Data-Driven Alzheimer's Research Challenge (D2ARC) Final MiniConference (17 Aug 2022)
D2ARC Description: The goal of this research training experience was to expose mature students / researchers to the basics of the R programming language and related tools and techniques available for analyzing omics-scale single-cell RNA sequencing data. D2ARC focused on the application of basic data analysis techniques such as data visualization and manipulation for drawing biological insight from high-dimensional next generation sequencing datasets. A case study approach was used to provide immediate immersion in the research problem; participants focused on a high-dimensional single-cell RNAseq dataset generated by the researchers Bowles et al. with the Neural Stem Cell Institute in 2021, to study frontotemporal dementia and related tauopathies (like Alzheimer’s) in 3D brain organoid models. Students worked individually to conduct analyses of this dataset for prospective translation into real-world, publishable clinical disease research. As the final MiniConference demonstrated, students emerged with concrete, practical skills for conducting data science with R, as well as extracting knowledge and insight from single-cell RNAseq data.
See also:FTD MINDER: A Tool for Exploring scRNAseq Data from Frontotemporal Dementia Organoids https://inciteprojects.idea.rpi.edu/alzapp/app/alzapp/
Reference: Bowles, K.R. et al., ELAVL4, splicing and glutamatergic dysfunction precede neuron loss in MAPT-mutation cerebral organoids. Cell, July 26, 2021 DOI: https://doi.org/10.1016/j.cell.2021.07.003