Podcasts and Videos
Seemingly everyone has been talking about the impact of OpenAI's ChatGPT on, well, everything, including writing code. In this very special RPIrates we'll talk specifically about generating great, and sometimes not-so-great, R code based on OpenAI's "Codex" models, which are easily accessed via the OpenAI API with the help of RStudio extensions (addins) provided by the GPTStudio package.
Please join us this Tuesday (31 Jan, 6p) in AE 217 as IDEA PhD student Xiao Shou leads us in a discussion of his work, "Learning and Inference from Temporal Event Sequences."
Michael Liu takes us on a tour of the Floating Solar Explorer, an interactive, GIS-based R Shiny web app that visualizes water reservoirs alongside economic data and political boundaries. Along with the website itself, the presentation explores certain aspects of the data generation process using R and provide an introduction to R Shiny.
Michael gives us a brief overview of the City of Cohoes Floating Solar project and the role the Floating Solar Explorer played in winning important grants, and then demonstrates and describes the structure of the app, including some of the performance challenges imposed by the size of the datasets. If you're interested in Shiny apps in general or apps that utilize geo-based data in particular, this is the RPIrates for you!
Find the Floating Solar Explorer at: https://bit.ly/floating_solar
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
Data Analytics Research (Fall 2021) students present their work studying AAVE, a DeFi (decentralized finance) protocol.
Data Analytics Research (Fall 2021) students present their work on the Eat4Genes app