Tips, Tricks, & Resources

High-Throughput Data Analysis:

1. Review – System Analysis of High Throughput Data
2. Review – Implication of Big Data for Cell Biology
3. Review – Coming of age: ten years of next-generation sequencing technologies
4. Review – Multi-Omic Approaches to Disease
5. Review – Methods for the integration of multi-omics data: mathematical aspects
6. Review – Statistical Methods in Integrative Genomics
7. Review – Analyzing gene expression data in terms of gene sets: methodological issues
8. Review – Studying and modeling dynamic biological processes using time-series gene expression data

Coding Resources:

Getting Up To Speed with R

• Coding Cheat Sheets
  1. Rstudio IDE Cheat Sheet
  2. R markdown Cheat Sheet
  3. ggplot2 Cheat Sheet
  4. shinyApps Cheat Sheet
  5. Data Transformation with dplyr Cheat Sheet
  6. Data Import Cheat Sheet
  7. R Package Development Cheat Sheet
• Animated Figure Tutorial
  1. Internal Braun Group tutorial for creating animated figures for presentation on Bitbucket
• Useful textbooks
  1. An Introduction to Statistical Learning with Applications in R – By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
  2. R For Data Science – By Garrett Grolemund and Hadley Wickham
  3. R Packages – By Hadley Wickham
  4. Advanced R – By Hadley Wickham
  5. Bookdown – Other Great R Books!

Getting Up To Speed with Git & GitHub

• Internal Braun Group git tutorial on BitBucket
• ProGit (SCM)
• GitHub User Guide

Getting Up To Speed with Northwestern’s Quest Super Computer

• Quest User Guide

Other Useful Links

• Data Visualization – Color Selection
  1. Color Brewer
  2. Color Picker