Difference between revisions of "Tutorials"
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* <s>[[Tutorials#Annotation|ANNOVAR Annotation]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/annovar.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/annovar/tags docker image]</s> | * <s>[[Tutorials#Annotation|ANNOVAR Annotation]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/annovar.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/annovar/tags docker image]</s> | ||
* <s>[[Tutorials#Cochran_Armitage_Trend_Test|Cochran Armitage Trend Test for GWAS power analysis]]</s> | * <s>[[Tutorials#Cochran_Armitage_Trend_Test|Cochran Armitage Trend Test for GWAS power analysis]]</s> | ||
| − | * [[Tutorials#FastLMM | Family-based Association using FaST-LMM, PLINK and R]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image] | + | * <s>[[Tutorials#FastLMM | Family-based Association using FaST-LMM, PLINK and R]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]</s> |
* <s>[[Tutorials#IGV|IGV]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/igv.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/igv/tags docker image]</s> | * <s>[[Tutorials#IGV|IGV]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/igv.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/igv/tags docker image]</s> | ||
* <s>[[Tutorials#Gemini|Gemini]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/gemini.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/gemini/tags docker image]</s> | * <s>[[Tutorials#Gemini|Gemini]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/gemini.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/gemini/tags docker image]</s> | ||
| − | * [[Tutorials# | + | * <s>[[Tutorials#Fast-LMM & GCTA | Fast-LMM & GCTA]] | [ exercise] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]</s> |
| − | * [[Tutorials#GWAS:_Data_Quality_Control|GWAS: QC]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/plink.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/ | + | * [[Tutorials#GWAS:_Data_Quality_Control|GWAS: QC]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/plink.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta docker image] |
* [[Tutorials#GWAS:_Association_Analysis_Controlling_for_Population_Substructure|GWAS: Controlling for Population Substructure]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/plink.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/plink/tags docker image] | * [[Tutorials#GWAS:_Association_Analysis_Controlling_for_Population_Substructure|GWAS: Controlling for Population Substructure]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/plink.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/plink/tags docker image] | ||
* Mendelian Randomization | docker image and exercise | * Mendelian Randomization | docker image and exercise | ||
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* <s>[[Tutorials#Variant_Association_Tools|Variant Association Tools]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/vat.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/vat/tags docker image]</s> | * <s>[[Tutorials#Variant_Association_Tools|Variant Association Tools]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/vat.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/vat/tags docker image]</s> | ||
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| + | == == | ||
==Running Tutorials on Your Computer== | ==Running Tutorials on Your Computer== | ||
| − | + | We adopt [https://www.docker.com/ docker] to run our course material, and we have created various [https://hub.docker.com/u/statisticalgenetics docker repositories] with freely available Docker images for users to readily run our exercises on their own computers using [https://docs.docker.com/desktop/ Docker Desktop]. These docker images are for multi-platform use (Mac, Windows, or Linux). More information regarding instructions to install Docker Desktop and to run the course material on your computer is detailed in the documentation below. | |
===General instructions=== | ===General instructions=== | ||
| − | * [https://github.com/statgenetics/statgen-courses/wiki/How-to-launch-course-tutorials#alternative-to-cloud-server-use-your-own-computer Instructions | + | * [https://github.com/statgenetics/statgen-courses/wiki/How-to-launch-course-tutorials#alternative-to-cloud-server-use-your-own-computer Instructions to install Docker Desktop and running the course material on your computer]<br /><br /> |
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| − | + | ||
===Preparing Your Computer=== | ===Preparing Your Computer=== | ||
