A Tidy Transcriptomics introduction to RNA-Seq analyses

https://stemangiola.github.io/bioc_2020_tidytranscriptomics/articles/tidytranscriptomics.html

*** R learning materials and computational biology

Online courses

Github applied computational genomics
https://github.com/quinlan-lab/applied-computational-genomics

https://github.com/BenLangmead/comp-genomics-class


R programing at Coursera
https://www.coursera.org/learn/r-programming

Data camp https://www.datacamp.com/ 
 introduction, intermediate, and advanced R

Statistics and R
https://www.edx.org/course/statistics-r-harvardx-ph525-1x

http://genomicsclass.github.io/book/pages/classes.html
https://courses.edx.org/courses/HarvardX/PH525.1x/1T2015/info

Quantitative biology workshop
https://www.edx.org/course/quantitative-biology-workshop-mitx-7-qbwx-2

Introduction to Bioconductor: annotation and analysis of genomes and genomics assays
https://www.edx.org/course/introduction-bioconductor-annotation-harvardx-ph525-5x

https://cgondro2.une.edu.au/Rcourse.htm
http://www.springer.com/us/book/9783319144740 . R book, primer to analysis of genomics data using R


Books and articles
An introduction to statistical learning with applications in R
http://www-bcf.usc.edu/~gareth/ISL/index.html

• Stephen J. Eglen's PLOS article. A quick guide to teaching R programming to computational biology students. 
• • 
    

References:
http://hongqinlab.blogspot.com/2013/10/useful-r-materials-for-teaching.html
http://hongqinlab.blogspot.com/2015/06/ngs-tutorials.html

Sample videos, Developing YouTube video to enhance education and training in bioinformatics and genomics

Selected Bioinformatics Videos

• Annotate ORF using APE
• Design PCR primers to amply a fragment that contains the mutation on msh2
• Analyzing PCR primer using ApE
• Protein structure visualization using SWISS PDB Viewer
• Translation from DNA to protein, Python
• Find Restriction Enzyme recognition sites, Python demo

Seletec Computing with R Videos

• A quick introduction to basic R usage (in RStudio)
• Serial dilution data analysis using R
• Standard curve analysis using R
• Flow cytometry data analysis, clustering, using R
• Convert Excel file to csv format, read into R and plot. 
• Hierarchical Clustering by hclust in R on a distance matrix between four cities.
• Hierarchical clustering illustrated by Lady Gaga using hclust() in R.
• Retrieve and analyze a gene expression data set from NCBI GEO in R
• How to change R working directory in Windows
• How to use 'match' function in R, and How to use R match() function to merge different data sets
• Multiple regression and causal factor identification using R
• Introduction to epidemiology using the London Cholera example (simulation needs improvement)
• Make solution demo in Rstudio, BIO125, spring 2015

Other tutorial videos

• Serial dilution demo, paper and coloring exercises

Animation on herd immunity
https://imgur.com/gallery/8M7q8#J7LANQ4

*** phylogenetic comparative method

Modern Phylogenetic Comparative Method

http://www.mpcm-evolution.org/

R related teach resources for BIO386

EdX computational genomics using R
http://genomicsclass.github.io/book/pages/classes.html

R programming by microsoft
https://www.edx.org/course/introduction-r-programming-microsoft-dat204x?utm_source=Course%20Announcements%20Mailing%20List&utm_medium=email&utm_content=image&utm_campaign=Student_Newsletter_August_4

Data Carpentry
https://github.com/datacarpentry/R-genomics
https://github.com/QinLab/r-bio.github.io

MSM guest lecture on R

Github: https://github.com/hongqin/MSM-guest-lecture

1.What is R? 

Wikipedia entry on R

Why R by Courtney Brown at Emory. 

Why R and beyond.

R blogger that provides recent and often interesting development about R.  

What is R video.

2. Install R to your own computers.

Instructions to download R. 

Install R studio.  RStudio provides a nice GUI to R.

Install packages to R: Video for Windows Version.

3. Introduction to R.

• TryR@code school
  
  
  R data camp
  
  
• Stephen J. Eglen's short intro slides. 
 
•  
 
•  
 
•  

Hong Qin's slides: Overview of R;   Basic programming in R; Input & Output in R;

Lydon Walker, getting started with R, an accelerated primer

Coursera course

 4. Simple exercises in R.

Qin's simple.R excises.  Please select 'Raw' and 'save-as' in text format. Unfortunately, web browser seem to automatically add 'txt' to the file name, and Rstudio does not run 'txt' format.  To solve this problem, we can 'create' a new R script, and copy-paste from code from the text file.

