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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# TestingPackage
An Example R Package For BCB410H: Applied Bioinformatics.
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[](https://www.codefactor.io/repository/github/anjalisilva/testingpackage) [](https://github.com/anjalisilva/TestingPackage/issues) [](./LICENSE)  
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## Description
A paragraph that describes the purpose of your R package and biological data being analyzed. Explain how your package add to or improve a current work flow in bioinformatics or computational biology (i.e., how is it unique?, what issue does it address?). Finally, include the R version (not RStudio version) and platform (Mac, Windows, Linux (Debian, Fedora/Redhat, Ubuntu)), used to develop the package. You may obtain this information by running ``` utils::sessionInfo()```. There should be no `Shiny` implementation at this point. E.g.,
<br>
<br>
<br>
`TestingPackage` is an R package to demonstrate components of a simple R package with RNA sequencing data. The R package includes the main components: DESCRIPTION, NAMESPACE, man subdirectory and R subdirectory. Additionally, LICENSE, README and subdirectories vignettes, tests, data and inst are also explored. The package is targeted for BCB410H (Applied Bioinformatics) students, who are to define a useful tool for the analysis of biological data in the format of a public R package housed on GitHub. The scope of the R package is to add to or improve a current work flow in bioinformatics or computational biology. The tool should contain functions to perform analysis of biological data and to produce a compelling graphical output, ideally to support for exploratory analysis. The `TestingPackage` package was developed using `R version 4.2.1 (2022-06-23)`, `Platform: x86_64-apple-darwin17.0 (64-bit)` and `Running under: macOS Ventura 13.2`.
## Installation
Provide the following text and commands, customized to your R package. E.g.,
<br>
<br>
<br>
To install the latest version of the package:
``` r
install.packages("devtools")
library("devtools")
devtools::install_github("anjalisilva/TestingPackage", build_vignettes = TRUE)
library("TestingPackage")
```
To run the Shiny app:
``` r
runTestingPackage() # not for Assessment 4; only for Assessment 5
```
## Overview
Provide the following commands, customized to your R package. Then provide a list of user accessible functions within the package and a brief description of each. Include one image illustrating the overview of the package that shows the inputs and outputs. Ensure the image is deposited in the correct location, as discussed in class. Point the user to vignettes for a tutorial of your package. E.g.,
<br>
<br>
<br>
``` r
ls("package:TestingPackage")
data(package = "TestingPackage")
browseVignettes("TestingPackage")
```
`TestingPackage` contains 3 functions.
1. ***InfCriteriaCalculation*** for calculating information criteria given dataset dimensions, log-likelihood and probability.
2. ***NormFactors*** for calculating normalization factors via via trimmed mean of M-values (TMM).
3. ***InfCriteriaPlot*** for plotting information criteria values as a scatter plot.
The package also contains two RNA sequencing datasets, called GeneCounts and GeneCounts2. Refer to package vignettes for more details. An overview of the package is illustrated below.
## Contributions
Provide a paragraph clearly indicating the name of the author of the package and contributions from the author. Outline contributions from other packages/sources for each function. Outline contributions from generative AI tool(s) for each function. Include how the tools were used and how the results from AI tools were incorporated. Remember your individual contributions to the package are important. E.g.,
<br>
<br>
<br>
The author of the package is Anjali Silva. The author wrote the *InfCriteriaCalculation* function, which calculates the information criteria values given data specifications. Here, the Bayesian information criterion (BIC), Akaike information criterion (AIC) and Integrated Complete Likelihood (ICL) are calculated. The *InfCriteriaCalculation* function makes use of map function from `mclust` R package to generate information criteria values. The `stats` R package is used for generating multinomially distributed random number vectors. Part of the code for *InfCriteriaCalculation* function has been taken from `<NamePackage>` R package. (Section of the borrowed code should be clearly indicated and referenced in the InfCriteriaCalculation R script). The *InfCriteriaPlot* is written by the author and generates a plot of information criteria values. The *InfCriteriaPlot* function makes use of the `graphics` R package. *NormFactors* is a function that calculates normalization factors via Trimmed Mean of M-values (TMM). *NormFactors* function uses Trimmed Mean of M-values (TMM) as implemented in `edgeR` R package. No generative AI tools were used in the development of this package.
## References
Provide full references for all sources used, including for the packages and tools mentioned under 'Contributions', in one format. E.g.,
<br>
<br>
- Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In *Second International Symposium on Information Theory*, New York, USA, 267–281. Springer Verlag. https://link.springer.com/chapter/10.1007/978-1-4612-1694-0_15.
- Biernacki, C., G. Celeux, and G. Govaert (2000). Assessing a mixture model for clustering with the integrated classification likelihood. *IEEE Transactions on Pattern Analysis and Machine Intelligence* 22. https://hal.inria.fr/inria-00073163/document
- BioRender. (2020). Image created by Silva, A. Retrieved October 30, 2020, from https://app.biorender.com/
- Chang, W., J. Cheng, J. Allaire, C. Sievert, B. Schloerke, Y. Xie, J. Allen, J. McPherson, A. Dipert, B. Borges (2023). _shiny: Web Application Framework for R_. R package version 1.8.0, <https://CRAN.R-project.org/package=shiny>
- McCarthy, D. J., Y. Chen and G. K. Smyth (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. *Nucleic Acids Research* 40. 4288-4297.
https://pubmed.ncbi.nlm.nih.gov/22287627/
- R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
- Robinson, M. D., D.J. McCarthy and G. K. Smyth (2010). edgeR: a
Bioconductor package for differential expression analysis
of digital gene expression data. *Bioinformatics* 26,
139-140. https://pubmed.ncbi.nlm.nih.gov/19910308/
- Schwarz, G. (1978). Estimating the dimension of a model. *The Annals of Statistics* 6, 461–464. https://projecteuclid.org/euclid.aos/1176344136.
