Log In Sign Up
Models:
Alyssa Salazar
AlyssaS/
CompgenCourse2025module3
0
Downloads: 1
Diff of /README.md [59ff71] .. [06edaf]

Switch to unified view

a/README.md b/README.md
1 
Download this file
 1 
Compgen Course 2025
2 

  2
  
===================





  3
  
141 lines (85 with data), 5.9 kB

  3
  





  4
  
Compgen Course 2025

  
  





  5
  
Here I collect relevant course material for the Computational Genomics Course (10-16 March 2025), Module 3. In this module, we learn about how to use deep learning models to integrate multi-omics data in the context of precision oncology applications.

  4
  
Here I collect relevant course material for the Computational Genomics Course (10-16 March 2025), Module 3.





  
  

  5
  
In this module, we learn about how to use deep learning models to integrate multi-omics data in the context of precision oncology applications.





  6
  

  6
  





  7
  
Info

  7
  
Info





  
  

  8
  
===================





  
  

  9
  





  
  

  10
  





  8
  
In this course, we are using two resources to organize the course:

  11
  
In this course, we are using two resources to organize the course:





  9
  

  12
  





  10
  
The Google Classroom for private course-related information, coursework, meeting announcements: Google Classroom

  13
  
1. **The Google Classroom** for private course-related information, coursework, meeting announcements:





  
  

  14
  
   `Google Classroom <https://classroom.google.com/c/NzQ5MTExMDU2Njkz>`_





  11
  

  15
  





  12
  
If you have to share private information such as your email address, please use Google Classroom. For any other problem that you run into, please use the GitHub Discussions (see below).

  16
  
   If you have to share private information such as your email address, please use Google Classroom.





  
  

  17
  
   For any other problem that you run into, please use the GitHub Discussions (see below).





  13
  

  18
  





  14
  
GitHub discussions on this repository to provide help to each other: GitHub Discussions

  19
  
2. **GitHub discussions** on this repository to provide help to each other:





  
  

  20
  
   `GitHub Discussions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/discussions>`_





  15
  

  21
  





  16
  
We have created different categories for potential issues you may come across. Please try to use the relevant category to open a discussion. Before opening a new discussion topic, please check if something similar is already open. If you know the answer to a question someone else raised, feel free to help your classmates! We appreciate your support.

  22
  
   We have created different categories for potential issues you may come across.





  
  

  23
  
   Please try to use the relevant category to open a discussion.





  
  

  24
  
   Before opening a new discussion topic, please check if something similar is already open.





  
  

  25
  
   If you know the answer to a question someone else raised, feel free to help your classmates! We appreciate your support.





  17
  

  26
  





  
  

  27
  





  18
  
Course Material

  28
  
Course Material





  19
  
Here is the course material we have developed during the workshop. Feel free to share and re-use.

  29
  
======================





  
  

  30
  





  
  

  31
  
Here is the course material we have developed during the workshop. Feel free to share and re-use. 





  20
  

  32
  





  21
  
Day-1:

  33
  
1. Day-1: 





  22
  
Slides

  34
  





  23
  
Live session

  35
  
   - `Slides <https://docs.google.com/presentation/d/1Z3m8JOQY0JidM7gIJNFWaOfCaTH-rU47y4zCu5Bk6mE/edit?usp=sharing>`_





  24
  
Homework

  36
  
   - `Live session <https://youtu.be/7QxRqhFDJiY?feature=shared>`_





  25
  
Homework solutions

  37
  
   - `Homework <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/homeworks/hw1>`_





  26
  
Day 2:

  38
  
   - `Homework solutions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/blob/main/solutions/day1_hw_brca_subtypes_solutions.ipynb>`_





  
  

  39
  





  
  

  40
  
2. Day 2: 





  27
  
Slides

  41
  





  28
  
Live session

  42
  
   - `Slides <https://docs.google.com/presentation/d/1a31RoNIiZYdZFL9cc4OZ3TpgBGrk1IH1brW9VeHo3dQ/edit?usp=sharing>`_





  29
  
Homework

  43
  
   - `Live session <https://youtu.be/CjTjcu_k2EI?feature=shared>`_





  30
  
Homework solutions

  44
  
   - `Homework <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/homeworks/hw2>`_