(The workshop audience mostly reached this step).

Youtube tutorial converting Excel data to CSV and load into R. 
    The sunflow seed Excel file is here.  

5. Make solution exercise.

Write an R function to calculate how much NaCl needed for X ml of Y mM NaCl solution. 

6. Simple statistical analysis in R.

Simple regression exercise.

7. Advanced training materials.

Multiple regression demo

Hierarchical clustering using cities. Code, Video.

Laddy Gaga and clustering analysis. Code. Video.

Bioconductor workshop materials.

8. Useful R materials.

• Hong Qin's R and computational biology GitHub site

• Stephen J. Eglen's PLOS article. A quick guide to teaching R programming to computational biology students. 

• 
  

http://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf

http://www.rseek.org. 

http://addictedtor.free.fr/graphiques/. 


Newly launched interactive cloud-based R for teaching: http://www.datamind.org/


Google-developers R programming videos


Avril Coghlan's little book of R for bioinformatics.


Github's collection of free R books

R by examples

9.   For preparation for the workshop

Wireless connection, laptops, power-outlets are recommended. 

Install packages and data on flash-drives in case internet connection is slow. (This can be a problem when all participants are download at the same time.)

A flow-chart on easel can be used for clarity.

Common problem is to set R working directory. 
 

R causal inference and machine learning

R  causal inference
http://blog.revolutionanalytics.com/2015/03/targeted-learning-r-packages-for-causal-inference-and-machine-learning.html

HMM in R
https://www.inovancetech.com/hmm-tutorial-1.html

From excel to R,

https://districtdatalabs.silvrback.com/intro-to-r-for-microsoft-excel-users

flowchart to learn R

http://www.r-bloggers.com/how-to-learn-r-a-flow-chart/

R/Rstudio tutorial page for BIO125, Spring 2015

This is a dynamic page and will change frequently during Spring semester of 2015.

1.What is R? 

Wikipedia entry on R

Why R by Courtney Brown at Emory. 

Why R and beyond.

R blogger that provides recent and often interesting development about R.  

What is R video (Added after the class).

2. Install R to your own computers.

Instructions to download R. 

Install R studio.  RStudio provides a nice GUI to R.

Install packages to R: Video for Windows Version.

3. Introduction to R.

• Stephen J. Eglen's short intro slides. 
 
•  
 
•  
 
•  

Hong Qin's slides: Overview of R;   Basic programming in R; Input & Output in R;

Lydon Walker, getting started with R, an accelerated primer

 4. Simple exercises in R.

Youtube tutorial converting Excel data to CSV and load into R. 
    The sunflow seed Excel file is here.  

5. Make solution exercise.

Write an R function to calculate how much NaCl needed for X ml of Y mM NaCl solution. 

6. Simple statistical analysis in R.

Simple regression exercise.

7. Advanced training materials.

Multiple regression demo

Hierarchical clustering using cities. Code, Video.

Laddy Gaga and clustering analysis. Code. Video.

Bioconductor workshop materials.

8. Useful R materials.

• Hong Qin's R and computational biology GitHub site

• Stephen J. Eglen's PLOS article. A quick guide to teaching R programming to computational biology students. 

• 
  

http://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf

http://www.rseek.org. 

http://addictedtor.free.fr/graphiques/. 


Newly launched interactive cloud-based R for teaching: http://www.datamind.org/


Google-developers R programming videos


Avril Coghlan's little book of R for bioinformatics.


Github's collection of free R books

R by examples

Elements of Statistical Learning, video, pdf, Hastie, Tibshirani, 2014

Cloned from R-Blogger. 

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.

If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website.

If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work.

As a supplement to the textbook, you may also want to watch the excellent course lecture videos (linked below), in which Dr. Hastie and Dr. Tibshirani discuss much of the material. In case you want to browse the lecture content, I’ve also linked to the PDF slides used in the videos.

