NYRaMP-Informatics-2024: Difference between revisions

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* Interpret results of data visualization & statistical analysis
* Interpret results of data visualization & statistical analysis
==Web Links==
==Web Links==
* Install R Base & R Studio (Desktop version): https://posit.co/download/rstudio-desktop/
* Cloud R account (free): https://posit.cloud/; Join the shared work space "NYRaMP-Informatics"
* For your own computer, download the desktop version: https://posit.co/download/rstudio-desktop/
* Textbook: [http://r4all.org/#about Introduction to R for Biologists]
* Textbook: [http://r4all.org/#about Introduction to R for Biologists]
* Download: [http://www.r4all.org/books/datasets R datasets]
* Download: [http://www.r4all.org/books/datasets R datasets]
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==Week 1. Aug 6==
==Week 1. Aug 6==
* Pre-test: visualization, interpretation, and stats
* Pre-test: visualization, interpretation, and stats. Download file: [[File:Pre-test.pdf|thumb]]
* Computer setup & software download/installation
* Computer/Cloud setup & software download/installation
* R Tutorial 1. Basics: interface, packages, variables, objects, functions
* R Tutorial 1. Getting started: Basics: interface, packages, variables, objects, functions. Download slides: [[File:R-tutorials-Part-1.pdf|thumb]]
* Practice #1


==Week 2. Aug 13==
==Week 2. Aug 13==
* R Tutorial 2. Data manipulation & visualization
* R Tutorial 2. Data manipulation. Download slides: [[File:R-tutorials-Part-2.pdf|thumb]]
* Practice #2


==Week 3. Aug 20==
==Week 3. Aug 20==
* R Tutorial 3. t-tests & regresssion
* R Tutorial 3. Data visualization. Lecture slides: [[File:R-tutorials-Part-3.pdf|thumb]]
* Practice #3


==Week 4. Aug 27==
==Week 4. Aug 27==
* R Tutorial 4. Cluster analysis & R Markdown
* Tutorial #4. t-test and regression analysis. Slides: [[File:R-tutorials-Part-4.pdf|thumb]]
* Exit quiz
* Practice #4
 
Advanced topics (for Self Study)
* R Demo I: [https://borreliabase.org/~wgqiu/tutorial-markdown.html#part-1.-t-test-for-two-groups Documentation & presentation with R Markdown (Part I)]
* R Demo II: [https://borreliabase.org/~wgqiu/r-demo-2024.html#part-4.-advanced-topic-cluster-analysis-of-multi-dimensional-data Cluster analysis (Part 4)]
* R Demo III: [https://borreliabase.org/~wgqiu/r-demo-2024.html#part-5.-case-study-differential-gene-expressions-in-lyme-diease-bacteria Differential gene expression analysis (Part 5)]

Latest revision as of 01:44, 27 August 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

Week 1. Aug 6

  • Pre-test: visualization, interpretation, and stats. Download file: File:Pre-test.pdf
  • Computer/Cloud setup & software download/installation
  • R Tutorial 1. Getting started: Basics: interface, packages, variables, objects, functions. Download slides: File:R-tutorials-Part-1.pdf
  • Practice #1

Week 2. Aug 13

Week 3. Aug 20

Week 4. Aug 27

Advanced topics (for Self Study)