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Material and instructions for specific exercise are listed in each section below (''only those using statgen-setup command are relevant to our docker based tutorials''). They provide links to materials and a minimal set of commands to use for launching and running an exercise. Command "statgen-setup login" will allow you to start and login to a Linux command line environment (regardless of your current computational environment) to perform all analysis in command shells. This works for all the tutorials on this page. Additionally, some tutorials support command "statgen-setup launch" which will start a JupyterLab server to perform the analysis. | Material and instructions for specific exercise are listed in each section below (''only those using statgen-setup command are relevant to our docker based tutorials''). They provide links to materials and a minimal set of commands to use for launching and running an exercise. Command "statgen-setup login" will allow you to start and login to a Linux command line environment (regardless of your current computational environment) to perform all analysis in command shells. This works for all the tutorials on this page. Additionally, some tutorials support command "statgen-setup launch" which will start a JupyterLab server to perform the analysis. | ||
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| − | + | ==FastLMM & GCTA== | |
| − | + | * [file:///https://statgen.us/files/fastlmm_gcta_exercise.pdf FastLMM & GCTA exercise ] | |
| + | * [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta FastLMM & GCTA docker image] | ||
| + | To pull and download the docker image from the Docker Hub repository, | ||
| + | <pre>docker pull statisticalgenetics/fastlmm-gcta</pre> | ||
| + | To run the exercise from the downloaded docker image, | ||
| + | <pre>docker run --rm -p 8888:8888 statisticalgenetics/fastlmm-gcta</pre> | ||
==Fine-mapping (SuSiE method)== | ==Fine-mapping (SuSiE method)== | ||
* [https://github.com/statgenetics/statgen-courses/blob/master/handout/finemapping.docx susieR Exercise <nowiki>[DOCX]</nowiki>] | * [https://github.com/statgenetics/statgen-courses/blob/master/handout/finemapping.docx susieR Exercise <nowiki>[DOCX]</nowiki>] | ||
Revision as of 18:05, 17 August 2026
Contents
- 1 Tutorials for Courses
- 2
- 3 Running Tutorials on Your Computer
- 4 FastLMM & GCTA
- 5 Fine-mapping (SuSiE method)
- 6 GCTA
- 7 Gemini
- 8 Genehunter
- 9 GWAS: Data Quality Control
- 10 GWAS: Association Analysis Controlling for Population Substructure
- 11 Homozygosity Mapper
- 12 IGV
- 13 Linkage/FastLinkage
- 14 LD clumping
- 15 MR-JTI for TWAS
- 16 Pleiotropy
- 17 Polygenic risk prediction (NPS method)
- 18 Polygenic risk prediction (LDpred2 method)
- 19 Population Genetics
- 20 PSEQ
- 21 R and PLINK
- 22 REGENIE
- 23 Regression
- 24 RV-TDT
- 25 SEQLinkage
- 26 SEQSpark
- 27 SLINK
- 28 SUPERLINK
- 29 Variant Association Tools
Tutorials for Courses
-
ANNOVAR Annotation | dockefile and docker image -
Cochran Armitage Trend Test for GWAS power analysis -
Family-based Association using FaST-LMM, PLINK and R | dockefile and docker image -
IGV | dockerfile and docker image -
Gemini | dockerfile and docker image -
Fast-LMM & GCTA | [ exercise] and docker image - GWAS: QC | dockerfile and docker image
- GWAS: Controlling for Population Substructure | dockerfile and docker image
- Mendelian Randomization | docker image and exercise
-
Interaction analysis using PLINK and CASSI | dockerfile and docker image -
LINKAGE | dockerfile and docker image -
Population Genetics | dockefile and docker image - Pleiotropy | dockefile and docker image
-
Polygenic risk prediction using non-parametric shrinkage | dockefile and docker image -
PLINK/SEQ (PSEQ) | dockefile and docker image - R and PLINK exercise | dockefile and docker image
- Regression | dockefile and docker image
-
RV-TDT -
SEQLinkage -
SEQSpark -
SLINK | dockerfile and docker image -
Variant Association Tools | dockerfile and docker image
Running Tutorials on Your Computer
We adopt docker to run our course material, and we have created various docker repositories with freely available Docker images for users to readily run our exercises on their own computers using Docker Desktop. These docker images are for multi-platform use (Mac, Windows, or Linux). More information regarding instructions to install Docker Desktop and to run the course material on your computer is detailed in the documentation below.
General instructions
Preparing Your Computer
Running Exercises
Tutorial specific instructions
We use a script "statgen-setup" to start the docker based environments for these tutorials. Please refer to the previous section for instructions on the installation of this script.
Material and instructions for specific exercise are listed in each section below (only those using statgen-setup command are relevant to our docker based tutorials). They provide links to materials and a minimal set of commands to use for launching and running an exercise. Command "statgen-setup login" will allow you to start and login to a Linux command line environment (regardless of your current computational environment) to perform all analysis in command shells. This works for all the tutorials on this page. Additionally, some tutorials support command "statgen-setup launch" which will start a JupyterLab server to perform the analysis.
