http://bmbl.sdstate.edu/IRIS/

Monier, B., McDermaid, A., Wang, C., Zhao, J., Miller, A., Fennell, A., & Ma, Q. (2019). IRIS-EDA: an integrated RNA-Seq interpretation system for gene expression data analysis. PLoS computational biology, 15(2), e1006792.

@article{monier2019iris,
  title={IRIS-EDA: an integrated RNA-Seq interpretation system for gene expression data analysis},
  author={Monier, Brandon and McDermaid, Adam and Wang, Cankun and Zhao, Jing and Miller, Allison and Fennell, Anne and Ma, Qin},
  journal={PLoS computational biology},
  volume={15},
  number={2},
  pages={e1006792},
  year={2019},
  publisher={Public Library of Science}
}

IRIS-EDA (Interactive RNA-seq analysis and Interpretation using Shiny-Expression Data Analysis), is a web-based tool for the analysis of RNA-seq count data. This tool's purpose is to provide users with a comprehensive and user-friendly method for performing differential gene expression (DGE) analysis regardless of their computational experience. IRIS-EDA also has integrated experimental design options to cater to users with non-traditional DGE requirements, such as interaction terms or paired data. This tool is designed in a way for usable results to be generated in around one minute or for users to invest more time into detailed investigations of their data. IRIS is a user-friendly and interactive Shiny app for gene expression analysis. This app takes advantage of several popular DGE tools (DESeq2, edgeR, and limma) available through Bioconductor in conjunction with the Plotly and DataTable API libraries for R.

To get a local version of IRIS-EDA, simply copy and paste the following code chunks into an R terminal:

IRIS-EDA requires several packages to operate. Run this code to get the necessary packages from the CRAN repository:

# CRAN
packages <- c(
    "crosstalk", "dplyr", "DT", "gtools", "plotly", "shiny", "plyr",
    "shinyBS", "shinycssloaders", "shinythemes", "tibble", "tidyr",
    "Rcpp", "Hmisc", "ggplot2", "locfit", "GGally", "pheatmap",
    "reshape2", "backports", "digest", "fields", "psych", "stringr",
    "tools", "openxlsx", "Rtsne", "WGCNA", "flashClust", "parallel",
    "MCL", "kmed", "ape"
)
np <- packages[!(packages %in% installed.packages()[, "Package"])]
if(length(np)) install.packages(np)

You will also need several Bioconductor packages. Run this code to get the necessary packages from the Bioconductor repository:

# Bioconductor
bioc_packages <- c(
    "DESeq2", "edgeR", "limma", "QUBIC", "geneplotter", "GO.db", "impute",
    "preprocessCore", "AnnotationDbi"
)
np <- bioc_packages[!(bioc_packages %in% installed.packages()[,"Package"])]
if (!require("BiocManager")) install.packages("BiocManager")
BiocManager::install(np)

To run BRIC analysis, you also need to download the source code for this clustering algorithm. Run this code to get the GitHub package:

# GitHub
if (!require("devtools")) install.packages("devtools")
devtools::install_github("OSU-BMBL/BRIC", force = T)

Once you have installed all of the necessary packages, you can run this code to launch the Shiny application:

shiny::runGitHub("iris", "OSU-BMBL")

Last updated: 2019-03-29