  31
  
Day 3:

  45
  
   - `Homework solutions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/blob/main/solutions/day2_hw_lgg_gbm_solutions.ipynb>`_ 





  32
  
Slides

  46
  





  33
  
Live session

  47
  
3. Day 3: 





  34
  
Homework

  48
  





  35
  
Homework solutions

  49
  
   - `Slides <https://docs.google.com/presentation/d/1OvXK4H5W7qbD4jeru8pwnkQdiGz0RfjrW4Omd8kd0dg/edit?usp=sharing>`_





  36
  
Day 4:

  50
  
   - `Live session <https://youtu.be/WM4VkjFHOwI?feature=shared>`_





  37
  
Live session

  51
  
   - `Homework <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/homeworks/hw3>`_





  38
  
We didn't use any slides and we didn't have any more homeworks.

  52
  
   - `Homework solutions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/solutions/hw3>`_ 





  39
  
Compute Environment

  53
  





  40
  
Cloud - Rolv.io

  54
  
4. Day 4: 





  41
  
You will be provided usernames and passwords to access the rolv.io platform, which comes with prebuilt packages that you will need throughout the course. With this option, you won't need to install any software yourself.

  55
  





  42
  

  56
  
   - `Live session <https://youtu.be/jYzKw4rF-ck?feature=shared>`_





  43
  
Please check your email that you signed up for the course, use your credentials to sign in: Rolv.io Platform. Then, click on Compute -> Launch -> Launch JupyterLab. Wait for the session to be ready (takes a few minutes). There you will have a JupyterLab environment with all packages installed. In this session, you can also use the terminal.

  57
  
   - We didn't use any slides and we didn't have any more homeworks. 





  44
  

  58
  
   





  45
  
Important Note: Each session you create on Rolv is limited to a total of 3 hours. Please make sure to backup your work before terminating your session. We recommend creating a github repo and have a backup of your work there.

  59
  





  46
  

  60
  
Compute Environment





  47
  
Docker Desktop

  61
  
===================





  48
  
We have also built a Docker image that contains the tools you will need. To be able to use this, you need Docker Desktop, which you can install from here: Docker Desktop.

  62
  





  49
  

  63
  
Cloud - Rolv.io





  50
  
After you install Docker, open a terminal and execute the following code to open a JupyterLab session with all the tools you need:

  64
  
---------------





  51
  

  65
  





  52
  
docker pull borauyar/flexynesis_image:latest

  66
  
You will be provided usernames and passwords to access the `rolv.io` platform, which comes with prebuilt packages that you will need throughout the course. With this option, you won't need to install any software yourself.





  53
  
docker run -it -p 8888:8888 borauyar/flexynesis_image

  67
  





  54
  
jupyter lab --ip=0.0.0.0 --no-browser --allow-root

  68
  
Please check your email that you signed up for the course, use your credentials to sign in: `Rolv.io Platform <https://platform.dev.cloud.rolv.io/>`_.





  55
  
This will create a link that looks like this:

  69
  
Then, click on **Compute -> Launch -> Launch JupyterLab**. Wait for the session to be ready (takes a few minutes).





  56
  

  70
  
There you will have a JupyterLab environment with all packages installed.





  57
  
http://127.0.0.1:8888/lab?token=<.......>

  71
  
In this session, you can also use the **terminal**.





  58
  
Copy-paste that link into your browser to open a JupyterLab session.

  72
  





  59
  

  73
  
+++++++++++++++++++++





  60
  
Mamba/pip

  74
  





  61
  
If you want to have more control over your system and you know what you are doing, you can also install flexynesis on your system using pip.

  75
  
**Important Note**: Each session you create on Rolv is **limited** to a **total of 3 hours**. 





  62
  

  76
  
Please make sure to **backup your work** before terminating your session. 





  63
  
mamba create -n flexenv python==3.11

  77
  
We recommend creating a github repo and have a backup of your work there. 





  64
  
mamba activate flexenv

  78
  





  65
  
pip install flexynesis jupyterlab snakemake

  79
  
+++++++++++++++++++++





  66
  
jupyter lab --ip=0.0.0.0 --no-browser --allow-root

  80
  





  67
  
This will create a link that looks like this:

  81
  
Docker Desktop





  68
  

  82
  
---------------





  69
  
http://127.0.0.1:8888/lab?token=<.......>

  83
  





  70
  
Copy-paste that link into your browser to open a JupyterLab session.

