NYRaMP-Informatics-2024: Difference between revisions

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<center>'''BioMedical Genomics'''</center>
<center>'''NYRaMP Informatics Workshop'''</center>
<center>August 2024, Tuesdays 10-12 noon, DNA Learning Center</center>
<center>August 2024, Tuesdays 10-12 noon, DNA Learning Center</center>
<center>'''Instructor:''' Weigang Qiu, Ph.D., Professor, Department of Biological Sciences, Hunter College, CUNY; '''Email:''' wqiu@.hunter.cuny.edu</center>
<center>'''Instructors:''' Weigang Qiu (Professor, Hunter College, wqiu@.hunter.cuny.edu) & Brandon Ely (CUNY Graduate Center, bely@gradcenter.cuny.edu)</center>
<center>'''T.A.:''' Brandon Ely, CUNY Graduate Center; '''Email:'''bely@gradcenter.cuny.edu</center>
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==Course Overview==
==Overview==
Welcome to Introductory BioMedical Genomics, a seminar course for advanced undergraduates and graduate students. 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 biology, computational, and data sciences.   
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.  
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. Students are expected to be able to replicate key results of data analysis from published studies.
This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises.
 
The pre-requisites of the course are college-level courses in molecular biology, cell biology, and genetics. Introductory courses in computer programming and statistics are preferred but not strictly required.


==Learning goals==
==Learning goals==
By the end of this course successful students will be able to:  
By the end of this workshop students will be able to:  
* Describe next-generation sequencing  (NGS) technologies & contrast it with traditional Sanger sequencing
* Visualize genomics data using R & RStudio
* Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome.
* Perform t-tests, regression analysis, and association tests with R & Rstudio
* Visualize and explore genomics data using R & RStudio
* Interpret results of data visualization & statistical analysis
* Replicate key results using a raw data set produced by a primary research paper
 
==Web Links==
==Web Links==
* Install R base: https://cloud.r-project.org
* Cloud R account (free): https://posit.cloud/; Join the shared work space "NYRaMP-Informatics"
* Install R Studio (Desktop version): http://www.rstudio.com/download
* 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]
* A reference book: [https://r4ds.had.co.nz/ R for Data Science (Wickharm & Grolemund)]
* A reference book: [https://r4ds.hadley.nz/ R for Data Science (Wickharm & Grolemund)]
 
==Week 1. Aug 6==
* Pre-test: visualization, interpretation, and stats. Download file: [[File:Pre-test.pdf|thumb]]
* 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|thumb]]
* Practice #1
 
==Week 2. Aug 13==
* R Tutorial 2. Data manipulation. Download slides: [[File:R-tutorials-Part-2.pdf|thumb]]
* Practice #2
 
==Week 3. Aug 20==
* R Tutorial 3. Data visualization. Lecture slides: [[File:R-tutorials-Part-3.pdf|thumb]]
* Practice #3
 
==Week 4. Aug 27==
* Tutorial #4. t-test and regression analysis. Slides: [[File:R-tutorials-Part-4.pdf|thumb]]
* 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)

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