causaldag
pgmpy
| causaldag | pgmpy | |
|---|---|---|
| 1 | 2 | |
| 133 | 2,627 | |
| 0.0% | 1.1% | |
| 0.0 | 8.0 | |
| about 1 year ago | 19 days ago | |
| JavaScript | Python | |
| GNU General Public License v3.0 or later | MIT License |
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causaldag
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Any methods or tools for virtual gene knock-out in single cell RNA seq data?
I am interested in finding out bioinformatically, a causal relationship between an upstream gene (Notch2) and a transcription factor downstream. Is there any other tool other than scTenifoldpy, to perform a virtual knock-down of genes of interest and see which other genes are affected? Is there also any other tool than causaldag that can help infer causal relationships between gene expressions?
pgmpy
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Use data from tables generated in python console,
No need to post the help, here is the DiscreteFactor class https://github.com/pgmpy/pgmpy/blob/eb65f40d2b32bf2ad971181333bb9ed7aefde907/pgmpy/factors/discrete/DiscreteFactor.py
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[D] Python toolboxes for probabilistic graphical model inference
I do know of a few promising toolboxes such as pgmpy, pymc3, and pyro, but have not used either of them (for this purpose) and am at a bit of a loss picking one to start with.
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
scTenifoldpy - A python package implements scTenifoldnet and scTenifoldknk
statsmodels - Statsmodels: statistical modeling and econometrics in Python
ims - 📚 Introduction to Modern Statistics - A college-level open-source textbook with a modern approach highlighting multivariable relationships and simulation-based inference. For v1, see https://openintro-ims.netlify.app.
scikit-learn - scikit-learn: machine learning in Python
CausalPy - A Python package for causal inference in quasi-experimental settings
rustworkx - A high performance Python graph library implemented in Rust.
pyhf - pure-Python HistFactory implementation with tensors and autodiff
Lottery-Simulation - This program can simulate a number of drawings in the Lottery (6 out of 49). The guesses and the draws are chosen randomly and the user can choose how many right guesses there should be (0-6). Then the program will run through the simulation as many times as it takes to get the exact number of correct guesses the user chose. The user can also choose how many times this should be repeated (the higher the number, the more accurate the result will be). Then the program will automatically calculate the average number of tries it took to get the chosen number of correct guesses and tell the user the chance of getting this certain number of correct guesses.
dodiscover - [Experimental] Global causal discovery algorithms
generalized-additive-models - Generalized Additive Models in Python.