- Scrucca, L., M. Fop, T. B. Murphy and A. E. Raftery (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. *The R Journal* 8(1), 289-317.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5096736/
- Wickham, H. ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York, 2016.
- Wickham, H. and J. Bryan (2019). *R Packages* (2nd edition). Newton, Massachusetts: O'Reilly Media. https://r-pkgs.org/
## Acknowledgements
Provide the following text, customized to your R package. E.g.,
<br>
<br>
<br>
This package was developed as part of an assessment for 2019-2023 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. `TestingPackage` welcomes issues, enhancement requests, and other contributions. To submit an issue, use the [GitHub issues](https://github.com/anjalisilva/TestingPackage/issues). Many thanks to those who provided feedback to improve this package.
## Student Packages
Packages developed by BCB410 alumni. Many thanks to those who provided permission to share their packages!
<br>
### 2023
* [AtlaZ](https://github.com/ashenafee/AtlaZ)
* [ClinicalTranscriptLink](https://github.com/michellemlin/ClinicalTranscriptLink)
* [CompHeatmaps](https://github.com/kojiwong/CompHeatmaps)
* [CovidWasteWatch](https://github.com/viv-wang/CovidWasteWatch)
* [DrugSeekR](https://github.com/this-wei/DrugSeekR)
* [GEAnaly](https://github.com/ReaganGen/GEAnaly)
* [IntegraTRN](https://github.com/j-y26/IntegraTRN)
* [IsoformVisRNA](https://github.com/hyunnaye/IsoformVisRNA)
* [MicrobiomeExplorer](https://github.com/itsSabbir/MicrobiomeExplorer)
* [MissensePathoR](https://github.com/Lola-W/MissensePathoR)
* [OpenViRome](https://github.com/HiAidenG/OpenViRome)
* [OsteoAnalizer](https://github.com/ika-joshi/OsteoAnalizer)
* [PDBCleanup](https://github.com/isaiahah/PDBCleanup)
* [PhenoGenRLib](https://github.com/RealYHD/PhenoGenRLib)
* [PROstat5](https://github.com/kannai99/PROstat5)
* [psychCNVassoc](https://github.com/angelauzelac10/psychCNVassoc)
* [SeuratToGO](https://github.com/dien-n-nguyen/SeuratToGO)
* [TMscoreAlign](https://github.com/kevqyzhu/TMscoreAlign)
* [TRexDAD](https://github.com/yunyicheng/TRexDAD)
* [TRNValStandVis](https://github.com/Mogtaba-Alim/TRNValStandVis)
### 2022
* [AbunRNA](https://github.com/zhangchengyue/AbunRNA)
* [DeregGenes](https://github.com/wezhubb/DeregGenes)
* [CNVds](https://github.com/jenydu/CNVds)
* [oncoAnalysis](https://github.com/xuxiny17/oncoAnalysis)
* [sexDisaggregate](https://github.com/lifangy6/sexDisaggregate)
### 2021
* [scRGNet](https://github.com/ff98li/scRGNet)
* [DEVisComp](https://github.com/Lori-tan/DEVisComp)
* [covid19Canada](https://github.com/RicoZong/covid19Canada)
* [IntegrationScore](https://github.com/eliaswilliams/IntegrationScore)
* [MyoManager](https://github.com/karenkuang37/MyoManager)
* [snpCYP](https://github.com/a-albuquerque/snpCYP)
* [VisualSARSDiff](https://github.com/aryanahmad/VisualSARSDiff)
* [clustREval](https://github.com/cindyfang70/clustREval)
* [ClustPhy](https://github.com/rainali475/ClustPhy)
### 2020
* [gscVisualizer](https://github.com/Deemolotus/gscVisualizer)
* [microCompet](https://github.com/MolyWang/microCompet)
* [uniprotProteinView](https://github.com/zzaassaa2/uniprotProteinView)
* [MethylExpress](https://github.com/diannamcallister/MethylExpress)
* [dynUGENE;](https://github.com/tianyu-lu/dynUGENE) [manuscript](https://www.biorxiv.org/content/10.1101/2021.01.07.425782v1)
* [pdb3D](https://github.com/kevin949373048/pdb3D)
* [STRExpansionAnalyzer](https://github.com/michaelzwong/STRExpansionAnalyzer)
* [CellTyPETool](https://github.com/meconsens/CellTyPETool)
* [ChIPanalyzer](https://github.com/RyDe4/ChIPanalyzer)
## Package Structure
The package structure is illustrated below:
<br>
<br>
The package tree structure is provided below.
``` r
- TestingPackage
|- TestingPackage.Rproj
|- DESCRIPTION
|- NAMESPACE
|- LICENSE
|- README
|- data
|- GeneCounts.rda
|- GeneCounts2.rda
|- inst
CITATION
|- extdata
|- SILVA_A_A1.png
|- GeneCountsData2.csv
|- shiny-scripts
|- app.R
|- man
|- GeneCounts.Rd
|- InfCriteriaCalculation.Rd
|- NormFactors.Rd
|- InfCriteriaPlot.Rd
|- R
|- data.R
|- InfCriteriaCalculation.R
|- InfCriteriaPlot.R
|- NormFactorCalculation.R
|- vignettes
|- TestingPackageVignette.Rmd
|- tests
|- testthat.R
|- testthat
|- test-InfCriteriaCalculation.R
```