If you use or take inspiration from this repository please cite with this link: santurini/DAG-Linkage-Analysis-in-R
Your support will be truly appreciated and feel free to contact me at my following links or just send me an email:
In this work we implemented to universal hypothesis test to check whether a link or a path existed or not in a directed acyclic graph. First we checked the properties of the tests and then we applied both on a real case study.
scripts this folder contains the scripts:
- functions.R which contains all the functions to run the scripts
- LinkageTest.R which contains the implementation of the test for a single link
- PathwayTest.R which contains the implementation of the test for a directed pathway
- Cytometry.R which contains the application of the tests on real data
- plotSparsity.R which contains the script fro the scatter plots of the test results with different sparsity values
report this folder contains the final report of the case study:
- Report.html is the knitted html file written in rmarkdown
data is a folder which contains:
- cytometry-data.xlsx the excel file with 9 different sheets
The aim of this repository is to implement the Universal Hypothesis Test that is explained in the following image:
This is the definition we used to implement the graph linkage test:
This is the definition we used to implement the graph pathway test:
To be able to implement it we used the clrdag package which contains the MLEdag function, the linck to the repository is the following:
Once implemented we tested them on real world data regarding Cell Signaling represented as a DAG:
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Estimating the variance and log-likelihood:
- Sigma estimate:
- log-Likelihood estimate:
- Sigma estimate:
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Implementing the LRT (Likelihood ratio test) and Crossfit test
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Implementing the Universal tests on random data to compute size and power
- Formula to generate the random data:
- Formula to generate the random data:
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Applying the test on real world data to check linkages between proteins