Chapter 1: Introduction (slides, playlist)

• Opening Remarks and Examples (18:18)
• Supervised and Unsupervised Learning (12:12)

Chapter 2: Statistical Learning (slides, playlist)

• Statistical Learning and Regression (11:41)
• Curse of Dimensionality and Parametric Models (11:40)
• Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
• Classification Problems and K-Nearest Neighbors (15:37)
• Lab: Introduction to R (14:12)

Chapter 3: Linear Regression (slides, playlist)

• Simple Linear Regression and Confidence Intervals (13:01)
• Hypothesis Testing (8:24)
• Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
• Model Selection and Qualitative Predictors (14:51)
• Interactions and Nonlinearity (14:16)
• Lab: Linear Regression (22:10)

Chapter 4: Classification (slides, playlist)

• Introduction to Classification (10:25)
• Logistic Regression and Maximum Likelihood (9:07)
• Multivariate Logistic Regression and Confounding (9:53)
• Case-Control Sampling and Multiclass Logistic Regression (7:28)
• Linear Discriminant Analysis and Bayes Theorem (7:12)
• Univariate Linear Discriminant Analysis (7:37)
• Multivariate Linear Discriminant Analysis and ROC Curves (17:42)
• Quadratic Discriminant Analysis and Naive Bayes (10:07)
• Lab: Logistic Regression (10:14)
• Lab: Linear Discriminant Analysis (8:22)
• Lab: K-Nearest Neighbors (5:01)

Chapter 5: Resampling Methods (slides, playlist)

• Estimating Prediction Error and Validation Set Approach (14:01)
• K-fold Cross-Validation (13:33)
• Cross-Validation: The Right and Wrong Ways (10:07)
• The Bootstrap (11:29)
• More on the Bootstrap (14:35)
• Lab: Cross-Validation (11:21)
• Lab: The Bootstrap (7:40)

Chapter 6: Linear Model Selection and Regularization (slides, playlist)

• Linear Model Selection and Best Subset Selection (13:44)
• Forward Stepwise Selection (12:26)
• Backward Stepwise Selection (5:26)
• Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared (14:06)
• Estimating Test Error Using Cross-Validation (8:43)
• Shrinkage Methods and Ridge Regression (12:37)
• The Lasso (15:21)
• Tuning Parameter Selection for Ridge Regression and Lasso (5:27)
• Dimension Reduction (4:45)
• Principal Components Regression and Partial Least Squares (15:48)
• Lab: Best Subset Selection (10:36)
• Lab: Forward Stepwise Selection and Model Selection Using Validation Set (10:32)
• Lab: Model Selection Using Cross-Validation (5:32)
• Lab: Ridge Regression and Lasso (16:34)

Chapter 7: Moving Beyond Linearity (slides, playlist)

• Polynomial Regression and Step Functions (14:59)
• Piecewise Polynomials and Splines (13:13)
• Smoothing Splines (10:10)
• Local Regression and Generalized Additive Models (10:45)
• Lab: Polynomials (21:11)
• Lab: Splines and Generalized Additive Models (12:15)

Chapter 8: Tree-Based Methods (slides, playlist)

• Decision Trees (14:37)
• Pruning a Decision Tree (11:45)
• Classification Trees and Comparison with Linear Models (11:00)
• Bootstrap Aggregation (Bagging) and Random Forests (13:45)
• Boosting and Variable Importance (12:03)
• Lab: Decision Trees (10:13)
• Lab: Random Forests and Boosting (15:35)

Chapter 9: Support Vector Machines (slides, playlist)

• Maximal Margin Classifier (11:35)
• Support Vector Classifier (8:04)
• Kernels and Support Vector Machines (15:04)
• Example and Comparison with Logistic Regression (14:47)
• Lab: Support Vector Machine for Classification (10:13)
• Lab: Nonlinear Support Vector Machine (7:54)

Chapter 10: Unsupervised Learning (slides, playlist)

• Unsupervised Learning and Principal Components Analysis (12:37)
• Exploring Principal Components Analysis and Proportion of Variance Explained (17:39)
• K-means Clustering (17:17)
• Hierarchical Clustering (14:45)
• Breast Cancer Example of Hierarchical Clustering (9:24)
• Lab: Principal Components Analysis (6:28)
• Lab: K-means Clustering (6:31)
• Lab: Hierarchical Clustering (6:33)

Interviews (playlist)

• Interview with John Chambers (10:20)
• Interview with Bradley Efron (12:08)
• Interview with Jerome Friedman (10:29)
• Interviews with statistics graduate students (7:44)

bio233 lab, R and Rstudio analyze of serial dilution data

We spent about 1 hour install R and Rstudio. I then told many students to work as a team.
Some students have various windows computers, and their interface were different.

Then many students waited for xlsx package to be installed through wireless. I have to abadon the xlsx approach, and switched to csv approach.

I then let students reviewed their streaked plates. Only 1 plate can be given A-. The rest of them are at B- . About 2/3 of the plates have to be redone (below C).

I let the students try to streak the plates again. Next day, I found out that almost all students failed. Most students seem to pick the old colonies for every streak, meaning that they did not get the idea at all. 


