Braun Lab
Computational Biology from Atoms to Ecosystems
Resources
References stud the shelves. They look dull but useful; many are well thumbed. Group members, start here.High-throughput data analysis
- System analysis of high-throughput data
- Implications of big data for cell biology
- Coming of age: ten years of next-generation sequencing technologies
- Multi-omic approaches to disease
- Methods for the integration of multi-omics data: mathematical aspects
- Statistical methods in integrative genomics
- Analyzing gene expression data in terms of gene sets: methodological issues
- Studying and modeling dynamic biological processes using time-series gene expression data
Getting up to speed with R
Cheat sheets. The current set lives at posit.co/resources/cheatsheets — RStudio IDE, R Markdown, ggplot2, Shiny, dplyr, data import, and package development.
Textbooks.
- An Introduction to Statistical Learning — James, Witten, Hastie, Tibshirani
- R for Data Science — Wickham, Çetinkaya-Rundel, Grolemund
- R Packages — Wickham, Bryan
- Advanced R — Wickham
- Bookdown — many other good R books
Git and version control
Computing and visualization
- Quest user guide — Northwestern's high-performance computing cluster
- ColorBrewer — colour selection for maps and figures
- Data colour picker — sequential and divergent palettes
Calendar
BraunAdjacent collects events of interest to the group: the NSF-Simons Center for Quantitative Biology, NICO, Applied Mathematics, Statistics, and Molecular Biosciences, among others. Subscribe by iCal feed, or browse the Northwestern-wide calendar.