View source for 22100 - Course Programme Spring 2020 Spring 2020

'''Please note - This is the FIRST time the course runs, so the page is being created and updated and updated on-the-fly, i.e. the following is subject to change without notice!'''

[[File:R_for_bio_data_Science_text_logo_w_dna.png|right|250px]] [[File:R_for_bio_data_science_hex_logo_quadratic_small.png|right|250px]]

Welcome to the spring 2020 version of R for Bio Data Science! Below you will find some basic information on the course and the complete course schedule. Please note: The course is scheduled for block F1A, i.e. Mondays 8-12.

==Information for Course Participants==

'''Course Responsible and Teacher'''
* [LEJ] [https://www.dtu.dk/english/service/phonebook/person?id=22554&cpid=257113&tab=2&qt=dtupublicationquery Leon Eyrich Jessen]

'''Course Communication'''
* All course communication will facilitated via the official [https://rforbiodatascience20.slack.com/ R for Bio Data Science 2020 Slack workspace] (You will receive and invite on your student mail). It is recommended to [https://slack.com/intl/en-dk/downloads install the Slack desktop client] for ease of use

'''Course Format'''
* Classes will be taught using [https://rstudio.cloud/ RStudio Cloud], which is free. Students must sign up for an account
* Classes will be a mixture of lectures and group work
* Most of the group work will consist of computer exercises, students are required to bring their own laptop
* All learning resources will be open and available through DTU inside or this site
* Expected time usage: [https://www.dtu.dk/english/Education/Course-base 1 ECTS point equals approx. 28 hours], this translates to an expected time usage of ~9-10 hours/week for a 5 ECTS 13-week course with 1 exam day and preparation. You will spend 4h in class per week and should therefore expect 5-6h of preparation.

'''Course Project Work and Exam
* Description of [https://teaching.healthtech.dtu.dk/material/22100/00_exam_description.html Project Work and Exam]

'''Course Ressources'''
* [https://teaching.healthtech.dtu.dk/22100/index.php/22100_-_R_for_Bio_Data_Science Official course website]
* [https://kurser.dtu.dk/course/22100 Official course description]

'''General Daily Schedule'''

* 08.00 - 09.00 Recap of subject covered the prior week and introduction to topic of the day
* 09.00 - 12.00 Exercises

'''Location'''

* Building and room: Building 208, room 903 (In via 208, down the stairs, through the glass doors on your left and then look for the room on your right)
* Should you be new to DTU, a map of DTU Lyngby Campus is available [[Media:Dtu_lyngby_campus.png|here]]

==2020 Course Schedule Overview==

The [https://www.dtu.dk/english/education/student-guide/studying-at-dtu/academic-calendar Academic calendar] sets the 13-week period  for spring 2020 to 3/2 2020 - 12/5 2020, excluding holiday and non-teaching study breaks (all dates included) as follows:
* Easter holiday: 6/4 2020 - 13/4 2020
* St. Bededag (Danish national Holiday): 8/5 2020
* Ascension Day: 21/5 2020 - 22/5 2020
* Whitsun holiday: 1/6 2020
* Constitution Day: 5/6 2020.

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=== W01 - Monday Feb 3rd: Course Introduction and The very basics of R ===

