clarite: 'clarite' package

In HallLab/clarite: CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures

Description

clarite package

Motivation

CLARITE was created to provide an easy-to-use tool for analysis of traits and exposures. It exists in several forms:

• This R package

• A python package (recommended)

• A command line program (part of the python package)

• A GUI tool

Citing CLARITE

If you use CLARITE in a scientific publication, please consider citing:

• Lucas AM, et al (2019) CLARITE facilitates the quality control and analysis process for EWAS of metabolic-related traits. Frontiers in Genetics: 10, 1240

• Passero K, et al (2020) Phenome-wide association studies on cardiovascular health and fatty acids considering phenotype quality control practices for epidemiological data. Pacific Symposium on Biocomputing: 25, 659

Example Analysis

An example analysis can be viewed here: http://rpubs.com/HallLabDocs/clarite_example_analysis

Categorizing variables

It is important to accurately categorize variables in order to correctly utilize them in any QC and analysis steps. Depending on the type of a variable (categorical, continuous, or binary) it should be plotted or analyzed differently. Variables of different types should be separated into individual data.frames manually, or using the heuristic method provided by the various get functions.

Kinds of functions that are provided

The CLARITE package provides many useful functions which could be grouped into a few categories: describe, modify, analyze, and plot.

Describe functions: These are functions related to summary statistics and tests

• chisq_tests

• correlations

• freq_tables

• outlier_impact

• sample_size

Modify functions: These are functions related to manipulating and normalizing data

• colfilter

• get_binary

• get_categorical

• get_check

• get_continuous

• get_uniq

• merge_data

• min_cat_n

• min_n

• recode_key

• recode_missing

• remove_incomplete_obs

• remove_outliers

• rowfilter

• transvar

Analyze functions: These are functions related to performing an EWAS analysis using regression

• ewas

• ewas_pval_adjust

Plot functions: These are functions related to generating plots

• bar_plot

• box_plot

• eman

• hist_plot

• multi_plot

• qq_plot

HallLab/clarite documentation built on Oct. 27, 2020, 6:27 p.m.