Age | Commit message (Collapse) | Author |
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* remove extra iteration that is unnecessary
* remove unnecessary variables
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* gn3/computations/correlations.py: rename function
* mypy.ini: deactivate mypy error about missing imports
* tests/integration/test_correlation.py: mock correct function
* tests/unit/computations/test_correlation.py: test correct function
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* To help speed up the processing of the correlations, convert the
`compute_all_sample_correlation` function to use the multiprocessing module.
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The `fix_strains` function works on the trait data, not the basic trait
info. This commit fixes the arguments passed to the function, and also some
bugs in the function.
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* Add a new script to compute the partial correlations against:
- a select list of traits, or
- an entire dataset
depending on the specified subcommand. This new script is meant to supercede
the `scripts/partial_correlations.py` script.
* Fix the check for errors
* Reorganise the order of arguments for the
`partial_correlations_with_target_traits` function: move the `method`
argument before the `target_trait_names` argument so that the common
arguments in the partial correlation computation functions share the same
order.
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Rework the code to process the traits in a single iteration to improve
performance.
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Return generator objects rather than pre-computed tuples to reduce the number
of iterations needed to process the data, and thus improve the performance of
the system somewhat.
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Enable the endpoint to actually compute partial correlations with selected
target traits rather than against an entire dataset.
Fix some issues caused by recent refactor that broke pcorrs against a dataset
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Compute partial correlations against a selection of traits rather than against
an entire dataset.
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* Extract the common error checking code into a separate function
* Rename the function to make its use clearer
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Remove an unnecessary looping construct to help with speeding up the partial
correlations somewhat.
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* Remove a module that is no longer in use
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See: <https://ci.genenetwork.org/jobs/genenetwork3-pylint/126>
* gn3/computations/rqtl.py: Run `black gn3/computations/rqtl.py`. Also,
manually fix other pylint issues.
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position in pair-scan results + return only the sorted top 500 results
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it's needed to store the proximal/distal markers for each position
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Dict and List respectively used for the pair scan figure and the table showing the results
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in pairscan results + renamed process_rqtl_output to process_rqtl_mapping to distinguish between that and pairscan
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* compute zscore function
* test case for computing zscore
* function to compute pca
* generate scree plot data
* generate new pca trait data from zscores and eigen_vec
* remove redundant functions
* generate factor loading table data
* generate pca temp dataset dict
* variable naming and error fixes
* unit test for processing factor loadings
* minor fixes for generating temp pca dataset
* pass datetime as argument to generate_pca temp dataset function
* add unittest for caching pca datasets
* cache temp datasets
* ignore missing imports for sklearn
* mypy fixes
* pylint fixes
* refactor tests for pca
* remove ununsed imports
* fix for generating pca traits vals
* mypy and code refactoring
* pep8 formatting and add docstrings
* remove comments /pep8 formatting
* sort eigen vectors based on eigen values
* minor fix for zscores
* fix for rounding variance ratios
* refactor tests
* rename module to pca
* rename datasets to traits
* fix failing tests
* fix caching function
* fixes return x and y coordinates for scree plot
* expand exception scope
* fix for deprecated numpy.matrix function
* fix for failing tests
* pep8 fixes
* remove comments
* fix merge conflict
* pylint fixes
* rename module name to test_pca
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Fix some issues caught by tests due to changes introducing the hand-off of the
partial correlations computations to an external process
Fix some issues due to the changes that introduce context managers for
database connections
Update some tests to take the above two changes into consideration
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correlations
In the original version of the if statement* I believe it was
interpreted as "if a_val and (b_val is not None)". This caused
values of 0 for a_val (the primary trait's values) to be evaluated as
False.
I changed it to compare both a_val and b_val to None. This seems to have
fixed the issue.
* if (a_val and b_val is not None)
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