Favorite Tutorials

My favorites bar is filled up, so I'm dumping all my favorite bioinformatics-relevant tutorials below.

General Bioinformatics Tutorials

• Biomedical Data Science (eBook) by Rafael Irizarry and Michael Love. This tutorial covers statistical modeling and inference, a little machine learning and has some great R based vignettes for RNAseq, ChIPseq and DNA CpG methylation analysis.

 

Single Cell Analysis

• Analysis of Single Cell RNA-seq mini textbook from the Hemberg Lab from the Sanger Institute, UK. Fairly comprehensive (video guided) vignette of how to do basic scRNA-seq analysis from raw data to figures. This is a pretty gentle intro to scRNA-seq analysis in R
• Orchestrating Single-Cell Analysis with Bioconductor is a more comprehensive online scRNA-seq text written by the Bioconductor team. Really good overview of the different tools and basic data structures for scRNA-seq

 

High Throughput Sequencing Stuff

• Dave's Wiki on SAMTools is a blog post that covers SAM and BAM conversions and other useful stuff about SAMTools.
• fastq-dump tips from Rob Edward's lab. Best practices for obtaining fastq files from SRA tools

 

R for Bioinformatics

• Advanced R (eBook) by Hadley Wickham covers the basics (despite the title) of R syntax and data structures. Very useful R primer.
• DiffBind Tutorial DiffBind is a great tool for doing comparative ChIP-seq analysis in R (bioconductor)
• Stats 366 is a Stanford course that biological sequences and a an intro to networks in R
• Linear Models in R is a companion website to a Princeton University course on linear models by Germán Rodríguez. The site has a gentle intro to R and linear modeling in R

 

R in General

• An Introduction to R is the official R manual, provided by the creators of R itself. A really solid programatic introduction to the R language.
• Resources to help you learn and use R Compiled by UCLA’s Technology Services
• The R Language Definition is a detailed guide to the technical terms of the R language. Useful to have when learning R from any source.
• R Programming Wikibook is a comprehensive source of information on R from introduction to more advanced topics.
• Penn 4-Week Summer R Course is a guided, 4-week tour of R.
• The R-Inferno is a guide to and description of trouble spots, odities, traps and glitches in R that may be a good resource once you’ve grown comfortable writing your first programs.
• An R and S-PLUS comopanion to Applied Regression by John Fox and Sanford Weisberg. Webite includes code, data, and other resources used in the book.
• Bret Larget’s R Help
• Using R for Data Analysis and Graphics is an introduction by J.H. Maindonald
• Kickstarting R by Jim Lemon
• R Style Guide by Hadley Wickham introduces some guidelines that help keep code portable, legible and clean.
• R-bloggers is a great blog that has tutorials on many topics in R but it is plotting and data cleaning heavy. Uses data from many different sources but all operations are relevant for Bioinformatics.
  • Machine Learning Course from R-bloggers
• R for Data Science is another exceptional Hadley Wickham contribution together with Garrett Grolemund. This free online book goes through the basics of data cleaning, (some) modeling and plotting in R using the TidyVerse paradigm.