Release History¶
v2.1.1 (2021-07-16)¶
Set pandas-genomics requirement to >= v0.10
v2.1.0 (2021-07-15)¶
Add multiprocessing to association_study and interaction_study
v2.0.2 (2021-07-13)¶
Bump pandas-genomics version, make it less specific
v2.0.1 (2021-07-12)¶
Change “weighted” encoding to “edge” encoding and support newer version of pandas-genomics
v2.0.0 (2021-07-09)¶
Enhancements¶
Analysis functions have been generalized into association_study and interaction_study functions
Support added for genotype data using Pandas-Genomics
v1.3.0 (2021-06-24)¶
Enhancements¶
Add a “report_betas” parameter to optionally return beta values for categorical variables
Add a “standardize_data” parameter to optionally normalize the data by zscores before running the regression, resulting in more comparable beta values
Fixes¶
Corrected an incorrect value logged for N in the stdout during ewas
v1.2.0 (2021-03-12)¶
Enhancements¶
Add clariate.analyze.interaction_test
Improved logging in r_survey ewas
Refactored lots of regression and ewas code to make it more efficient and provide more validation of input data, including better handling of variable names with symbols/numbers
Corrected instructions on installing R packages with Conda
Improved documentation of Regression classes
Manhattan plots have a “return_figure” option
Fixes¶
r_survey regression no longer uses an LRT for binary variables in order to make it concordant with regression in python
outlier_removal is now working as intended
Tests¶
Added a test for outlier removal
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