NYRaMP-Informatics-2024

NYRaMP Informatics Workshop August 2024, Tuesdays 10-12 noon, DNA Learning Center Instructors: Weigang Qiu (Professor, Hunter College, wqiu@.hunter.cuny.edu) & Brandon Ely (CUNY Graduate Center, bely@gradcenter.cuny.edu)

MA plot	Volcano plot	Heat map
fold change (y-axis) vs. total expression levels (x-axis)	p-value (y-axis) vs. fold change (x-axis)	genes significantly down or up-regulated (at p<1e-4)

Overview

A genome is the total genetic content of an organism. Driven by breakthroughs such as the decoding of the first human genome and rapid DNA and RNA-sequencing technologies, biomedical sciences are undergoing a rapid & irreversible transformation into a highly data-intensive field, that requires familiarity with concepts in both biological, computational, and statistical sciences.

Genome information is revolutionizing virtually all aspects of life sciences including basic research, medicine, and agriculture. Meanwhile, use of genomic data requires life scientists to be familiar with concepts and skills in biology, computer science, as well as statistics.

This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises.

Learning goals

By the end of this workshop students will be able to:

• Visualize genomics data using R & RStudio
• Perform t-tests, regression analysis, and association tests with R & Rstudio
• Interpret results of data visualization & statistical analysis

Web Links

• Install R Base & R Studio (Desktop version): https://posit.co/download/rstudio-desktop/
• Textbook: Introduction to R for Biologists
• Download: R datasets
• A reference book: R for Data Science (Wickharm & Grolemund)

Week 1. Aug 6

• Pre-test: visualization, interpretation, and stats
• Computer setup & software download/installation
• R Tutorial 1. Getting started: Basics: interface, packages, variables, objects, functions

Week 2. Aug 13

• R Tutorial 2. Data manipulation & visualization

Week 3. Aug 20

• R Tutorial 3. t-tests & linear regression
• Documentation & presentation with R Markdown

Week 4. Aug 27

• R Tutorial 4. Cluster analysis
• Exit self-test