Data Analysis Skills
Survival Analyses - Association of features with sample level information such as outcomes, with and without repeated measures
Functional Data Analyses - Associate points for a range of x-values with sample level information
High Throughput Sequencing - Quality control, alignment, and feature extraction of RNA and DNA sequencing
Data Processing Pipeline Construction - Create pipelines with Conda environments for reproducible large-scale sequence processing
Somatic Mutation Analyses - Using called mutations, identify high confidence alterations and characterize mutational burden
Copy Number Analyses - Summarize chromosome segment changes at arm and gene levels, and associate changes with sample level informatoin
Simulation Studies - Simulating immune composition and cell imaging data
Programming Skills
R Programming - Advanced scripting for fast, reproducible data wrangling, visualization, and analyses
R Package Development - Advanced function design and writing using Roxygen2 documentation, with experience submitting to CRAN and from GitHub
GitHub Version Control - Committing and pushing documented code to GitHub for open source analysis solutions - View My GitHub
Shiny Dashboards - Advanced tool buildling with Shiny such as iTIME where users can analyze data without needing to code
Bash Scripting - Intermediate script writing to process files
Python Programming - Intermediate scripting for data wrangling when R does not have packages or methods available
C++ Programming - Novice scripting for data wrangling when R is slow for specific tasks
