Bioinformatics for Beginners: RNA-Seq Data Analysis (Sept 27th – Dec 13th)
Lesson 1 (Sept 27th): Introduction to Unix and the Shell
Lesson 2 (Sept 29th): Navigating file systems with Unix
Qiagen Pathway Analysis for Beginners: Generating a Gene Network from Experimental Data (August 31)
For questions, email Qiagen technical support: email@example.com
Creating Publication Ready RNA-Seq Graphs in Partek Flow (Aug 24)
Bioinformatics Resources for CCR Scientists (July 26)
Presentation slides: Presentation Intro to Bioinformatics Resources FINAL VERSION
Variant Analysis – Experimental Design, Best Practices, and Workflows (June 30)
Chat history: chat history
Qlucore: Bulk RNA-Seq Data Analysis (June 29)
Data Wrangling with R (June 7 – July 7)
Course Materials: https://btep.ccr.cancer.gov/docs/data-wrangle-with-r/
Lesson 1, June, 7th: Introduction to R, RStudio, and the Tidyverse
This will be a no coding introduction to R, RStudio, and the Tidyverse. In this lesson, we will review some of the advantages of using R for data analysis and will get you acquainted with the RStudio environment. The help session will be devoted to getting everyone connected to the course on DNAnexus.
Lesson 2, June 9th: Getting started with R.
Lesson 2 will focus on some of the basics of R programming including naming and assigning R objects, recognizing and using R functions, understanding data types and classes, becoming familiar with the R programming syntax.
Lesson 3, June 14: Importing and reshaping data
In lesson 3, we will learn how to import simple and complex data and how to avoid common mistakes. We will also learn how to reshape data, for example, from wide to long format, with tidyr.
Lesson 4, June 16: Data Visualization with ggplot2
Lesson 4 will be a brief reprieve from data wrangling. In this lesson, we will learn the basics of plotting with ggplot2.
Lesson 5, June 21st: Introducing dplyr and the pipe
In Lesson 5, we will learn how to improve code interpretability with the pipe (%>%) from the magrittr package. We will also learn how to merge and filter data frames.
Lesson 6, June 23rd: Continue data wrangling with dplyr.
In Lesson 6, we will continue to wrangle data using dplyr. This lesson will focus on functions such as group_by(), arrange(), summarize(), and mutate().
Lesson 7, July 5th: Lesson Review
In Lesson 7 we will review many of the important concepts we learned throughout the course.
Lesson 8, July 7th: Working with your own data.
Lesson 8 will be a BYOD (bring your own data) class. You will have two hours to work on your own data and get help accordingly. If you do not have your own data, we will provide a data set and practice questions for you to test your wrangling skills.
Partek Flow: Bulk and Single Cell Gene Expression Visualization (May 18)
Qlucore: Import and analyze public data from SRA, GEO and TCGA (May 11)
Training: Access GEO, SRA, ArrayExpress, TCGA, GTEx and more with Qiagen IPA Land Explorer (April 20)
Single Cell RNA-Seq Analysis with Partek Flow (April 13)
Qlucore: Pathway Analysis with Gene Set Enrichment Analysis (GSEA) (April 6)
- GSEA hands-on webinar_Qlucore.pdf
- GSEA in Qlucore.pdf
- GSEA_MSigDB Collections.pdf
- How to do Pathway Analysis A.pdf
- MSigDB Hallmark human gene sets UCSD.pdf
Data Visualization with R (April 5 – May 10)
Course Materials: https://btep.ccr.cancer.gov/docs/data-visualization-with-r/
Lesson 1, April 5: Introduction to plot types
Why R for data visualization? We will introduce the various plot types that will be generated throughout the course and will showcase related plots that you will be able to create in the future using the foundational skills gained.
Lesson 2, April 12: Basics of ggplot2
In lesson 2 of the Data Visualization with R series we will focus on the basics of ggplot2, including the grammar of graphics philosophy and its application. This lesson will provide a hands on introduction to the ggplot2 syntax, geom functions, mapping and aesthetics, and plot layering.
Lesson 3, April 19: Scatter plots and ggplot2 customization
In lesson 3 of the Data Visualization with R series we will continue the discussion on the grammar of graphics, with a focus on ggplot2 plot customization including axes labels, coordinate systems, axes scales, and themes. This hands on lesson will showcase these features of plot building through the generation of increasingly complex scatter plots using data included with a base R installation as well as RNASeq data.
Lesson 4, April 26: Visualizing summary statistics with histograms, bar plots, and box plots
In lesson 4 of the Data Visualization with R series we will learn to generate plots that will help with visualization of summary statistics including a bar plot with error bars, histogram, as well as the box and whiskers plot.
Lesson 5, May 3: Visualizing clusters with heatmaps
In lesson 5 of the Data Visualization with R series we will introduce the heatmap and dendrogram as tools for visualizing clusters in data.
Lesson 6, May 10: Combining multiple plots to create a figure panel
Chat history: data_visualization_r_lesson6
In lesson 6 of the Data Visualization with R series we will focus on generating sub plots and multi plot figure panels using ggplot2 associated packages. This will allow us to meet any figure limitations that scientific journals may have.
Introducing QIIME2, a Powerful Platform for Microbiome Analysis (March 24)
QIIME2 is a powerful microbiome analysis platform with a wide array of tools that can be used throughout all stages of your microbiome workflow, from raw data to statistical evaluation and visualization.
This course will provide an overview of QIIME2, which will include an introduction to the core plugins and methods available with a base QIIME2 installation, tools for reproducibility and visualization, features available for community support and help, and additional learning opportunities.
Link to Slides: qiime2_overview
Qlucore: Single Cell Data, from 10x Output to Clustering, Cluster ID and Statistical Analysis in a Visual Qlucore Platform (February 23)
R Introductory Series (Jan – Feb)
This course will include a series of lessons for individuals new to R or with limited R experience. The purpose of this course is to introduce the foundational skills necessary to begin to analyze and visualize data in R. This course is not designed for those with intermediate R experience and is not tailored to any one specific type of analysis.
Course documentation: https://btep.ccr.cancer.gov/docs/rintro/
Lesson 1, Introduction to R: Why Learn R?, Getting Started with R and RStudio, R Basics
Lesson 2, Data Frames and Data Wrangling
Lesson 3, Working with Tabular Data in R
Lesson 4, Visualize Data in Graphs, Plots and Charts with R