Release History¶
v1.1.0 (2020-08-14)¶
Enhancements¶
- Add a subset method on the SurveyDesignSpec class
- Refactored regression so that the ewas function now takes a regression_kind parameter
Tests¶
- Added tests for the subset method
v1.0.1 (2020-06-12)¶
Enhancements¶
- Improve the legend in the top_results plot and add additional parameters similar to the manhattan plots
Fixes¶
- Update the default names for the ewas parameter single_cluster in the CLI
- Add the “drop_unweighted” parameter to the printed result of Survey Designs
- Fix an IndexError caused by non-continuous variables being passed to describe.skewness
- Fix the travis build (the bioconda channel must be specified to install r-survey)
Tests¶
- Added a plot test for passing “None” as the cutoff to the top results plot
v1.0.0 (2020-06-04)¶
Fixes¶
- Fixed ewas_r not working for some parameter combinations
- Improved the top_results plot to work with non-continuous values (which don’t have Betas)
- Corrected ewas results for some scenarios (strata and clusters) related to missing data (incorrect degrees of freedom)
Tests¶
- Added additional analysis tests with realistic data (more missing values)
- All analysis tests are now passing with 1E-4 relative tolerance
- Added the first plot tests
v0.10.0 (2020-05-28)¶
Enhancements¶
- Manhattan plot split into three functions (raw, bonferroni, and fdr) and now has a custom threshold parameter
- Use Pandas v1.0+
- Refactored regression objects to simplify internal code and potentially allow for more types of regression in the future
- Added an ewas_r function that seamlessly runs the ewas analysis in R, using the R survey library * This is recommended when using weights, as the python version has some inconsistencies in some edge cases
- Added a skewness function
- Added a top_results plot
- Add a drop_unweighted parameter to the SurveyDesignSpec to provide an easy (if potentially incorrect) workaround for observations with missing weights
Fixes¶
- Provide a warning and a convenience function when categorical types have categories with no occurrences
- Catch errors when categorizing variables with many unique string values
- Corrected some edge-case EWAS results when using weights in the presence of missing values
- Avoid some cryptic errors by ensuring the input to some functions is a DataFrame and not a Series
Tests¶
Many additional tests were added, especially related to EWAS
v0.9.1 (2019-11-20)¶
Minor documentation update
v0.9.0 (2019-10-31)¶
Enhancements¶
- Add a figure parameter to histogram and manhattan plots in order to plot to an existing figure
- SurveyDesignSpec can now utilize more parameters, such as fpc
- The larger (numeric or alphabetic) binary variable is always treated as the success case for binary phenotypes
- Improved logging during EWAS, including printing the survey design information
- Extensively updated documentation
- CLARITE now has a logo!
Fixes¶
- Corrected an indexing error that sometimes occurred when removing rows with missing weights
- Improve precision in EWAS results for weighted analyses by using sf instead of 1-cdf
- Change some column names in the EWAS output to be more clear
Tests¶
An R script and the output of that script is now included. The R output is compared to the python output in the test suite in order to ensure analysis result concordance between R and Python for several analysis scenarios.
v0.8.0 (2019-09-03)¶
Enhancements¶
- Allow file input in the command line for skip/only
- Make the manhattan plot function less restrictive of the data passed into it
- Use skip/only in the transform function
Fixes¶
- Categorization would silently fail if there was only one variable of a given type
v0.7.0 (2019-07-23)¶
Enhancements¶
- Improvements to the CLI and printed log messages.
- The functions from the ‘Process’ module were put into the ‘Modify’ module.
- Datasets are no longer split apart when categorizing.
v0.6.0 (2019-07-11)¶
Extensive changes in organization, but limited new functionality (not counting the CLI).
Enhancements¶
- Reorganize functions - https://github.com/HallLab/clarite-python/pull/13
- Add a CLI - https://github.com/HallLab/clarite-python/pull/11
v0.5.0 (2019-06-28)¶
Enhancements¶
- Added a function to recode values - https://github.com/HallLab/clarite-python/issues/4
- Added a function to filter outlier values - https://github.com/HallLab/clarite-python/issues/5
- Added a function to generate manhattan plots for multiple datasets together - https://github.com/HallLab/clarite-python/issues/9
Fixes¶
- Add some validation of input DataFrames to prevent some errors in calculations
Tests¶
- Added an initial batch of tests
v0.4.0 (2019-06-18)¶
Support EWAS with binary outcomes. Additional handling of NA values in covariates and the phenotype. Add a ‘min_n’ parameter to the ewas function to require a minimum number of observations after removing incomplete cases. Add additional functions including ‘plot_distributions’, ‘merge_variables’, ‘get_correlations’, ‘get_freq_table’, and ‘get_percent_na’
v0.3.0 (2019-05-31)¶
Add support for complex survey designs
v0.2.1 (2019-05-02)¶
Added documentation for existing functions
v0.2.0 (2019-04-30)¶
First functional version. Mutliple methods are available under a ‘clarite’ Pandas accessor.
v0.1.0 (2019-04-23)¶
Initial Release