Presentation by Dr. Ryan Huebinger
Associate Professor of Emergency Medicine
University of New Mexico Health Sciences Center

Real-world datasets are power tools for conducting low-cost or free research and generating preliminary data for grant applications. In this lecture, Dr. Huebinger covers different types of real-world data in addition to their strengths and weakness. He will then use an example from his own research to show how these data can be used to address research questions in novel ways.

Click the link below to register and access the Zoom session.

Mindy Hébert-DeRouen, PhD, MPH
Assistant Professor Department of Public Health Sciences
College of Health, Education, and Social Transformation New Mexico State University

Use of EHR-based cohorts for research requires informed extraction, harmonization, and operationalization of EHR data. Dr. Hebert-DeRouen will describe the development of an EHR-based cohort with data from three healthcare systems linked to population-based cancer registry data to examine clinical and neighborhood factors that contribute to racial and ethnic disparities in liver cancer. She will detail operationalization of detailed race/ethnicity (17 categories) and liver cancer risk factors from EHR data; illustrate the utility of linking EHR data to cancer registry data; and present results on racial/ethnic disparities in liver cancer as well as the relative prevalence and contribution of risk factors to liver cancer diagnosis across racial/ethnic groups.

Click the link below to register and access the Zoom session.

Analysis of gene expression changes in response to different treatments or conditions can yield important biological insights, including elucidating abiotic and biotic stress responses, uncovering disease and resistance mechanisms, and highlighting developmental changes. In this workshop you will learn how to perform differential gene expression, visualization, and pathway analysis. In addition, you will learn how to perform coexpression analysis and identify genes driving expression shifts.

Application Due by October 13, 2025

A typical RNA sequencing experiment captures transcriptional changes at the bulk tissue level representing a heterogenous mixture of cells and providing an aggregated expression result. By performing RNA sequencing on isolated single cells, one can pinpoint cellular expression and consider transcriptome sequences from individual cells to investigate, for example, cell lineage relationships, variability between cells and how a cell functions in its local environment. In this workshop, you will import single cell data, and cover topics such as normalization, quality control, dimensionality reduction, visualization, and data interpretation.

Application Due by October 20, 2025