omicplotR
is an R package containing a
Shiny
app used to visually explore omic datasets, where the
input is a table of read counts from high-throughput sequencing runs. It
integrates the ALDEx2
1 package for compositional analysis of
differential abundance. omicplotR
is intended facilitate
exploring high-throughput sequencing datasets by providing a graphical
user interface for users with and without experience in R.
High-throughput sequencing (HTS) instruments generate an amount of
reads that is constrained by limitations of the sequencing instrument
itself, and do not represent the absolute number of DNA molecules in a
sample. For example, an Illumina NextSeq can deliver up to 400 million
single-end reads, whereas an Illumina MiSeq2 can only deliver up to 15
million single-end reads2. This type of data, which is constrained by
an arbitrary or constant sum, is referred to as compositional data, and
high-throughput sequencing data must be treated as such3. See
ALDEx2
for more information.
Although several R packages exist for exploring high-throughput
sequencing data, they are typically command line based, which presents a
barrier for users without any significant command line or scripting
experience. omicplotR
was created to facilitate the
exploratory phase of high-throughput sequencing data analysis allowing
the generation of basic exploratory plots automatically with adjustable
features and filters.
This vignette provides an overview of the R package
omicplotR
and the input requirements. A tutorial for each
component of the Shiny
app is available on the wiki: https://github.com/dgiguer/omicplotR/wiki.
omicplotR
was developed for several types of HTS datasets
including RNASeq, meta-RNASeq, and 16s rRNA gene sequencing, and in
principle, can be used for nearly any type of data generated by HTS that
contains a tables of counts per feature for each sample.
omicplotR
provides a graphical user interface using the
Shiny
package for the following visualizations for HTS
data:
Additional features include:
ALDEx2
ALDEx2
tables and colour points by
rownames for large datasetsInstall the latest version of omicplotR
using
BiocManager
. Make sure you have the newest version of R,
ALDEx2
, and other dependancies. omicplotR
requires you to have at least R version 3.5. The most up to date version
is available at www.github.com/dgiguer/omicplotr/, and is the dev
branch.
First, load the omicplotR
package. All other
dependencies will be loaded automatically. This will launch the
Shiny
app in your default browser. For this vignette, we
will be using the example data and metadata provided. Example data and
metadata are accessible by data(otu_table)
and
data(metadata)
. They are also available as .txt files in
~/omicplotR/shiny-app/
.
install.packages("BiocManager")
BiocManager::install("omicplotR")
library(omicplotR)
omicplotr.run()
After launching the Shiny
app, click the ‘Input data’
tab to get started.
The ‘Data’ tab on the sidebar panel allows you to choose your own data and metadata by clicking ‘Browse’. To follow along with this vignette, please click the ‘Example data’ tab on the sidebar panel, and click the checkbox for the ‘Vaginal dataset’. This dataset, which includes associated metadata, is from a study that characterized the changes in the vaginal microbiome following antibiotic and probiotic treatment by 16s rRNA gene sequencing4. Return to the ‘Data’ tab on the sidebar panel to view the data and metadata by clicking ‘Show data’ and ‘Show metadata’. The tabs on the main panel allow you to switch between displaying your data and metadata tables.
When choosing your own data set, input requirements are as follows: for both metadata and data, each sample and feature name (operational taxonomic unit - OTU) must be unique. An example of an appropriately formatted data file is shown in Figure 2.
Your metadata file must follow a similar format. An example of an appropriate metadata file is shown in Figure 3.