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
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