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📊Graphical User Interface for TCC package
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README.md

📊 TCC-GUI: Graphical User Interface for TCC package


TCC1 is a R/Bioconductor package provides a series of functions for performing differential expression (DE) analysis from RNA-seq count data using a robust normalization strategy (called DEGES).

The basic idea of DEGES is that potential differentially expressed genes (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing the multi-step normalization procedures based on DEGES (DEG elimination strategy) implemented in TCC.

TCC internally uses functions provided by edgeR2, DESeq3, DESeq24, and baySeq5 . The multi-step normalization of TCC can be done by using functions in the four packages.

In this GUI version of TCC (TCC-GUI), all parameter settings are available just like you are using the original one. Besides, it also provides lots of plotting functions where the original package is unsupported now.

Tips: Development is now undergoing, some functions and features may be changed in the final version.

📈 Features

Simulation Data Generation Exploratory Analysis

TCC Computation

MA Plot Generation

Volcano Plot Generation

Heatmap Generation

Expression Level Plot Generation

Report Generation

📔 Usage

🌐 Online version

Go to 🔗TCC-GUI.

💻 Standalone version

📲 Installation

Make sure that you have already installed those packages in your environment.

shiny, shinydashboard, shinyWidgets, plotly, dplyr, TCC, DT, heatmaply, rmarkdown, data.table, tidyr, RColorBrewer, utils, knitr, cluster, shinycssloaders, shinyBS, MASS.

If any package is missing, Please run the following command in your RStudio and it will install all packages automatically.

# Check "BiocManager"
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Package list
libs <- c("shiny", "shinydashboard", "shinyWidgets", "plotly", "dplyr", "DT", "heatmaply", "tidyr","utils","rmarkdown","data.table","RColorBrewer", "knitr", "cluster", "shinycssloaders", "shinyBS", "MASS", "TCC")

# Install packages if missing
for (i in libs){
  if( !is.element(i, .packages(all.available = TRUE)) ) {
     BiocManager::install(i, suppressUpdates=TRUE)
  }
}
⭕ Launch

Run the following command to launch TCC-GUI in your local environment, then it will download TCC-GUI automatically from github and launch.

Method 1
shiny::runGitHub("TCC-GUI", "swsoyee", subdir = "TCC-GUI", launch.browser = TRUE)

This method always download the source code from github before launching, so maybe you can try to download all the source code by yourself and launch it.

Method 2
  1. Click Clone or download button on the top of this page, then click Download ZIP;
  2. Unzip the file to your working directory (use getwd() to know your working directory);
  3. Run the code of launching (according to your structure of working directory it may be different).
shiny::runApp("TCC-GUI-master//TCC-GUI", launch.browser = TRUE)

📕 Publication

TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data
Wei Su, Jianqiang Sun, Kentaro Shimizu and Koji Kadota
BMC Research Notes 2019 12:133
https://doi.org/10.1186/s13104-019-4179-2 | © The Author(s) 2019
Received: 14 January 2019 | Accepted: 11 March 2019 | Published: 13 March 2019

📚 References

[1] Sun J, Nishiyama T, Shimizu K, et al. TCC: an R package for comparing tag count data with robust normalization strategies. BMC bioinformatics, 2013, 14(1): 219.

[2] Robinson M D, McCarthy D J, Smyth G K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010, 26(1): 139-140.

[3] Anders S, Huber W. Differential expression analysis for sequence count data. Genome biology, 2010, 11(10): R106.

[4] Love M I, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 2014, 15(12): 550.

[5] Hardcastle T J, Kelly K A. baySeq : empirical Bayesian methods for identifying differential expression in sequence count data. BMC bioinformatics, 2010, 11(1): 422.

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