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omicsArt User Manual

omicsArt is a tool for quality control (QC), statistical analysis, and visualization of omics data. omicsArt is currently under development. We will regularly update the documentation and tutorials.

Citation:

For installation and a quick demo, read the omicsArt Tutorial.

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Contents

• Description
• Requirements
  • For Installation
  • For Execution
• How to Run
  • Input Files
  • Output Files
  • Run a Demo
  • Options
• Visualization
• Troubleshooting

Description

omicsArt is developed to perform QC and statistical analyses of LC-MS based metabolite profiles. These tasks are critical for extracting biological signals from profile data. All statistical tests come with visualizations of the data for easy interpretation.

Requirements

omicsArt is an R package containing various functions. It requires the following R packages, which can be found in Biocondutor and CRAN (Comprehensive R Archive Network). Please install these packages before running omicsArt.

Packages needed to install omicsArt (all from CRAN):

• devtools
• dplyr
• pbapply
• lme4
• lmerTest
• car
• cplm
• pscl
• logging
• ggrepel
• gridExtra
• future
• cowplot

These can all be installed with a single command:
>install.packages(c('devtools', 'dplyr', 'pbapply', 'lme4', 'lmerTest', 'car', 'cplm', 'pscl', 'logging', 'ggrepel', 'gridExtra', 'future', 'cowplot'), repos='http://cran.r-project.org')

Packages needed to run omicsArt:

• Bioconductor packages
  • edgeR: Empirical Analysis of Digital Gene Expression Data in R
  • metagenomeSeq: Statistical analysis for sparse high-throughput sequencing
  • These packages can be installed through Bioconductor by first sourcing biocLite with source("https://bioconductor.org/biocLite.R") and then installing each package with biocLite("PACKAGE_NAME").
• CRAN packages
  • chemometrics: Multivariate Statistical Analysis in Chemometrics
  • ggplot2: Create Elegant Data Visualizations Using the Grammer of Graphics
  • pheatmap: Pretty Heatmaps
  • logging: R logging package
  • data.table: Fast aggregation of large data
  • These packages can be installed in R with install.packages('ggplot2') or from the command line $ R -q -e "install.packages('ggplot2', repos='http://cran.r-project.org')" individually (for those packages which you do not yet have installed) or as a set by providing the complete list as a vector.````

Installation

omicsArt can be run as an R function.
Once all requirements are installed, the following commands can be run to install omicsArt:
>library(devtools)
> devtools::install_github('omicsEye/omicsArt', force = TRUE)

How to Run

Input Files

Output Files

Run a Demo

Options

Visualization

Most of omicsArt's functions provide appropriate plots to give an easy interpretation of data.

Ordination plots

Ordination plots are a common way to glance at data and see if there is a trend in data that can be explained by specific metadata.

ord_plots <- ordplots(data, metadata, output = output_path, outputname = NA , method = 'pcoa')

parameters:

data: is an R data frame of size N rows (samples or observations) by D columns (features). This can be taken from the output of the load_data function.

metadata: is an R data frame of size N rows (samples or observations) by M columns (metadata, clinical data, or phenotypes). This can be taken from the output of the load_data function.

output: is a path to a directory where the plots will be written to in pdf format.

outputname: is a name for the output file. If it is not provided, the function will use the method name as the output file name.

method: is the ordination method and can be 'pcoa' or 'tsne'

output:

a pdf file is written in the output path with file name outputname, and the function also returns a list of ggplot2 plots including one plot for each metadata, named using the column headers in metadata.

Statistical tests

T test

stats_table <- omicsArt:::stats_2groups(case = data_case , control = data_control , test_type = 'wilcox.test', paired = T)

• case

• control

• test_type

• paired

Troubleshooting