R Programming and RNA-Seq Analysis
Each of our lessons are recorded, to allow you to look back at the confusing parts and review anything you need to. Please contact any one of the admins for a link to the DataCamp course.
Week 1: Orientation
Here we go over the basics of R, RStudio, and Data structures in R. You can use the format here to work through the problems in RStudio.
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Lecturer: Henry Miller
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Homework
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Download R & RStudio
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Introductory R course in DataCamp: link
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Week 2: Conditional logic and Control Flow
Lecturer: Paulino Ramirez
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Homework:
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Intermediate R course in DataCamp
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Importing flat files chapter in DataCamp
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R assessment in DataCamp
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Week two practice problems
Week 3: Review
Lecturer: Henry Miller
You can use the format here to go through the lesson in RStudio.
Homework
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Introduction to the Tidyverse in DataCamp
Week 4: The Tidyverse
Lecturer: Henry Miller
Homework
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Week 4 practice problems
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Continue practicing on Datacamp
Week 5: R for biological data science-how to use R to organize your big data
Lecturer: Henry Miller
GitHub directory: here
Homework
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Analyze qPCR data (found in project file link above) using R
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Continue practicing on DataCamp
Week 6: Starting Downstream RNASeq analysis with R
Lecturer: Dr. Yidong Chen
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Homework
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Differential gene expression analysis with “airway” data set
Week 7: RNA Seq Visualization
Homework
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EDA and DEG plots with airway data set
Week 8: Biological interpretation
Lecturer: Henry Miller
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Week 9: Databases and Web-based analysis tools
Lecturer: : Paulino Ramirez & Henry Miller
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Homework
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Gather data from Recount2 and analyze using previous methods along with web-based tools (e.g., enrichr).
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Complete the introduction to Shell course in DataCamp
Upstream RNA-Seq analysis with Linux shell:
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Week 10: Linux shell basics
Lecturer: Bernard Fongang, PhD
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Homework
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​Finish the Introduction to Shell course in DataCamp and download fastq files
Week 11: Alignment and read quantification
Lecturer: Chris Chiu, PhD
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Week 12: Beyond gene expression
Lecturer: Siyuan Zheng, PhD
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