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)