#Color makes more divergent, check MetaboAnalyst

Description

OmicsAnalystR is the underlying R package synchronized with OmicsAnalystR web server. It is designed for data-driven multi-omics integration and systems-level interpretation. The R package is composed of R functions necessary for the web-server to perform data processing, batch correction, correlation analysis, clustering analysis and dimension reduction analysis.

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

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

Installation

Step 1. Install package dependencies

To use OmicsAnalystR, 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

OmicsAnalystR 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 OmicsAnalystR.

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 OmicsAnalystR WITHOUT documentation
devtools::install_github("xia-lab/OmicsAnalystR", build = TRUE, build_opts = c("--no-resave-data", "--no-manual", "--no-build-vignettes"))

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

Tips for using the OmicsAnalystR package

1. 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.
2. The OmicsAnalystR package will output data files/tables/analysis/networks outputs in your current working directory.
3. 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.
4. Each main function in OmicsAnalystR is documented. Use the ?Function format to open its documentation. For instance, use ?OmicsAnalystR::QueryNet to find out more about this function.

Example

Processing and integrating datasets

library(OmicsAnalystR)

# Step 1. Initiate the R objects
Init.Data();

# Step 2. Read datasets
ReadOmicsData("preg_prot.csv", "prot");
ReadOmicsData("preg_met.csv", "met_t");

# Step 3. Process datasets individually
SanityCheckData("preg_prot.csv");
CheckDataType("preg_prot.csv", "true");
AnnotateGeneData("preg_prot.csv", "hsa", "symbol");
RemoveMissingPercent("preg_prot.csv", 0.5)
ImputeMissingVar("preg_prot.csv", "min")
FilteringData("preg_prot.csv","pct","2", "15");
NormalizingData("preg_prot.csv", "log", "NA", "AutoNorm");

SanityCheckData("preg_met.csv");
CheckDataType("preg_met.csv", "true");
AnnotateMetaboliteData("preg_met.csv", "name");
RemoveMissingPercent("preg_met.csv", 0.5)
ImputeMissingVar("preg_met.csv", "min")
FilteringData("preg_met.csv","pct","2", "15");
NormalizingData("preg_met.csv", "log", "NA", "AutoNorm");

# Step 4. Visual inspection of processed data
PlotMultiTsne("qc_multi_tsne_0_","72", "png", "");
PlotMultiPCA("qc_multi_pca_0_","72", "png", "");
PlotMultiDensity("qc_multi_density_0_","72", "png", "");

# Step 5. Network correlation analysis
DoFeatSelectionForCorr("default",20,3);
DoOmicsCorrelation("univariate", "pearson");
DoCorrelationFilter("both", "true", "both",0.9,0.5,2000.0,"genus","agora","false");