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README.Rmd
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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%"
)
```
# DeregGenes
<!-- badges: start -->
<!-- badges: end -->
## Description
The main objective of `DeregGenes` is to find genes that are deregulated(up-regulate and down- regulate) in different diseases. This package could also allow users to put different results together to generate a heatmap for cross studies analysis. It improves users’ time on massive data cleaning and data annotating processes prior to the analysis since different data prepared by different platforms will need to be handled by different tools. Moreover, this package provides a simple way to let users get a summarised result across multiple current studies data to provide them with a more confident result and conclusion. Also, using this package will save users time to switch back and forth between different distinct packages and learn different documentation since the different packages will require different input data types which is not available on any current published r package.
- R requirement: 4.2.0 or later version
- Development platform: Mac
## Installation
You can install the development version of DeregGenes from [GitHub](https://github.com/) with:
``` r
require("devtools")
devtools::install_github("wezhubb/DeregGenes", build_vignettes = TRUE)
library("DeregGenes")
```
To run the ShinyApp:
``` r
DeregGenes::runDeregGenes()
```
## Overview
``` r
ls("package:DeregGenes")
data(package = "DeregGenes")
browseVignettes("DeregGenes")
```
There are 4 functions in `DeregGenes`
`prepareData`: A function used to clean and annotate the data, including handling raw CEL format data, putting together different individual samples into a table, converting different gene IDs and probe IDs to universal HGNC gene symbols, and joining different tables. This function will create a data matrix in which each row represent different gene(with the gene symbol in its rowname), and each column represent different gene's expression level in different sample.
`logFCsingle`: A function to analyze the gene expression data to find gene expression fold change/gene differential expression for each gene in a single study. This function will create a data frame where each row represent different genes, and six columns that gives expressional change(logFC), average expression(AveExpr), t value(t), p value(P.Value), adjusted p value(adj.P.Val), and log-odd ratio/B-statistic(B).
`Aggreg`: A function to aggregate different gene expression fold changes across different studies. This function will create a list of length three. The first element of the list is a data frame of up-regulated differential genes. The second element of the list is a data frame of down-regulated differential genes. For the first two data frame, each row a a different genes, and there will be four columns: gene symbol(Name), p value(Pvalue), adjust p value(adjPvalue), and expressional change(logFC). The last element of the list is a aggregated data frame where each row is a gene, and each column is the logFC of different studies.
`plotHeatMap`: A function to draw a heatmap representation of differential gene expression corss different studies. Notice that this function will not return the heatmap, but will stored the heat map in local directory.
Below is a flowchart demonstart the work flow of this package.
{ width=75% }
See `help(package = "DeregGenes")` for further details and references provided by `citation("DeregGenes")`.
## Contributions
The package is created by Wenzhu Ye.
The `prepareData` function use of `oligo` package to read in and normalize CEl file, use of `biomaRt` package to get the HGNC gene symbol, and use of `dplyr` package to filter dataset.
The `logFCsingle` function use of `impute` package to impute the missing values in dataset and use of `limma` package to compute linear fit of the gene expression.
The `Aggreg` function use of `RobustRankAggreg` package to compute overall preformance of each gene's expression level across target studies.
The `plotHeatMap` function use of `pheatmap` package to plot the heatmap.
`shinny` package is used to develop shinny app.
## Acknowledgements
This package was developed as part of an assessment for 2022f BCB410H: Applied Bioinformatics, University of Toronto, Toronto, Canada.
## References
Carvalho B. S., and Irizarry, R. A. 2010. A Framework for Oligonucleotide Microarray Preprocessing Bioinformatics.
Gautier, L., Cope, L., Bolstad, B. M., and Irizarry, R. A. 2004. affy---analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 3 (Feb. 2004), 307-315.
Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Steffen Durinck, Paul T. Spellman, Ewan Birney and Wolfgang Huber, Nature Protocols 4, 1184-1191 (2009).
BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Steffen Durinck, Yves Moreau, Arek Kasprzyk, Sean Davis, Bart De Moor, Alvis Brazma and Wolfgang Huber, Bioinformatics 21, 3439-3440 (2005).
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” _Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
Wickham H, François R, Henry L, Müller K (2022). _dplyr: A Grammar of Data Manipulation_. R package version 1.0.10, <https://CRAN.R-project.org/package=dplyr>.
Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.
Hastie T, Tibshirani R, Narasimhan B, Chu G (2022). _impute: impute: Imputation for microarray data_. R package version 1.70.0.
Kolde R (2022). _RobustRankAggreg: Methods for Robust Rank Aggregation_. R package version 1.2.1, <https://CRAN.R-project.org/package=RobustRankAggreg>.
Kolde R (2019). _pheatmap: Pretty Heatmaps_. R package version 1.0.12, <https://CRAN.R-project.org/package=pheatmap>.
Wang H, Huo X, Yang XR, He J et al. STAT3-mediated upregulation of lncRNA HOXD-AS1 as a ceRNA facilitates liver cancer metastasis by regulating SOX4. Mol Cancer 2017 Aug 14;16(1):136. PMID: 28810927
Stefanska B, Huang J, Bhattacharyya B, Suderman M et al. Definition of the landscape of promoter DNA hypomethylation in liver cancer. Cancer Res 2011 Sep 1;71(17):5891-903. PMID: 21747116
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.