Project Discovery of Novel Longitudinal Analysis Techniques on Microbiome Data Program Health & Human Sciences Term Academic Year 2024 Status Closed Research Area Microbiome; Statistical Analysis Description Microbiome data analysis poses significant challenges due to its inherent complexity, particularly in longitudinal studies. These challenges include sparsity, over-dispersion, compositionality, covariates, missing values, within-subject correlations, and more. Traditional statistical techniques often fall short in effectively handling such data intricacies. This project aims to address these challenges by investigating both established and novel longitudinal analysis techniques tailored to microbiome data. Supervisor Tzu-Wen L. Cross Mentor Tzu-Wen L. Cross Type of work required Synthetic data generation; Data analysis; Scientific write-up of results Websites Qualifications Proficiency in R programming language, previous experience in statistical analysis, particularly longitudinal data analysis, familiarity with microbiome data analysis techniques, and strong analytical and problem-solving skills. Credit Hours 7 Weekly Hours 12 (estimated)