R Shiny App for Streamlined Transcriptomics: A Hands-On User Journey in Microarray Analysis
“My most enjoyable moments were when tasks/the project in general began to function how I hoped. For example, after a two or so weeks of the project, working with Sam and Nikita for setting up the project’s outline the initial chaos of the project began to settle as participants got a better understanding of our goal. Seeing things beginning to run smoothly was very rewarding as a project management lead. Similarly, when I got R Shiny code that I worked on for a while to work (e.g., my volcano plot), I felt overjoyed.”
Milestones
Operational Excellence: Spearheaded project management, while actively contributing to backend development and the overall project architecture.
Leadership Recognition: Awarded for outstanding leadership in project management across the pathway.
Soft Skills and Leadership
Project Planning, Team Leadership, Employee Onboarding, Feedback Synthesis, Team Collaboration, Technical Presentations, Self-Evaluation, Time Management, Problem-Solving
Technical Capabilities
R Shiny Framework: Mastery in Reactive Inputs & Outputs
Bioinformatics Technologies:
Quality Control ( RLE Boxplots, NUSE Boxplots )
Data Visualization ( PCA, Heatmaps, Volcano Plots )
Differential Gene Expression Analysis ( Annotation, Gene Filtering, Limma Analysis, Volcano Plots )
Functional Analysis ( GO, KEGG, STRING Database )
Toolkit Proficiency
GitHub, R Shiny, R Studio, Trello, GSuite, Airtable, Typeform
Project Insights
About sMAP: Developed to demystify the complexities of a standard transcriptomics pipeline, sMAP presents an interactive interface allowing users to upload GEO datasets. They can then engage in quality control, statistical analysis, and functional scrutiny to unearth potential biomarkers.
Team Dynamics: The project was conceived and executed through STEM-Away’s virtual internship platform. Our multi-faceted team, categorized into five specialized units, took charge of distinct roles—from coding the transcriptomics pipeline and integrating it into the R Shiny app, to GitHub management and documentation.
