CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures
NOTE
There is now a python version of CLARITE that is more actively developed. See documentation here.
The goal of clarite is to guide a dataset from the “raw” data stage to EWAS analysis and
subsequent visualization of results. The package is designed to lead a user through the
stages of data cleaning: from generating descriptive statistics, to making QC decisions
informed by the descriptive statistics, to running analyses on the filtered dataset and
visualizing the results.
A development version of the package can be installed using devtools.
devtools::install_github('HallLab/clarite')
The following image depicts a typical workflow for a project from raw data stage to analysis, in this case an
Environment-Wide Association Study, and results visualization, all of which can be performed using the clarite
package. The user starts with raw data and alternates filtering (dark boxes) or summary steps (light boxes) until it is sufficiently “cleaned” and in a stage where analyses can be run.
If you have any questions not answered by the documentation, feel free to open an Issue.
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Lucas AM, et al (2019) CLARITE facilitates the quality control and analysis process for EWAS of metabolic-related traits. Frontiers in Genetics: 10, 1240
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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