Some students tried to open R code in the ZIP archive directly. This is a common mistake. This problem become more annoying with the different version of operating systems offer different viewing and extraction options. 

*** Useful R materials for teaching and learning

Useful R materials.

EdX computational genomics using R
http://genomicsclass.github.io/book/pages/classes.html
https://courses.edx.org/courses/HarvardX/PH525.1x/1T2015/info

Data Camp

https://www.datacamp.com/

 

R swirl 

https://www.datacamp.com/swirl-r-tutorial

TryR @ code school (no loops in chapter 1-7!)

• Stephen J. Eglen's PLOS article. A quick guide to teaching R programming to computational biology students. 

• 
  

http://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf

http://www.rseek.org. 

http://addictedtor.free.fr/graphiques/. 


Newly launched interactive cloud-based R for teaching: http://www.datamind.org/


Hong Qin's R and computational biology GitHub site




Why R? 

An Introduction to Statistical Learning

with Applications in R

http://www-bcf.usc.edu/~gareth/ISL/index.html 



Elements of statistical learning, 

http://www.r-bloggers.com/free-stanford-online-course-on-statistical-learning-with-r-starting-on-19-jan-2015/

 http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
 

R tutorial for Oak Ridge Science Education

Please note that the entire workshop material can be download from its GitHub repository ORAU-R.

1.What is R? 

Wikipedia entry on R

Why R by Courtney Brown at Emory. 

Why R and beyond.

R blogger that provides recent and often interesting development about R.  

What is R video (Added after the class).

2. Install R to your own computers.

Instructions to download R. 

Install R studio.  RStudio provides a nice GUI to R.

Install packages to R: Video for Windows Version.

3. Introduction to R.

• Stephen J. Eglen's short intro slides. 
•  
•  
•  

Hong Qin's slides: Overview of R;   Basic programming in R; Input & Output in R;

Lydon Walker, getting started with R, an accelerated primer

 4. Simple exercises in R.

Qin's simple.R excises.  Please select 'Raw' and 'save-as' in text format. Unfortunately, web browser seem to automatically add 'txt' to the file name, and Rstudio does not run 'txt' format.  To solve this problem, we can 'create' a new R script, and copy-paste from code from the text file.

(The workshop audience mostly reached this step).

Youtube tutorial converting Excel data to CSV and load into R. 
    The sunflow seed Excel file is here.  

5. Make solution exercise.

Write an R function to calculate how much NaCl needed for X ml of Y mM NaCl solution. 

6. Simple statistical analysis in R.

Simple regression exercise.

7. Advanced training materials.

Multiple regression demo

Hierarchical clustering using cities. Code, Video.

Laddy Gaga and clustering analysis. Code. Video.

Bioconductor workshop materials.

8. Useful R materials.

• Hong Qin's R and computational biology GitHub site

• Stephen J. Eglen's PLOS article. A quick guide to teaching R programming to computational biology students. 

• 
  

http://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf

http://www.rseek.org. 

http://addictedtor.free.fr/graphiques/. 


Newly launched interactive cloud-based R for teaching: http://www.datamind.org/


Google-developers R programming videos


Avril Coghlan's little book of R for bioinformatics.


Github's collection of free R books

R by examples

9. Discussions on programming workshops.

Titus Brown's blog on teaching programming.

10. My reflections

The main workshop is led by Jame Ferguson and Bob Panoff. There were 9 college biology and 2 high-school biology faculty in the workshop.

I was given 90 minutes. My goal was that audience would be able to install R and Rstudio and run R scripts on their own after the workshop.

I spent 30 minutes on introducing R to the audience, my experience of teaching R and computational genomics to undergraduates. I showed them the GEO database and R interface.  I used the examples of make-solution, hclust on cities and Lady Gaga.  For the next 50 minutes, I let the audience to download and install Rstudio.  A few of them needed my help to down and install R and Rstudio. Most of them were able to run to the simple.R exercise (step 4.a). I run out of time after step 6.

I was somewhat stunned that downloading R code directly from GitHub repository is surprisingly cumbersome.

During the exercise time, a few people were clearly ahead and poked around. Some were especially interested in the GEO2R portal.

Bob mentioned that R has been used in a few other liberal art colleges, including Davidson and Pomona.

At the end of the workshop, I was asked "why do 'we' have to teach R to biology students?". I used my own experiences and argued that R is the state-of-the-art tool for data analysis in biology.


For preparation for the workshop

Wireless connection, laptops, power-outlets are recommended. 

Install packages and data on flash-drives in case internet connection is slow. (This can be a problem when all participants are download at the same time.)

A flow-chart on easel can be used for clarity.