{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* <code>base R</code>
| style="vertical-align: top;" |
* Talk: [https://teaching.healthtech.dtu.dk/material/22100/01_course_introduction.html Course Introduction]
* Talk: [https://teaching.healthtech.dtu.dk/material/22100/20180208_hackinar_project_organisation.pdf Reproducibility and Replicability in modern Bio Data Science]
* Talk: Getting started with RStudio and Rmarkdown
* Exercises: R - The very basics
| style="vertical-align: top;" |
* Video: [https://www.youtube.com/watch?v=n3uue28FD0w RStudio Overview]
* Book Chapter: [https://www.oreilly.com/library/view/hands-on-programming-with/9781449359089/ch01.html R - The very basics]
* Paper: [http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* Paper: [http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424 A Quick Guide to Organizing Computational Biology Projects]
* Web: [https://kurser.dtu.dk/course/22100 Read the detailed course description]
| style="vertical-align: top;" |
A student who has met the objectives of the session will be able to:
* Explain why reproducible data analysis is important, as well as identify relevant challenges and explain replicability versus reproducibility
* Describe the components of a reproducible data analysis
* Create an [https://rstudio.cloud/ RStudio Cloud] account and run cloud based sessions
* Master the very basics of R
* Navigate the RStudio IDE
* Create, edit and run a basic RMarkdown document
|-
|}
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=== W02 - Monday Feb 10th: Data Visualisation ===
{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://ggplot2.tidyverse.org/ <code>ggplot</code>]
| style="vertical-align: top;" |
* Recap: R - The Very Basics
* [https://teaching.healthtech.dtu.dk/material/22100/02_data_visualisation.html Lecture]
* [https://teaching.healthtech.dtu.dk/material/22100/02_exercises_data_visualisation.html Exercises]
| style="vertical-align: top;" |
* Book Chapter: [https://r4ds.had.co.nz/data-visualisation.html R4DS Chapter 3: Data Visualisation]
* Paper: [http://vita.had.co.nz/papers/layered-grammar.pdf A Layered Grammar of Graphics]
* Video: [https://www.youtube.com/watch?v=9YTNYT1maa4 EMBL Keynote Lecture - Data visualization and data science]
| style="vertical-align: top;" |
A student who has met the objectives of the session will be able to:
* Use ggplot to visualize multilayer data from e.g. high-througput -omics platforms
* Decipher the components of a ggplot
|-
|}
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=== W03 - Monday Feb 17th: Data manipulation I: The 6 basic verbs ===
{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://dplyr.tidyverse.org/ <code>dplyr</code>]
| style="vertical-align: top;" |
* Recap: Data Visualisation
* [https://teaching.healthtech.dtu.dk/material/22100/03_talk_data_manipulation.html Lecture]
* [https://teaching.healthtech.dtu.dk/material/22100/03_exercises_data_manipulation.html  Exercises]
| style="vertical-align: top;" |
* Book Chapter: R4DS: [https://r4ds.had.co.nz/explore-intro.html 2], [https://r4ds.had.co.nz/transform.html 5], [https://r4ds.had.co.nz/wrangle-intro.html 9], [https://r4ds.had.co.nz/tidy-data.html 12]
* Video: [https://www.youtube.com/watch?v=8SGif63VW6E Hadley Wickham's "dplyr" tutorial at useR 2014 (1/2)]
* Video: [https://www.youtube.com/watch?v=Ue08LVuk790 Hadley Wickham's "dplyr" tutorial at useR 2014 (2/2)]
| style="vertical-align: top;" |
A student who has met the objectives of the session will be able to:
* Understand and apply the 6 basic dplyr verbs <code>filter()</code>, <code>arrange()</code>, <code>select()</code>, <code>mutate()</code>, <code>summarise()</code> and <code>group_by()</code>
* Understand and apply the additional verbs <code>count()</code>, <code>drop_na()</code>, <code>View()</code>
* Combine dplyr verbs to form a data manipulation pipeline using the pipe <code>%>%</code> operator
* Decipher the components and functions hereof, of a dplyr pipeline
|-
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=== W04 - Monday Feb 24th: Data Manipulation II: Long and wide data, joins, strings and factors ===

