In shashidhar22/LymphoSeq2: Analyze high-throughput sequencing of T and B cell receptors

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

LymphoSeq2

Overview

Adaptive Immune Receptor Repertoire Sequencing (AIRR-seq) provides a unique opportunity to interrogate the adaptive immune repertoire under various clinical conditions. The utility offered by this technology has quickly garnered interest from a community of clinicians and researchers investigating the immunological landscapes of a large spectrum of health and disease states. LymphoSeq2 is a toolkit that allows users to import, manipulate and visualize AIRR-Seq data from various AIRR-Seq assays such as Adaptive ImmunoSEQ and BGI-IRSeq, with support for 10X VDJ sequencing coming soon. The platform also supports the importing of AIRR-seq data processed using the MiXCR pipeline. The vignette highlights some of the key features of LymphoSeq2.

Installation

To install the latest version of LymphoSeq2 you can use the devtools package and install LymphoSeq2 from GitHub

# install.packages("devtools")
devtools::install_github("shashidhar22/LymphoSeq2", build_vignettes = TRUE)

Getting started

To import AIRR-Seq data using LymphoSeq2 we can use the readImmunoSeq function. Currently the function can import data from MiXCR, Adaptive ImmunoSEQ, BGI IR-SEQ, and 10X Genomic single cell VDJ rearrangements.

library(LymphoSeq2)
study_files <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq2")
study_table <- LymphoSeq2::readImmunoSeq(study_files)

To get a quick summary of repertoire characteristics, use the clonality function. This will calculate many standard repertoire diversity metrics such clonality, gini coefficient, convergence, and unique productive sequence for each of the repertoires in the input dataset.

summary_table <- LymphoSeq2::clonality(study_table)
summary_table

To compare samples with varying depth of sequencing, you can use the clonality function to sample down all repertoires to a minimum number of sequences. Since we randomly sample sequences from each repertoire, in this mode the clonality function will repeat this operation for a user specified number of iterations and caculate the average value for all the diversity metrics.

sampled_summary <- LymphoSeq2::clonality(study_table, rarefy = TRUE, iterations = 5, min_count = 1000)
sampled_summary

shashidhar22/LymphoSeq2 documentation built on Jan. 16, 2024, 4:29 a.m.