Description

OmicsNetR is the underlying R package synchronized with OmicsNet web server. It is designed for network-based multi-omics integration and systems-level interpretation. The R package is composed of R functions necessary for the web-server to perform network creation, trimming and analysis.

Following installation and loading of OmicsNetR, users will be able to reproduce web server results from their local computers using the R command history downloaded from OmicsNet. Running the R functions will allow more flexibility and reproducibility.

Note - OmicsNetR is still under development - we cannot guarantee full functionality

Installation

Step 1. Install package dependencies

To use OmcisNetR, make sure your R version is >4.0.3 and install all package dependencies. Ensure that you are able to download packages from Bioconductor. To install package dependencies, use the pacman R package. Note that some of these packages may require additional library dependencies that need to be installed prior to their own successful installation.

install.packages("pacman")

library(pacman)

pacman::p_load(igraph, RColorBrewer, qs, rjson, RSQLite)

Step 2. Install the package

OmicsNetR is freely available from GitHub. The package documentation, including the vignettes for each module and user manual is available within the downloaded R package file. If all package dependencies were installed, you will be able to install the OmicsNetR.

Install the package directly from github using the devtools package. Open R and enter:


# Step 1: Install devtools
install.packages(devtools)
library(devtools)

# Step 2: Install OmicsNetR WITHOUT documentation
devtools::install_github("xia-lab/OmicsNetR", build = TRUE, build_opts = c("--no-resave-data", "--no-manual", "--no-build-vignettes"))

# Step 2: Install OmicsNetR WITH documentation
devtools::install_github("xia-lab/OmicsNetR", build = TRUE, build_opts = c("--no-resave-data", "--no-manual"), build_vignettes = TRUE)

Tips for using the OmicsNetR package

The first function that you will use in every module is the Init.Data function, which initiates the dataSet object that stores user's data for further processing and analysis.
The OmicsNetR package will output data files/tables/analysis/networks outputs in your current working directory.
Every function must be executed in sequence as it is shown on the R Command history, please do not skip any commands as this can result in errors downstream.
Each main function in OmicsNetR is documented. Use the ?Function format to open its documentation. For instance, use ?OmicsNetR::QueryNet to find out more about this function.

Examples

Starting from a list of genes

library(OmicsNetR)

# Step 1. Initiate the dataSet object
dataSet<-Init.Data()

# Step 2. Map list of genes to the application
dataSet<-PrepareInputList(dataSet,"#Entrez  logFC
4495  61.12
4496  51.06
4499  23.79
6354  21.04
6369  19.76", "hsa", "gene", "entrez", "direct");

# Step 3. Identify interacting partners
dataSet<-QueryNet(dataSet, "gene", "innate")

# Step 4. Build interaction subnetwork
CreateGraph();

# Step 5. Prepare the network file to be used for visualization, the output will be in JSON format.
dataSet<-PrepareNetwork(dataSet, "subnetwork1", "omicsnet_1.json")

Starting from list of genes and miRNA

library(OmicsNetR)

# Step 1. Initiate the dataSet object
dataSet<-Init.Data()

# Step 2. Map list of genes to the application
dataSet<-PrepareInputList(dataSet,"#Entrez  logFC
4495  61.12
4496  51.06
4499  23.79
6354  21.04
6369  19.76", "hsa", "gene", "entrez");

# Step 2. Map list of miRNA to the application
dataSet<-PrepareInputList(dataSet,"hsa-mir-101-3p
hsa-mir-133b
hsa-mir-147a
hsa-mir-3140-3p
hsa-mir-361-5p
hsa-mir-510-5p", "hsa", "mir", "mir_id");

# Step 3. Build PPI network from uploaded list of genes
dataSet<-QueryNetMulti(dataSet, "gene", "innate", "gene" )

# Step 4. Build miRNA-gene network from uploaded list of miRNA
dataSet<-QueryNetMulti(dataSet, "mir", "mirtarbase", "mir" )

# Step 5. Merge networks together through shared nodes and decompose into interconnected subnetworks
CreateGraph();

# Step 6. Prepare the network file to be used for visualization, the output will be in JSON format.
dataSet<-PrepareNetwork(dataSet, "subnetwork1", "omicsnet_1.json")

Save OmicsNet JSON file for saving current analysis.

library(OmicsNetR)

# Step 1. Initiate the dataSet object
dataSet<-Init.Data()

# Step 2. Map list of genes to the application
dataSet<-PrepareInputList(dataSet,"#Entrez  logFC
4495  61.12
4496  51.06
4499  23.79
6354  21.04
6369  19.76", "hsa", "gene", "entrez", "direct");

# Step 3. Identify interacting partners
dataSet<-QueryNet(dataSet, "gene", "innate")

# Step 4. Decompose network into interconnected subnetworks
CreateGraph();

# Step 5. Save the JSON file, this file can be uploaded again to restore analysis session
SaveNetworkJson("omicsnet_graph_file_1.json")

Load OmicsNet JSON file to restore analysis session

library(OmicsNetR)

# Step 1. Initiate the dataSet object
dataSet<-Init.Data()

# Step 2. Read graph file, specify correct file format
dataSet<-ReadGraphFile(dataSet, "Your file path", "jsonOmicsnet");

# Step 3. Prepare the network file to be used for visualization, the output will be in JSON format.
dataSet<-PrepareNetwork(dataSet, "subnetwork1", "omicsnet_1.json")

wtbxsjy/OmicsNetR