Web version:

http://octad.org/

How to Install

Before library installation install required Bioconductor and CRAN packages through this code:

bioconductor_packages=c('edgeR','RUVSeq','DESeq2','limma','rhdf5','artMS')

#For R version 3.5> use BiocManager to install required bioconductor packages: 
if (length(setdiff(bioconductor_packages, rownames(installed.packages()))) > 0) {
  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  BiocManager::install(setdiff(bioconductor_packages, rownames(installed.packages())))
}

#For R version <3.5 use the BiocInstaller to install required bioconductor packages: 
source("https://bioconductor.org/biocLite.R")
BiocInstaller::biocLite(bioconductor_packages)

packages=c('magrittr','dplyr','ggplot2','doParallel','foreach','lme4','Rfast','httr','data.table')
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())))  
}

Next, install the octad.db, package with all required files for computation available via link octad.db

install.packages("path%to%octad.db_0.99.0.tar.gz", repos = NULL, type="source")

Or without downloading the distributive:

install.packages("https://chenlab-data-public.s3-us-west-2.amazonaws.com/octad/octad.db_0.99.0.tar.gz",
                 method="libcurl",repos=NULL,type="source")

It takes a few minutes to install the package and verify files. Afterward, the pipeline will be ready to run. Finally, install the package:

devtools::install_github('Bin-Chen-Lab/octad',build_vignettes = TRUE)

Additional data

By default, octad package uses expression data for 978 genes from the LINCS dataset. However, it can influence the result and we advice using whole octad database. To obtatin whole results for DE, downloading of the additional OCTAD database octad.counts.and.tpm.h5 from the AWS link is required.

Tutorial

The tutorial available via following link

Examples

The several examples listed in the file octad_example.R :

Example 1. liver hepatocellular carcinoma vs adjacent reference tissues;

Example 2. breast cancer invasive carcinoma with PIK3 mutation vs reference tissues;

Example 3. lung adenocarcinoma with amplified MYC gene vs reference tissues;

Example 4. Primary breast cancer invasive carcinoma vs metastatic breast cancer invasive carcinoma using only 978 genes expression data for DE;

Example 5. Compute sRGES score using GEO obtained dataset

Contacts and citation

If you use our work, please cite the paper OCTAD: an open workplace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features, Nature Protocols. Both OCTAD package and website was developed by Bin Chen laboratory. Examples and questions can be addressed to Eugene Chekalin, PhD, chekali1@msu.edu or Bin Chen, PhD, PI, bin.chen@hc.msu.edu