{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://dplyr.tidyverse.org/ <code>dplyr</code>]
* [https://stringr.tidyverse.org/ <code>stringr</code>]
* [https://tidyr.tidyverse.org/ <code>tidyr</code>]
| style="vertical-align: top;" |
* Recap: Data Manipulation I
* [https://teaching.healthtech.dtu.dk/material/22100/04_exercises_data_manipulation_II.html Exercises]
| style="vertical-align: top;" |
* Book Chapter(s): R4DS: [https://r4ds.had.co.nz/tidy-data.html 12], [https://r4ds.had.co.nz/relational-data.html 13], [https://r4ds.had.co.nz/strings.html 14], [https://r4ds.had.co.nz/factors.html 15]
* Video: [https://www.youtube.com/watch?v=jOd65mR1zfw What is data wrangling? Intro, Motivation, Outline, Setup -- Pt. 1 Data Wrangling Introduction]
* Video: [https://www.youtube.com/watch?v=1ELALQlO-yM Tidy Data and tidyr -- Pt 2 Intro to Data Wrangling with R and the Tidyverse]
* Video: [https://www.youtube.com/watch?v=Zc_ufg4uW4U Data Manipulation Tools: dplyr -- Pt 3 Intro to the Grammar of Data Manipulation with R]
* Video: [https://www.youtube.com/watch?v=AuBgYDCg1Cg Working with Two Datasets: Binds, Set Operations, and Joins -- Pt 4 Intro to Data Manipulation]
''(These session materials contain repetition, this is intentional)''
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A student who has met the objectives of the session will be able to:
* Understand and apply the various <code>str_*()</code> functions for string manipulation
* Understand and apply the family of <code>*_join()</code> functions for combining data sets
* Understand and apply <code>pivot_wider()</code> and <code>pivot_longer()</code>
* Use factors in conjugation with plotting categorical data using ggplot
|-
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=== W05 - Monday Mar 2nd: Modelling, dimension reduction and clustering ===

{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://broom.tidyverse.org/ <code>broom</code>]
* [https://purrr.tidyverse.org/ <code>purrr</code>]
| style="vertical-align: top;" |
* [https://teaching.healthtech.dtu.dk/material/22100/05_recap_data_manipulation_II.html Recap: Data Manipulation II]
* [https://teaching.healthtech.dtu.dk/material/22100/05_lecture_mdl_dim_clstr.html Lecture]
* [https://teaching.healthtech.dtu.dk/material/22100/05_exercises_mdl_dim_clstr.html Exercises]
| style="vertical-align: top;" |
* Book Chapter(s): R4DS: [https://r4ds.had.co.nz/model-intro.html 22], [https://r4ds.had.co.nz/model-basics.html 23], [https://r4ds.had.co.nz/model-building.html 24] and [https://r4ds.had.co.nz/many-models.html 25]
* Video: [https://www.youtube.com/watch?v=7VGPUBWGv6g broom: Converting statistical models to tidy data frames]
* Video: [https://www.youtube.com/watch?v=cU0-NrUxRw4 PLOTCON 2016: Hadley Wickham, New open viz in R]
* Video: [https://www.youtube.com/watch?v=FgakZw6K1QQ StatQuest: Principal Component Analysis (PCA), Step-by-Step]
* Video: [https://www.youtube.com/watch?v=4b5d3muPQmA StatQuest: K-means clustering]
| style="vertical-align: top;" |
A student who has met the objectives of the session will be able to:
* Understand and apply simple <code>map()</code> functions for element-wise function application
* Understand and apply grouped supervised models to form nested model objects
* Understand and apply the <code>tidy()</code> function for tidying various model objects
* Perform a principal component analysis for dimension reduction of high dimensional data
* Perform an unsupervised k-means clustering of high dimensional data
|-
|}
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=== W06 - Monday Mar 9th: Scripting in a Reproducible and Collaborative Framework using GitHub via RStudio ===

{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://git-scm.com/ <code>git</code>]
| style="vertical-align: top;" |
* [https://teaching.healthtech.dtu.dk/material/22100/06_recap_mdl_dim_clstr.html Recap]
* [https://teaching.healthtech.dtu.dk/material/22100/06_lecture_git_scripting.html Lecture]
* [https://teaching.healthtech.dtu.dk/material/22100/06_exercises_git_scripting.html Exercises]
| style="vertical-align: top;" |
* Book Chapter: [https://rafalab.github.io/dsbook/git.html Introduction to Data Science - Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry: Chapter 39 Git and GitHub]
* Video: [https://www.youtube.com/watch?v=KjLycV1IWqc RStudio and Git - an Overview (Part 1)]
* Video: [https://www.youtube.com/watch?v=qcjpHFwCugE RStudio and Git - an Example (Part 2)]
| style="vertical-align: top;" |
A student who has met the objectives of the session will be able to:
* Explain why reproducible data analysis is important, as well as identify relevant challenges and explain replicability versus reproducibility
* Describe the components of a reproducible data analysis
* Use RStudio and github for collaborative analysis projects
|-
|}
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=== W07 - Monday Mar 16th: Artificial Neural Networks using Keras / Tensorflow in R ===

