Discovery of Novel Longitudinal Analysis Techniques on Microbiome Data Health & Human Sciences Academic Year 2024 Closed Microbiome; Statistical Analysis 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. Tzu-Wen L. Cross Tzu-Wen L. Cross Synthetic data generation; Data analysis; Scientific write-up of results 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. 7 12 (estimated)
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