  84
  
We have also built a Docker image that contains the tools you will need.





  71
  

  85
  
To be able to use this, you need Docker Desktop, which you can install from here: `Docker Desktop <https://www.docker.com/products/docker-desktop/>`_.





  72
  
Further Learning

  86
  





  73
  
Here are some resource I found useful:

  87
  
After you install Docker, open a terminal and execute the following code to open a JupyterLab session with all the tools you need:





  74
  

  88
  





  75
  
Fastai: https://course19.fast.ai/part2

  89
  
.. code-block:: bash





  76
  
Pytorch: https://pytorch.org/tutorials/index.html

  90
  





  77
  
Lightning: https://www.datacamp.com/tutorial/pytorch-lightning-tutorial

  91
  
   docker pull borauyar/flexynesis_image:latest





  78
  
Pytorch-Geometric for GNNs: https://pytorch-geometric.readthedocs.io/en/latest/

  92
  
   docker run -it -p 8888:8888 borauyar/flexynesis_image





  79
  
Graph Neural Networks: https://www.youtube.com/watch?v=fOctJB4kVlM&list=PLV8yxwGOxvvoNkzPfCx2i8an--Tkt7O8Z&ab_channel=DeepFindr

  93
  
   jupyter lab --ip=0.0.0.0 --no-browser --allow-root





  80
  
Elements of statistical learning (Rob Tibshirani, Trevor Hastie): https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ&ab_channel=StanfordOnline

  94
  





  81
  
Computational Genomics in R (Akalin, Franke, Ronen, Uyar): https://compgenomr.github.io/book/

  95
  
This will create a link that looks like this:





  
  

  96
  





  
  

  97
  
   http://127.0.0.1:8888/lab?token=<.......>





  
  

  98
  





  
  

  99
  
Copy-paste that link into your browser to open a JupyterLab session.





  
  

  100
  





  
  

  101
  
Mamba/pip





  
  

  102
  
---------------





  
  

  103
  





  
  

  104
  
If you want to have more control over your system and you know what you are doing, you can also install **flexynesis** on your system using `pip`.





  
  

  105
  





  
  

  106
  
.. code-block:: bash





  
  

  107
  





  
  

  108
  
   mamba create -n flexenv python==3.11





  
  

  109
  
   mamba activate flexenv





  
  

  110
  
   pip install flexynesis jupyterlab snakemake





  
  

  111
  
   jupyter lab --ip=0.0.0.0 --no-browser --allow-root





  
  

  112
  





  
  

  113
  
This will create a link that looks like this:





  
  

  114
  





  
  

  115
  
   http://127.0.0.1:8888/lab?token=<.......>





  
  

  116
  





  
  

  117
  
Copy-paste that link into your browser to open a JupyterLab session.





  
  

  118
  





  
  

  119
  
Further Learning





  
  

  120
  
===================





  
  

  121
  





  
  

  122
  
Here are some resource I found useful: 





  
  

  123
  





  
  

  124
  
- Fastai: https://course19.fast.ai/part2





  
  

  125
  
- Pytorch: https://pytorch.org/tutorials/index.html





  
  

  126
  
- Lightning: https://www.datacamp.com/tutorial/pytorch-lightning-tutorial





  
  

  127
  
- Pytorch-Geometric for GNNs: https://pytorch-geometric.readthedocs.io/en/latest/ 





  
  

  128
  
- Graph Neural Networks: https://www.youtube.com/watch?v=fOctJB4kVlM&list=PLV8yxwGOxvvoNkzPfCx2i8an--Tkt7O8Z&ab_channel=DeepFindr





  
  

  129
  
- Elements of statistical learning (Rob Tibshirani, Trevor Hastie): https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ&ab_channel=StanfordOnline





  
  

  130
  
- Computational Genomics in R (Akalin, Franke, Ronen, Uyar): https://compgenomr.github.io/book/





  
  

  131
  





  
  

  132
  





  
  

  133
  





  
  

  134
  





  
  

  135
  





  
  

  136
  





  
  

  137
  





  
  

  138
  





  
  

  139
  





  
  

  140