API Reference

CLARITE functions are organized into several modules:

Analyze

EWAS and associated calculations

ewas(phenotype, covariates, data, …)

Run an EWAS on a phenotype

add_corrected_pvalues(ewas_result)

Add bonferroni and FDR pvalues to an ewas result and sort by increasing FDR (in-place)


Describe

Functions that are used to gather information about some data

correlations(data, threshold)

Return variables with pearson correlation above the threshold

freq_table(data)

Return the count of each unique value for all binary and categorical variables.

get_types(data)

Return the type of each variable

percent_na(data)

Return the percent of observations that are NA for each variable

summarize(data)

Print the number of each type of variable and the number of observations


Load

Load data from different formats or sources

from_tsv(filename, index_col, int, …)

Load data from a tab-separated file into a DataFrame

from_csv(filename, index_col, int, …)

Load data from a comma-separated file into a DataFrame


Modify

Functions used to filter and/or change some data, always taking in one set of data and returning one set of data.

categorize(data, cat_min, cat_max, cont_min)

Classify variables into binary, categorical, continuous, and ‘unknown’.

colfilter(data, skip, List[str], …)

Remove some variables (skip) or keep only certain variables (only)

colfilter_percent_zero(data, filter_percent, …)

Remove continuous variables which have <proportion> or more values of zero (excluding NA)

colfilter_min_n(data, n, skip, List[str], …)

Remove variables which have less than <n> non-NA values

colfilter_min_cat_n(data, n, skip, …)

Remove binary and categorical variables which have less than <n> occurences of each unique value

make_binary(data, skip, List[str], …)

Set variable types as Binary

make_categorical(data, skip, List[str], …)

Set variable types as Categorical

make_continuous(data, skip, List[str], …)

Set variable types as Numeric

merge_observations(top, bottom)

Merge two datasets, keeping only the columns present in both.

merge_variables(left, right, how)

Merge a list of dataframes with different variables side-by-side.

move_variables(left, right, skip, List[str], …)

Move one or more variables from one DataFrame to another

recode_values(data, replacement_dict, skip, …)

Convert values in a dataframe.

remove_outliers(data, method[, cutoff])

Remove outliers from continuous variables by replacing them with np.nan

rowfilter_incomplete_obs(data, skip, …)

Remove rows containing null values

transform(data, transform_method, skip, …)

Apply a transformation function to a variable


Plot

Functions that generate plots

histogram(data, column, figsize, int]=, …)

Plot a histogram of the values in the given column.

distributions(data, filename, …)

Create a pdf containing histograms for each binary or categorical variable, and one of several types of plots for each continuous variable.

manhattan(dfs, pandas.core.frame.DataFrame], …)

Create a Manhattan-like plot for a list of EWAS Results


Survey

Complex survey design

SurveyDesignSpec(survey_df[, strata, …])

Holds parameters for building a statsmodels SurveyDesign object