Skip to contents

Shiny-AnalyzAIRR

Source: vignettes/shiny.Rmd
shiny.Rmd

Shiny-AnalyzAIRR is a Shiny web application developed for the AnalyzAIRR package, making it user-friendly for biologists with little or no background in bioinformatics.

Users are can apply the full range of analytical methods proposed by AnalyzAIRR and described in this documentation, without requiring any bioinformatics expertise.

The Shiny includes additional features:

  • interactive plots that reveal information that might be missed in static visualizations
  • summary reports compiling all plots created during an analysis session. This report can be customized with descriptive text for each figure, allowing for detailed interpretation of results, and can be downloaded in various formats, including PDF, HTML, and PowerPoint.

Installation

Installation of the AnalyzAIRR package

The latest release of AnalyzAIRR can be installed from Github using devtools:

devtools::install_github("i3-unit/AnalyzAIRR")

Installation of Shiny-AnalyzAIRR

Shiny-AnalyzAIRR should be cloned in the command line as follow:

git clone https://github.com/i3-unit/Shiny-AnalyzAIRR.git

Once downloaded, Shiny-AnalyzAIRR can be launched from the terminal:

# Go to the Shiny-AnalyzAIRR folder
cd Shiny-AnalyzAIRR
# Launch R and shiny
R -e 'shiny::runApp("./")'

Or run in Rstudio:

# Open the global.R file in RStudio and run:
shiny::runApp("./")

Alternatively, Shiny-AnalyzAIRR has been deployed on the web and can be used directly at this link.

Data loading

Uploading a RepSeqExperiment object

A RepSeqExperiment object generated using the AnalyzAIRR R package and saved in an rds format can be loaded directly into the Shiny

Uploading alignment data files

File source

Different alignment file formats are supported by the package. More details on AnalyzAIRR-supported input data can be found here.

Metadata

It is possible to provide a metadata if users wish to perform inter-group comparative analyses.

The metadata should be provided as a table containing:

  • a column sample_id with the sample names that match the name of the alignment files and their order.

Only one column containing the sample ids should be provided in the metadata file.

  • any additional columns with relevant information for the analyses. Columns could encompass the experimental conditions, clinical variables, etc…

Besides sample_id, no specific column names nor order are required.

Additional information on the metadata can be found here.

Choice of chain to analyze

The chain type to be analyzed is to be chosen. Users can choose only one of the following:

  • “TRA”, “TRB”,“TRG” or “TRD” for the TCR repertoires
  • “IGH”,“IGK” or “IGL” for the BCR repertoires

When all files are loaded, Shiny-AnalyZAIRR automatically creates and loads the RepSeqExperiment object. This object is used as input in all of the functions. For more details on the the RepSeqExperiment object structure, visit this page. An example data is included to illustrate the functionalities of the package.

Getting started

Once the data is successfully loaded, a summary of the dataset is displayed on the home page. The summary include:

  • The total number of samples in the dataset
  • The number of groups found in the metadata
  • The total number of sequences across all samples
  • The sequence number overview illustrates the distribution of sample sizes within the dataset, with each sample’s size quantified by its sequence count
  • The count distribution showing the clone count distribution for each sample within the dataset

The package offers various statistical metrics and visualization methods, allowing a complete data exploration as well as cross-sample comparisons to answer defined biological questions. A detailed description of each of AnalyzAIRR’s functionalities can be found in the different sections of this documentation.