{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://tensorflow.rstudio.com/ <code>TensorFlow</code>]
* [https://keras.rstudio.com/ <code>Keras</code>]
| style="vertical-align: top;" |

* '''[https://github.com/leonjessen/RPharma2019 Click here to go to workshop]'''

* 08.00 - 08.10 Remote teaching setup and brief recap of git exercises
* 08.10 - 08.15 Brief talk: Introduction to Artificial Neural Networks
* 08.25 - 08.50 Exercise: Prototyping an ANN in R
* 08.50 - 08.55 Brief talk: Introduction to TensorFlow/Keras in R 1
* 08.55 - 09.15 Exercise: TensorFlow Playground
* 09.15 - 09.30 Brief talk: Introduction to TensorFlow/Keras in R 2
* 09.30 - 09.40 Brief talk: Session 1 Summary and Q&A
* 09.40 - 10.00 Coffee Break / Time buffer
* 10.00 - 10.30 Exercise: Hello Keras (Classification)
* 10.30 - 10.45 Brief talk: A bit more on Keras
* 10.45 - 11.15 Exercise: Predicting Price (regression)
* 11.15 - 11.45 Exercise: Deep Learning for Cancer Immunotherapy
* 11.45 - 12.00 Brief talk: Session 2 Summary and Q&A
| style="vertical-align: top;" |
* Video: [https://www.youtube.com/watch?v=atiYXm7JZv0 Machine Learning with R and TensorFlow] ''(See if you can guess who created the example on [https://blogs.rstudio.com/tensorflow/posts/2018-01-29-dl-for-cancer-immunotherapy/ "Deep Learning for Cancer Immunotherapy at 44:15")'']
* Spend remaining preparation time finishing the [https://teaching.healthtech.dtu.dk/material/22100/06_exercises_git_scripting.html#how_to_organise_a_project project organisation and git exercises from week 06]
| style="vertical-align: top;" |
A student who has met the objectives of the session will be able to:
* Train and apply a simple basic machine learning model based on a neural network with Keras / Tensorflow in R 
|-
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=== W08 - Monday Mar 23rd: Creating a simple R-package ===

{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://github.com/r-lib/devtools devtools]
* [https://github.com/r-lib/roxygen2 roxygen2]
* [https://github.com/r-lib/testthat testthat]
* [https://github.com/yihui/knitr knitr]
| style="vertical-align: top;" |
* '''Self-study:''' Using the site [https://support.rstudio.com/hc/en-us/articles/200486488-Developing-Packages-with-RStudio Developing Packages with RStudio], spend time equivalent to your preparation and in-class time to study how to create a simply R-package
* Remember, there is so much material available online, a quick google revealed [https://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/ this little example]
* Create a blank [https://rstudio.cloud/ RStudio Cloud Project] to use for studying the subject
* Use your [https://rforbiodatascience20.slack.com/ group Slack channel] <code>groupXX</code> to pose and answer questions and interact with your study group. I will monitor these channels ~8-18 on the session date.
* You are free to choose which function(s) you want to wrap in a package - A suggestion could be to create a set of functions to work programatically with DNA. Perhaps you want to be able transcribe, reverse, translate, etc.?
* Look into including data in your package, perhaps you want your users to be able to access the [https://www.ncbi.nlm.nih.gov/Class/FieldGuide/BLOSUM62.txt BLOSUM62] matrix?
* Remember to not only create the functions, but also work with creating the documentation around it, so that users can get help by typing, as per usual, <code>?your_function_name</code> in the console
| style="vertical-align: top;" |
* None
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A student who has met the objectives of the session will be able to:
* Prepare a '''simple''' R package for distributing documented functions
* Using relevant online ressources to independently obtain new and expand on existing knowledge of <code>R</code>
|-
|}
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=== W09 - Monday Mar 30th: Creating a simple Shiny application ===

