GENETEX
Overview
The goal of GENETEX (pronounced “genetics”) is to facilitate data abstraction from genomic reports for clinical research. With that goal in mind, the package centralizes around a core function called genetex_to_redcap() which applies natural language and text mining tools to abstract key genomic information from a variety of clinical reports found in routine clinical practice and prepares those data for import into a REDCap® database.
We have also created a front-end Shiny application that uses the core functions of GENETEX on the server side of the app. The Shiny app of GENETEX allows for data to be entered in the browser to facilitate data capture. The package does not require the use of the Shiny app, but the app enhances the usability of the package.
To facilitate the installation of this package and shiny application, we have created a Video Demo, that can be accessed here
GENETEX provides a set of verbs that help you import, text mine and process genomic data for clinical research:
| Verbs | Function |
|---|---|
| genetex_to_redcap() | integrates key verbs to provide NLP tools to abstract data from a variety of genomic reports and import them to REDCap |
| gene.variants() | integrates various platform-specific NLP functions to text mine gene names and nucleotide variants from genomic reports and transforms them to structured data for import into REDCap |
| cnv() | integrates various platform-specific NLP functions to text mine gene names and copy number variants data from a variety of genomic reports and transforms them to structured data for import into REDCap |
| mmr() | text mines mismatch repair status from genomic reports and transforms it to structured data for import into REDCap |
| mutational.signatures() | text mines mutational signatures data from a variety of genomic reports and transforms it to structured data for import into REDCap |
| tmb() | text mines tumor mutation burden (TMB) data from a variety of genomic reports and transforms it to structured data for import into REDCap |
| platform() | applies regular expressions to assign a numerical value for the various platforms used for genomic reports that aligns with the genomics_platform field in the REDCap Genomics Instrument |
| genes_regex() | produces a regular expression of over 900 HGNC gene names |
| genes_boundary_regex() | produces a regular expression of over 900 HGNC gene names as a unique string with word boundaries |
| genomics.tissue.type() | applies regular expressions to assign a numerical value for the various platforms used for genomic reports that aligns with the genomics_platform field in the REDCap Genomics Instrument |
Dependencies
Software Dependencies
GENETEX is written in R (version 4.0.0), organized using roxygen2, and utilizes the following packages dplyr, tidyr, readr, stringr, purrr, REDCapR, magrittr, splitstackshape and Shiny.
Clinical Informatics Dependencies
The purpose of this package is to facilitate abstraction of medical records for importation into the Genomics Instrument in REDCap®. Therefore you must have a REDCap® project and the Genomics Instrument. The data dictionary of the Genomics Instrument, has been previously published and can be found here.
Installation
Development version
To get a bug fix or to use a feature from the development version, you can install the development version of GENETEX from GitHub.
devtools::install_github("TheMillerLab/genetex")
Usage
library(genetex)
Input
Overview
We wanted to make the process of securing data into the package user-friendly to busy clinicians and clinical research staff. Since most staff will have their target/desired report open in a window in their Electronic Health Record or an open PDF form a commercial vendor, we designed GENETEX to take advantage of the readr::clipboard() function. Thus, the input data canb e directly copied to your computer’s clipboard from the clinical report by selecting the relevant text (or by “selecting all” e.g. “control + A” or “Command + A) and then”copy” (“control + A” or “Command + A”). That is the default.
R Studio-Based Data Acquisition
One does not need to use the Shiny App to secure data into R. You can execute the “pasting” of the data with the readr::clipboard() function right in R Studio. If you don’t use the clipboard function, you can use a data frame of the text file. That data frame can be engineered from a txt or csv file. Each line of the data frame should mirror a line in the genomics report.
Shiny App-Based Data Acquisition
In addition, to ease abstraction of these data, we created a browser-based user interface that incorporates text data captured on a clipboard as input in a Shiny application. This Shiny app is separate, but complementary to the GENETEX package. Text is copied to a computer’s clipboard and pasted into the text area input in the Shiny application.
Supported Platforms
At this time, reports from the following platforms are able to be processed by genetex:
Output
- The output of this function will be a csv file stored in the “./file” subfolder in your working directory.
- The above output is just a “glimpse” that is provided to provide instant feed back to the end user that the function called without error
- The csv file be entitled: “guardant_import_Lewis Jones.csv” since the record was titled “Lewis Jones”.
- This csv file can then be imported into REDCap®.
- Alternatively, if you have a REDCap® API, you can directly import the data into your REDCap® project with the argument redcap_api_token, and along with the url assigned to the argument redcap_uri.
- Alternatively you can use the Shiny App.
Direct Import into REDCap®
- If you used the redcap_uri and redcap_api_token arguments and directly imported your data into the Genomics instrument.
Other Functions
We’ve incorporated a few other functions with the goal of facilitating analysis of genomics data
genes_regex()
genes_regex() returns a regex of cancer gene symbols, which can be used to assist in text mining data by matching a list of 906 cancer-related genes
Getting help
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.
Disclaimer and Acknowledgements
We would like to acknowledge Ravikumar Komandur, PhD, Project Director at Project Data Sphere for review and critique of the manuscript; Guardant Health and Foundation Medicine for making sample reports available for the development of this package. GENETEX is for research purposes only. No clinical decisions should be made with the information obtained from its output. This article reflects the views and work (including development and use of GENETEX) of the authors and should not be construed to represent the work, policies, of any of the vendors whose reports were used to develop GENETEX and whose reports may be provided as part of the package. This package reflects the views of the authors and should not be construed to represent the views or policies of any of the vendors that have supplied sample reports to the package.