tripr 1.12.0
tripr
is a Bioconductor package,
written in shiny that provides
analytics services on
antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor,
TR) gene sequence data. Every step of the analysis can be
performed interactively, thus not requiring any programming skills. It takes
as input the output files of the
IMGT/HighV-Quest tool.
Users can select to analyze the data from each of the input samples separately,
or the combined data files from all samples and visualize the results
accordingly. Functions for an R
command-line use are also available.
tripr
is distributed as a Bioconductor
package and requires R
(version “4.2”), which can be installed on any
operating system from CRAN, and
Bioconductor (version “3.15”).
To install tripr
package enter the following commands in your R
session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("tripr")
## Check that you have a valid Bioconductor installation
BiocManager::valid()
Once tripr
is successfully installed, it can be loaded as follow:
library(tripr)
tripr
as a shiny
applicationIn order to start the shiny
app, please run the following command:
tripr::run_app()
tripr
should be opening in a browser (ideally Chrome, Firefox or Opera).
If this does not happen automatically,
please open a browser and navigate to the address shown on the R
console
(for example, Listening on http://127.0.0.1:6134
).
In this tab users can import their data by selecting the directory where the data is stored, by pressing the Choose directory button. The tool takes as input the 10 output files of the IMGT/HighV-Quest tool in text format (.txt). Users can also choose only some of the files depending on the type of the downstream analysis.
Note that every sample of the dataset must have its own individual folder and every sample folder must be in one root folder (See example below). For the dataset to be selected for upload, this root folder must be selected and then the button Load Data has to be pressed.
Previous sessions can also be loaded with the Restore Previous Sessions button.
There are 2 options regarding the cell type (T cell and B cell) as well as 2 options based on the amount of available data (High- or Low-Throughput). Concerning the latter, the main difference is the application of the preselection and selection steps. In the case of High-Throughput data, all filters are applied consequentially (i.e. if a sequence fails >1 selection criteria, only the first unsatisfied criterion will be reported), whereas for Low-Throughput data all criteria are applied at the same time.
tripr
offers 2 steps of preprocessing:
Preselection: Refers to the cleaning process of the input dataset.
Selection: Refers to the filtering process of the resulting data from Preselection process.
The Preselection process comprises 4 different criteria: