Usage

Organization of Functions

CLARITE has many functions organized into several different modules:

Analyze
Functions related to calculating EWAS results
Describe
Functions used to gather information about data
Load
Functions used to load data from different formats or sources
Modify
Functions used to filter and/or modify data
Plot
Functions that generate plots
Survey
Functions and classes related to handling data with a complex survey design

Coding Style

There are three primary ways of using CLARITE’.

  1. Using the CLARITE package as part of a python script or Jupyter notebook

This can be done using the function directly:

import clarite
df = clarite.load.from_tsv('data.txt')
df_filtered = clarite.modify.colfilter_min_n(df, n=250)
df_filtered_complete = clarite.modify.rowfilter_incomplete_obs(df_filtered)
clarite.plot.distributions(df_filtered_complete, filename='plots.pdf')

Or it can be done using Pandas pipe

clarite.plot.distributions(df.pipe(clarite.modify.colfilter_min_n, n=250)\
                             .pipe(clarite.modify.rowfilter_incomplete_obs),
                           filename='plots.pdf')
  1. Using the command line tool
clarite-cli load from_tsv data/nhanes.txt results/data.txt --index SEQN
cd results
clarite-cli modify colfilter-min-n data data_filtered -n 250
clarite-cli modify rowfilter-incomplete-obs data_filtered data_filtered_complete
clarite-cli plot distributions data_filtered_complete plots.pdf
  1. Using the GUI (coming soon)