README.md

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

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)
#> Loading required package: data.table
study_files <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq2")
study_table <- LymphoSeq2::readImmunoSeq(study_files)
#> Registered S3 methods overwritten by 'readr':
#>   method                    from 
#>   as.data.frame.spec_tbl_df vroom
#>   as_tibble.spec_tbl_df     vroom
#>   format.col_spec           vroom
#>   print.col_spec            vroom
#>   print.collector           vroom
#>   print.date_names          vroom
#>   print.locale              vroom
#>   str.col_spec              vroom

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
#> # A tibble: 10 × 8
#>    repertoire_id    total_sequences unique_productive_se…¹ total_count clonality
#>    <chr>                      <int>                  <int>       <dbl>     <dbl>
#>  1 TRB_CD4_949                 1000                    845       25769     0.443
#>  2 TRB_CD8_949                 1000                    794       26239     0.431
#>  3 TRB_CD8_CMV_369              414                    281        1794     0.332
#>  4 TRB_Unsorted_0              1000                    838       18161     0.281
#>  5 TRB_Unsorted_13…            1000                    838      178190     0.422
#>  6 TRB_Unsorted_14…            1000                    832       33669     0.389
#>  7 TRB_Unsorted_32              920                    767       31078     0.134
#>  8 TRB_Unsorted_369            1000                    830      339413     0.426
#>  9 TRB_Unsorted_83             1000                    823      236732     0.338
#> 10 TRB_Unsorted_949            1000                    831        6549     0.306
#> # ℹ abbreviated name: ¹​unique_productive_sequences
#> # ℹ 3 more variables: gini_coefficient <dbl>, top_productive_sequence <dbl>,
#> #   convergence <dbl>

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
#> # A tibble: 10 × 8
#>    repertoire_id    total_sequences unique_productive_se…¹ total_count clonality
#>    <chr>                      <dbl>                  <dbl>       <dbl>     <dbl>
#>  1 TRB_CD4_949                 160.                   134         1000    0.311 
#>  2 TRB_CD8_949                 190.                   150.        1000    0.301 
#>  3 TRB_CD8_CMV_369             276                    188.        1000    0.298 
#>  4 TRB_Unsorted_0              248.                   209         1000    0.157 
#>  5 TRB_Unsorted_13…            184.                   149         1000    0.282 
#>  6 TRB_Unsorted_14…            217                    182.        1000    0.262 
#>  7 TRB_Unsorted_32             416.                   350.        1000    0.0928
#>  8 TRB_Unsorted_369            244.                   200.        1000    0.338 
#>  9 TRB_Unsorted_83             307.                   250         1000    0.272 
#> 10 TRB_Unsorted_949            307                    255.        1000    0.221 
#> # ℹ abbreviated name: ¹​unique_productive_sequences
#> # ℹ 3 more variables: gini_coefficient <dbl>, top_productive_sequence <dbl>,
#> #   convergence <dbl>

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