OsteoAnalizer

Description

The goal of OsteoAnalizer is to clean CSV data sets containing information related to Osteoarthritis. The package also aims to analyse the relation between the 4 key aspects of Osteoarthritis(i.e fluid levels, cartilage thickness, severity levels, and patient age) by outputing visual elements.

This package aims to improve current osteoarthritis related workflows by concentrating the analysis on the four key variables(fluid levels, cartilage thickness, severity levels, and patient age). To better understand the complexities of osteoarthritis, the package uses advanced visualization options, such as box plots and scatter plots, which enable comparative analyses across all four variables, a capability not available in other packages. Furthermore, this package has a key feature not found in other bioinformatics packages, which is the ability to analyze OA data without the use of images. As opposed to previous packages that analyzed image-based data in order to calculate the severity of osteoarthritis, OsteoAnalizer utilizes a CSV format data sets for analysis.

R Version: R version 4.3.1 (2023-06-16) Platform: x86_64-apple-darwin20

Installation

You can install the development version of OsteoAnalizer like so:

require("devtools")
devtools::install_github("ika-joshi/OsteoAnalizer", build_vignettes = TRUE)
library("OsteoAnalizer")

To run the ShinyApp:

OsteoAnalizer::runOsteoAnalizer()

Overview

A quick overview of the package

ls("package:OsteoAnalizer")
data(package = "OsteoAnalizer") # optional
browseVignettes("OsteoAnalizer")

OsteoAnalizer contains 3 functions:

cleanCSVFile cleans and organizes a CSV file into 4 columns(i.e fluid levels, cartilage thickness, severity levels, and patient age) which is stored in a list.

makeBox visualizes fluid levels, cartilage thickness and patient age against severity levels. It creates a box plot of each of the variable mentioned with severity level.

makeScatter visualizes the relation between any two variables from fluid levels, cartilage thickness and patient age. It creates a scatter plot of the two chosen variable. It also takes performs linear regression analysis on the two chosen variables.

Contributions

The package was developed by Krutika Joshi. The author coded the all the functions independently with the aid of the resources listed below:

Function CleanCSVFile:

• Package na.omit is used to clean data sets in CleanCSVFile.
• Package read.csv is used to read the CSV file in CleanCSVFile.
• Package sexDisaggregate and Bates are used to understand the error checking for CSV files.
• Author coded the cleaning of the data and outputting result.

Function makeBox:

• Package ggplot2 is used to create box plots.
• GeeksforGeeks and Holtz was used to understand to color templates in the ggplot2 plots.
• Package sexDisaggregate is used to understand the error checking for inputs.
• Package cowplot is used to combine box plots.
• ChatGPT, Claus and Wickham were used to understand the syntax of cowplot and ggplot2.
• Author combined the concepts listed above to code a box plot with different stylistic elements and labels.

Function makeScatter:

• Package ggplot2 is used to create scatter plots.
• Package ggpmisc is used to show the line of regression in makeScatter.
• Package coef is used to compute the line of regression.
• Package sexDisaggregate is used to understand the error checking for input.
• Package lm is used to compute the line of regression.
• R CODER was used to understand the conceptual computations involved to create a scatter plot.
• Wickham was used to understand the syntax of the functions present in ggplot2.
• ChatGPT was used to understand the syntax of the functions present in ggpmisc, coef and ggplot2.
• Author combined the concepts listed above to code a scatter plot with different stylistic elements and labels with the line of regression.

References

Bates, C. (2023, April 9). Tutorial: Loading and cleaning data with R and the Tidyverse. Dataquest. https://www.dataquest.io/blog/load-clean-data-r-tidyverse/

Chatgpt. ChatGPT. (n.d.). https://openai.com/chatgpt

Claus O. Wilke (2017). cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. https://github.com/wilkelab/cowplot.

GeeksforGeeks. (2022, December 26). How to color scatter plot points in R ?. GeeksforGeeks. https://www.geeksforgeeks.org/how-to-color-scatter-plot-points-in-r/

Holtz, Y. (n.d.). Control Ggplot2 Boxplot colors. – the R Graph Gallery. https://r-graph-gallery.com/264-control-ggplot2-boxplot-colors.html

Li, F. (2022) sexDisaggregate:To Obtain and Analyze Sex-disaggregated Datasets, Unpublished. URL https://github.com/lifangy6/sexDisaggregate”

R box plot. DataMentor. (n.d.). https://www.datamentor.io/r-programming/box-plot

R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Scatter plot in R programming 🟢 [with examples]. R CODER. (2021, June 18). https://r-coder.com/scatter-plot-r/

U.S. Department of Health and Human Services. (2017). Study of osteoporotic fractures. National Institute on Aging. https://agingresearchbiobank.nia.nih.gov/studies/sof/?search_term=Osteoarthritis

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.

Acknowledgements

This package was developed as part of an assessment for 2022 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. OsteoAnalizer welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.

Tree Structure

- OsteoAnalizer
  |- .gitignore
  |- .Rbuildignore
  |- .RData
  |- .Rhistory
  |- data
    |- OA_data.rda
  |- DESCRIPTION
  |- doc
    |- Introduction_OsteoAnlizer.html
    |- Introduction_OsteoAnlizer.R
    |- Introduction_OsteoAnlizer.Rmd
  |- inst
    |- CITATION
    |- extdata
      |- example_data.csv
      |- overview.jpg
    |- shiny-script
      |- app.R
  |- LICENSE
  |- LICENSE.md
  |- man
    |- CleanCSVFile.RD
    |- makeBox.Rd
    |- makeScatter.Rd
    |- OA_data.Rd
    |- runOsteoAnalizer.Rd
  |- NAMESPACE
  |- OsteoAnalizer.Rproj
  |- R
    |- CleanCSVFile.R
    |- data.R
    |- makeBox.R
    |- makeScatter.R
    |- runOsteoAnalizer.R
  |- README.md
  |- README.RMD
  |- tests
    |- testthat
      |- test-CleanCSVFile.R
      |- test-makeBox.R
      |- test_makeScatter.R
    |- testthat.R
  |- vignettes