FastLMM & GCTA
To pull and download the docker image from the Docker Hub repository,
docker pull statisticalgenetics/fastlmm-gcta
To run the exercise from the downloaded docker image,
docker run --rm -p 8888:8888 statisticalgenetics/fastlmm-gcta
Fine-mapping (SuSiE method)
- susieR Exercise [DOCX]
- susieR Exercise Answers [DOCX]
- susieR Exercise [Ipython notebook]
- susieR Exercise Answers [Ipython notebook]
To run the exercise from docker image provided,
statgen-setup launch --tutorial finemap
GCTA
To run the exercise from docker image provided,
statgen-setup login --tutorial fastlmm-gcta
Gemini
To run the exercise from docker image provided,
statgen-setup login --tutorial gemini
Genehunter
To install from packages, follow the configuration steps above and run the following command.
sudo apt-get install genehunter-tutorial
The exercise's files will then be installed in the folder /home/shared/genehunter. You can run from there or copy the files into your user's home directory and proceed with the exercise.
GWAS: Data Quality Control
To run the exercise via JupyterLab from docker image provided,
statgen-setup launch --tutorial plink
Alternatively, you can opt to run the exercise via command terminal from docker image provided,
statgen-setup login --tutorial plink
GWAS: Association Analysis Controlling for Population Substructure
To run the exercise via JupyterLab from docker image provided,
statgen-setup launch --tutorial plink
Alternatively, you can opt to run the exercise via command terminal from docker image provided,
statgen-setup login --tutorial plink
Homozygosity Mapper
IGV
Linkage/FastLinkage
To run the exercise from docker image provided,
statgen-setup login --tutorial mlink
LD clumping
To run the exercise from docker image provided,
statgen-setup launch --tutorial clumping
MR-JTI for TWAS
To run the exercise from docker image provided,
statgen-setup launch --tutorial twas
Pleiotropy
To run the exercise from docker image provided,
statgen-setup login --tutorial pleiotropy
Polygenic risk prediction (NPS method)
To run the exercise from docker image provided,
statgen-setup login --tutorial nps
Polygenic risk prediction (LDpred2 method)
To run the exercise from docker image provided,
statgen-setup launch --tutorial ldpred2
Then follow prompts on the terminal output to open up the JupyterLab server in your web browser. If it is the first time you start this server, please open a command terminal inside JupyterLab, and type
get-data
to load the data-set to the JupyterLab workspace.
Population Genetics
To run the exercise from docker image provided,
statgen-setup login --tutorial popgen
PSEQ
To run the exercise from docker image provided,
statgen-setup launch --tutorial pseq
Notice that since PSEQ exercise does not involve generating and visualizing plots, it is also fine to use a command terminal, instead of the JupyterLab server, to run this exercise and reproduce exactly what was described in the tutorial. To do so,
statgen-setup login --tutorial pseq
R and PLINK
To run the exercise from docker image provided,
statgen-setup launch --tutorial plink-r-nothnagel
REGENIE
To run the exercise from docker image provided,
statgen-setup launch --tutorial regenie
Then follow prompts on the terminal output to open up the JupyterLab server in your web browser. If it is the first time you start this server, please open a command terminal inside JupyterLab, and type
get-data
to load the data-set to the JupyterLab workspace.
Regression
To run the exercise from docker image provided,
statgen-setup login --tutorial regression
RV-TDT
Installing Packages
To install from packages, follow the configuration steps above and run the following command.
sudo apt-get install rvtdt-tutorial
The exercise's files will then be installed in the folder /home/shared/rvtdt. You can run from there or copy the files into your user's home directory and proceed with the exercise.
SEQLinkage
To install from packages, follow the configuration steps above and run the following command.
sudo apt-get install seqlinkage-tutorial
The exercise's files will then be installed in the folder /home/shared/seqlinkage. You can run from there or copy the files into your user's home directory and proceed with the exercise.
SEQSpark
Installing Packages
To install from packages, follow the configuration steps above and run the following command.
sudo apt-get install seqspark-tutorial
The exercise's files will then be installed in the folder /home/shared/seqspark. You can run from there or copy the files into your user's home directory and proceed with the exercise.In order for the commands to work correctly, you don't need to reboot, but you should log out and log back in to make sure that the computer's environment is correctly configured.
SLINK
To run the exercise from docker image provided,
statgen-setup login --tutorial slink
SUPERLINK
Variant Association Tools
To run the exercise from docker image provided,
statgen-setup launch --tutorial vat
Then follow the prompts on the terminal output to open up the JupyterLab server in your web browser. You should find the exercise notebook in the side panel, and you can click to open it.
