October 1. Orun earned his bachelor's degree in mathematics from Izmir University of Economics in Turkey, followed by a MS in math from Bilkent University (Ankara, Turkey) and a MS in statistics from Colorado State University, with his master's project focusing on alumni association membership retention at CSU. At Vanderbilt University Medical Center since 2019, Orun has collaborated primarily with Vanderbilt's Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, playing a lead role in creating statistical analysis plans and high-quality, reproducible reports for randomized controlled trials and observational studies, with first-authorship of "Statistical analysis plan for the Surgery for Cancer with Option of Palliative Care Expert (SCOPE) trial: A randomized controlled trial of a specialist palliative care intervention for patients undergoing surgery for cancer" (Trials 2021). Orun has co-authored nine other peer-reviewed publications to date, in high-impact journals such as Lancet: Respiratory Medicine, JAMA Surgery, Chest, and New England Journal of Medicine, and won our department's Linda Stewart Applied Analysis Report Award twice, in 2019 for "Vitamin D to Improve Outcomes by Leveraging Early Treatment: Long-term Brain Outcomes in Vitamin D Deficient Patients (VIOLET-BUD)" and in 2023 for "BRAIN-ICU Long-term Outcomes Latent Trajectory Analysis Report." Away from his computer, his interests include foodie adventures and hiking. Click his name to view his staff profile.

The Statistical Computing Series hosted by Vanderbilt University Medical Center's Department of Biostatistics features presentations on the implementation of statistical models and methods, statistical computation, and graphics. These informal meetings allow experienced statisticians and developers to share their expertise on computing topics with practitioners across Vanderbilt. On Monday, November 25, 2024, at 1:30pm on Teams, principal biostatistician Josh DeClercq will present "Mealtime: A Shiny app for meal planning." Here is his description of the talk:

Have you ever come home from the grocery store and realize you have no idea what to cook? Do you have stacks of cookbooks that largely go unused? Mealtime is a data-driven approach to meal planning. This presentation will touch on how to deconstruct a recipe into its essential components to derive its "makeability" score. I'll explain how I scale this process to accommodate large numbers of recipes and ingredients and discuss how I leverage data to accommodate dietary restrictions. Finally, I will demonstrate how it all comes together in a Shiny application, sharing some tips and challenges I encountered along the way.

For access to this webinar, contact series organizer Ryan Moore.

Congratulations to former visiting student Chia-Jung "Charlene" Chang (PhD candidate in biomedical engineering at National Cheng Kung University), research assistant professor Chih-Yuan Hsu, and professors Qi Liu and Yu Shyr on the publication of "VICTOR: Validation and inspection of cell type annotation through optimal regression." The article on this new method appeared online ahead of print on October 15 and will go to press as part of Computational and Structural Biotechnology Journal's December issue.  As described in the paper's abstract:

Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

Figure 1 in the paper provides an " One example in diagnosing the reliability of cell annotations. a) diagnostic performance of singleR, scmap, SCINA, scPred, CHETAH, scClassify, and Seurat. b) diagnostic performance of VICTOR when applied to annotations from singleR, scmap, SCINA, scPred, CHETAH, scClassify, and Seurat.