{| class="wikitable" style="font-size:100%;"
! Package(s)
! Schedule
! Learning Materials
! Session Learning Objectives
|-
| style="vertical-align: top;" |
* [https://shiny.rstudio.com/ shiny]
| style="vertical-align: top;" |
* '''Self-study:''' Using the book [https://mastering-shiny.org/ Mastering Shiny by Hadley Wickham], spend time equivalent to your preparation and in-class time to study how to create a simply shiny application
* Here is a nice primer on [https://shiny.rstudio.com/articles/basics.html Shiny basics]
* Briefly on shiny: Think of shiny as a way to connect your data to a pointy-clicky interface, so that non-data users may interact with the data
* Create a blank [https://rstudio.cloud/ RStudio Cloud Project] to use for studying the subject
* Use your [https://rforbiodatascience20.slack.com/ group Slack channel] <code>groupXX</code> to pose and answer questions and interact with your study group. I will monitor these channels ~8-18 on the session date.
* You are free to choose what you want to present using your <code>shiny</code> app. You could continue working with the package of DNA functions from last week. If you are interested in sequence logos, I can recommend looking into [https://omarwagih.github.io/ggseqlogo/ <code>ggseqlogo</code>]
* Investigate how you can use [https://www.shinyapps.io/ shinyapps.io] to publish your <code>shiny</code> app - Here is a small [https://leonjessen.shinyapps.io/nnvizRt/ example of a <code>shiny</code> app], that I have created
* Your end-product for the day is a '''simple''' functional shiny server published on [https://www.shinyapps.io/ shinyapps.io] - Send me the link to the server in a personal slack message. If circumstances do not allow you to finish, then that is fine, but do try to see if you can get it working.
| style="vertical-align: top;" |
* None
| style="vertical-align: top;" |
A student who has met the objectives of the session will be able to:
* Prepare a '''simple''' shiny application for distributing interactive data exploration
* Using relevant online ressources to independently obtain new and expand on existing knowledge of <code>R</code>
|-
|}

=== Wnn - Monday Apr 6th: Happy Easter Holidays ===

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=== Wnn - Monday Apr 13th: Happy Easter Holidays ===

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=== W10-11-12-13 - Monday Apr 20th, Apr 27th, May 4th, May 11th: Project Work ===

* '''[https://teaching.healthtech.dtu.dk/material/22100/00_exam_description.html Description of Project and Exam]'''
* '''[https://docs.google.com/spreadsheets/d/17NQiqdyshyL8Hl5abFalIsGCSeWGTEogv7xq0tuZAOg/edit#gid=0 Add your groups here]'''
* Now is the time to put everything you learned to use
* In groups of 4 students (remember you have to form these yourself), you are to prepare a project (See above description)
* Every '''Monday''', each group will have a project-supervision meeting with me according to the below schedule
* This year due to the situation, each meeting will take place using skype (My skype ID is: jessenleon)
* It's a tight schedule and each group has ~20 minutes, so in the groups, be sure to prepare any questions you may have prior to the meeting

{| class="wikitable" style="font-size:100%;"
! Time
! Group
|-
| style="vertical-align: top;" |
* 08.00 - 08.19
* 08.20 - 08.39
* 08.40 - 08.59
* 09.00 - 09.19
* 09.20 - 09.39
* 09.40 - 09.59
* 10.00 - 10.19
* 10.20 - 10.39
* 10.40 - 10.59
* 11.00 - 11.19
* 11.20 - 11.39

| style="vertical-align: top;" |
* 1
* 2
* 3
* 4
* 5
* break
* 6
* 7
* 8
* 9
* 10
|-
|}
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=== Wnn - Thursday May 14th and Friday May 15th: Exam Day ===
[https://teaching.healthtech.dtu.dk/material/22100/00_exam_description.html Description of Project and Exam]

=== 2020 Exam Schedule ===

==== Thursday May 14th (Ordinary Spring F1A) ====
* 09.00 - 10.00 Group 8
* 10.00 - 11.00 Group 4
* 11.00 - 12.00 Group 10
* 12.00 - 13.00 Lunch break
* 13.00 - 14.00 Group 5
* 14.00 - 15.00 Group 6

==== Friday May 15th (Extra Exam Day) ====
* 09.00 - 10.00 Group 1
* 10.00 - 11.00 Group 3
* 11.00 - 12.00 Group 9
* 12.00 - 13.00 Lunch break
* 13.00 - 14.00 Group 7
* 14.00 - 15.00 Group 2