verohan / bioinfoclass

Bioinformatics Class at LLLab

bioinfoClass Further Reading

Bioinformatics Class at LLLab

For the whole class

Analysis of single cell RNA-seq data

n this course we will discuss some of the questions that can be addressed using scRNA-seq as well as the available computational and statistical methods avialable. The course is taught through the University of Cambridge Bioinformatics training unit, but the material found on these pages is meant to be used for anyone interested in learning about computational analysis of scRNA-seq data. The course is taught twice per year and the material here is updated prior to each event.

R, ggplot2 and Statistical Foundations r-statistics.co

An educational resource for those seeking knowledge related to machine learning and statistical computing in R. Here, you will find quality articles, with working R code and examples, where, the goal is to make the #rstats concepts clear and as simple as possible.

Data Analysis in Genome Biology

This course introduces algorithms, statistical methods and data analysis programming routines relevant for genome biology. It consists of three main components: lectures, hands-on practicals and student course projects. The lecture topics cover databases, sequence (NGS) analysis, phylogenetics, comparative genomics, genome-wide profiling methods, network biology and more. The hands-on practicals include homework assignments and course projects focusing on data analysis programming of next generation genome data using command-line tools on a computer cluster and the programming environment R.

Visualization

Visualizing Dendrograms in R https://rpubs.com/gaston/dendrograms

For Slide_01

Linux Commad

https://github.com/jaywcjlove/linux-command

For Slide_04

Introduction to R (PDF)

1. Overview
2. R Package Repositories
3. Installation of R Packages
4. Getting Around
5. Basic Syntax
6. Data Types
7. Data Objects
8. Important Utilities
9. Operators and Calculations
10. Reading and Writing External Data
11. Useful R Functions
12. dplyr Environment
13. SQLite Databases
14. Graphics in R
15. Analysis Routine
16. R Markdown
17. Shiny Web Apps
18. Session Info
19. References

Programming in R (PDF)

1. Overview
2. Control Structures
3. Loops
4. Functions
5. Useful Utilities
6. Running R Scripts
7. Building R Packages
8. Programming Exercises
9. Homework 5
10. Session Info
11. References

NGS Analysis Basics in R (PDF)

1. Overview
2. Package Requirements
3. Strings in R Base
4. Sequences in Bioconductor
5. NGS Sequences
6. Range Operations
7. Transcript Ranges
8. Homework 6
9. Session Info
10. References