Add effect/pvalue selectors to ExpressionAPI() #103
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) This should only allow pulls from a branch called `dev` or `patch` directly to main. otherwise, pull requests will be required to be against `dev` Adding the `use_cache` option to AbstractRecordAndFilesAPI(). Also did some `dev` branch maintenance to rebase onto the curren state of `main` removing use_cache setting; cache_key updated to include effect and pvalue colnames
* adding perturbation response relationship tutorial * addressed the changes to the notebook: added analysis to all graphs, typehinting and docstrings to methods, and other misc changes * added new methods and analysis to enable comparison between different dataset combinations. Added everything under the last subtitle, with each section having smaller subheadings underneath to group everything * Calculate variance explained (BrentLab#88) * fixed adjustment function so its based on enrichment strength * added new file util.py and new test suite and updated notebook * Update pyproject.toml This is already in the dev dependencies. I forgot to go over that. To add 'production' depdencies with python, you add to the default dependencies section with just: ``` poetry add <package> ``` You can also add dependencies to a group, eg: ``` poetry add --group dev <package> ``` See https://python-poetry.org/docs/cli/#options-4 That way, you can control what dependencies get installed. For a typical user, I don't think we'll want to install jupyter in the environment. They should have jupyter in their environment, and then install yeastdnnexplorer into it. * parameterizing the max_adjustment value and adding the calculate_variance_explained function and test suite * removing the function and test suite for calculating the variance explained and adding the function to the visualizing_and_testing_data_generation_methods notebook * Added docstrings and typehinting, removed unnecessary work and added exposition to graphs and methods * updated notebook to use sphinx docstrings, added headings and subheadings and improved exposition --------- Co-authored-by: Eric Jia <ericjia@Erics-MBP-2.attlocal.net> Co-authored-by: Chase Mateusiak <chasem@wustl.edu> * fixed adjustment function so its based on enrichment strength (BrentLab#86) Co-authored-by: Eric Jia <ericjia@Erics-MBP-2.attlocal.net> * Database Interface (BrentLab#90) * adding new file for explanation * adding ParamsDict * init implementation of the API classes. Documentation and some testing included. RankResponse is not, and the testing is minimal due to the difficulty of testing futures * adding some words to the project ignore settings * rank response api working * addressing unused imports in RankResponseAPI * updating the database_interface notebook for the new database backend; addressing logging warning on instantiation * updating the tutorial to show how to use the aggregated data (BrentLab#91) * table data retrieved as gzip; addtiional columns now present from DB * Update README.md closes BrentLab#81 * Adding update to manualqc (BrentLab#96) * removing new file, part of a demo * adding update() method to bindingmanualqc; added _delimiter_detect method to AbstractRecords * addressing pre-commit issues * This is getting the dev branch rebased onto the main branch (BrentLab#100) * Calculate variance explained (BrentLab#88) * fixed adjustment function so its based on enrichment strength * added new file util.py and new test suite and updated notebook * Update pyproject.toml This is already in the dev dependencies. I forgot to go over that. To add 'production' depdencies with python, you add to the default dependencies section with just: ``` poetry add <package> ``` You can also add dependencies to a group, eg: ``` poetry add --group dev <package> ``` See https://python-poetry.org/docs/cli/#options-4 That way, you can control what dependencies get installed. For a typical user, I don't think we'll want to install jupyter in the environment. They should have jupyter in their environment, and then install yeastdnnexplorer into it. * parameterizing the max_adjustment value and adding the calculate_variance_explained function and test suite * removing the function and test suite for calculating the variance explained and adding the function to the visualizing_and_testing_data_generation_methods notebook * Added docstrings and typehinting, removed unnecessary work and added exposition to graphs and methods * updated notebook to use sphinx docstrings, added headings and subheadings and improved exposition --------- Co-authored-by: Eric Jia <ericjia@Erics-MBP-2.attlocal.net> Co-authored-by: Chase Mateusiak <chasem@wustl.edu> * fixed adjustment function so its based on enrichment strength (BrentLab#86) Co-authored-by: Eric Jia <ericjia@Erics-MBP-2.attlocal.net> * Database Interface (BrentLab#90) * adding new file for explanation * adding ParamsDict * init implementation of the API classes. Documentation and some testing included. RankResponse is not, and the testing is minimal due to the difficulty of testing futures * adding some words to the project ignore settings * rank response api working * addressing unused imports in RankResponseAPI * updating the database_interface notebook for the new database backend; addressing logging warning on instantiation * updating the tutorial to show how to use the aggregated data (BrentLab#91) * table data retrieved as gzip; addtiional columns now present from DB * Update README.md closes BrentLab#81 * Adding update to manualqc (BrentLab#96) * removing new file, part of a demo * adding update() method to bindingmanualqc; added _delimiter_detect method to AbstractRecords * addressing pre-commit issues --------- Co-authored-by: ejiawustl <89940465+ejiawustl@users.noreply.github.com> Co-authored-by: Eric Jia <ericjia@Erics-MBP-2.attlocal.net> * Add branch protection CI to prevent pulls directly to main (BrentLab#101) This should only allow pulls from a branch called `dev` or `patch` directly to main. otherwise, pull requests will be required to be against `dev` * fixed adjustment function so its based on enrichment strength (BrentLab#86) Co-authored-by: Eric Jia <ericjia@Erics-MBP-2.attlocal.net> * adding perturbation response relationship tutorial * addressed the changes to the notebook: added analysis to all graphs, typehinting and docstrings to methods, and other misc changes * added new methods and analysis to enable comparison between different dataset combinations. Added everything under the last subtitle, with each section having smaller subheadings underneath to group everything * added docstring and typehinting to all methods, and added exposition to better explain the different conditions we use the model in. TODO: need to hide some of the output when training models or create an issue if I am unable to do so. * Update exploring_perturbation_response_relationship notebook, still WIP * adding notebook, new pyproject * updated notebook: including a lot of new things based on the research work we have been doing for the last month. This notebook currently ends with a guide on creating the linear models on the cluster, but I can include more recent work involving the correlations and models we have been experimenting with * went through all notebooks in vim and resolved merges by keeping the current changes * adding statsmodels to pyproject --------- Co-authored-by: Eric Jia <ericjia@Erics-MBP-2.attlocal.net> Co-authored-by: Chase Mateusiak <chasem@wustl.edu> Co-authored-by: Chase Mateusiak <chase.mateusiak@gmail.com>
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This adds a branch protection CI and also the ability to select the expression/pvalue columns from the ExpressionAPI().
An example with McIsaac
Please be very careful with this and check assumptions. In particular:
More information on all of the data in the database can be found here:
https://github.com/cmatKhan/parsing_yeast_database_data?tab=readme-ov-file
These are the columns of interest in mcisaac
Let's use
log2_cleaned_ratio, but we might trylog2_ratio, also