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Single-cell and paired chain data: scTCRseq and scBCRseq exploration

ImmunoMind – improving design of T-cell therapies using multi-omics and AI. Research and biopharma partnerships, more details: immunomind.io

Source: vignettes/web_only_v0/v21_singlecell.Rmd
v21_singlecell.Rmd

Executive Summary

This is a vignette dedicated to provide an overview on how to work with single-cell paired chain data in immunarch

Single-cell support is currently in the development version. In order to access it, you need to install the latest development version of the package by executing the following command:

install.packages("devtools"); devtools::install_github("immunomind/immunarch", ref="dev")

To read paired chain data with immunarch use the repLoad function with .mode = "paired". Currently we support 10X Genomics only.

To create subset immune repertoires with specific barcodes use the select_barcodes function. Output of Seurat::Idents() as a barcode vector works.

To create cluster-specific and patient-specific datasets using barcodes from the output of Seurat::Idents() use the select_clusters function.

Use the data packaged with immunarch

Load the package into the R enviroment:

For testing purposes we attached a new paired chain dataset to immunarch. Load it by executing the following command:

data(scdata)

Load the paired chain data

To load your own datasets, use the repLoad function. Currently we implemented paired chain data support for 10X Genomics data only. A working example of loading datasets into R:

file_path <- paste0(system.file(package = "immunarch"), "/extdata/sc/flu.csv.gz")
igdata <- repLoad(file_path, .mode = "paired")
## 
## == Step 1/3: loading repertoire files... ==
## Processing "<initial>" ...
##   -- [1/1] Parsing "/home/runner/work/_temp/Library/immunarch/extdata/sc/flu.csv.gz" -- 10x (filt.contigs)
## 
## == Step 2/3: checking metadata files and merging files... ==
## 
## Processing "<initial>" ...
##   -- Metadata file not found; creating a dummy metadata...
## 
## == Step 3/3: processing paired chain data... ==
## 
## Done!
igdata$meta
## # A tibble: 1 × 1
##   Sample
##   <chr> 
## 1 flu
head(igdata$data[[1]][c(1:7, 16, 17)])
##   Clones Proportion
## 1      3      3e-04
## 2      3      3e-04
## 3      2      2e-04
## 4      2      2e-04
## 5      2      2e-04
## 6      2      2e-04
##                                                                                                               CDR3.nt
## 1                                           TGTGCGAGGCTATGGGGTTGGGGATTACTCTACTGG;TGCACCTCATATGCAGGCAGCAACAATTTGGTATTC
## 2                TGTGCACACACCACCGAACTCTATTGTACTAATGGTGTATGCTATGGGGGCTACTTTGACTACTGG;TGCCAACAGTATAATAGTTATTCGTGGACGTTC
## 3                                        TGTGCGAGAGCTACCTCTTTTTATTACTTTCACTACTGG;TGCACCTCATATACAACCAGGACCACTCTGATATTC
## 4                                        TGTGCGAGAGCTACGTCTTTTTATTACTTTCACCACTGG;TGCACCTCATATACAACCAGGACCACTCTGATATTC
## 5 TGTGCGAGACAAAAGCGAGGGAGTATTACTATGGTTCGGGGAGTTATTATAACACGTCCCTACTTTGACTACTGG;TGCAGCTCATATACAAGCAGCAGCACCCTTTATGTCTTC
## 6                               TGTGCGAGGACTCTGCAACTGGGGATGCTGAGCGCTTTTGATATCTGG;TGCAGCTCATATACAAGCAGCAGCACTTATGTCTTC
##                                   CDR3.aa            V.name            D.name
## 1               CARLWGWGLLYW;CTSYAGSNNLVF IGHV4-59;IGLV2-11     IGHD3-10;None
## 2      CAHTTELYCTNGVCYGGYFDYW;CQQYNSYSWTF   IGHV2-5;IGKV1-5      IGHD2-8;None
## 3              CARATSFYYFHYW;CTSYTTRTTLIF  IGHV3-7;IGLV2-14 IGHD2OR15-2B;None
## 4              CARATSFYYFHHW;CTSYTTRTTLIF  IGHV3-7;IGLV2-14 IGHD2OR15-2B;None
## 5 CARQKRGSITMVRGVIITRPYFDYW;CSSYTSSSTLYVF IGHV4-34;IGLV2-14     IGHD3-10;None
## 6           CARTLQLGMLSAFDIW;CSSYTSSSTYVF IGHV5-51;IGLV2-14     IGHD7-27;None
##        J.name   chain                                                  Barcode
## 1 IGHJ4;IGLJ2 IGH;IGL AGAGCGACACCTTGTC-1;ATTGGTGAGACCTAGG-1;TCTTCGGAGGTGATTA-1
## 2 IGHJ4;IGKJ1 IGH;IGK AGTAGTCAGTGTACTC-1;GGCGACTGTACCGAGA-1;TTGAACGGTCACCTAA-1
## 3 IGHJ4;IGLJ2 IGH;IGL                    AGACGTTGTACACCGC-1;CAAGTTGCACGGCCAT-1
## 4 IGHJ4;IGLJ2 IGH;IGL                    ATAACGCTCGCATGAT-1;GACTAACGTCCAGTGC-1
## 5 IGHJ4;IGLJ1 IGH;IGL                    ACTGAACCAGTATGCT-1;GGGAGATCAGTATGCT-1
## 6 IGHJ3;IGLJ1 IGH;IGL                    GCGACCACACGGTTTA-1;GTCATTTCAAGCGATG-1

Subset by barcodes

To subset the data by barcodes, use the select_barcodes function.

barcodes <- c("AGTAGTCAGTGTACTC-1", "GGCGACTGTACCGAGA-1", "TTGAACGGTCACCTAA-1")

new_df <- select_barcodes(scdata$data[[1]], barcodes)

new_df

Patient-specific datasets

To create a new dataset with cluster-specific immune repertoires, use the select_clusters function:

scdata_pat <- select_clusters(scdata, scdata$bc_patient, "Patient")

names(scdata_pat$data)

scdata_pat$meta

Cluster-specific datasets

To create a new dataset with cluster-specific immune repertoires, use the select_clusters function. You can apply this function after you created patient-specific datasets to get patient-specific cell cluster-specific immune repertoires, e.g., a Memory B Cell repertoire for a specific patient:

scdata_cl <- select_clusters(scdata_pat, scdata$bc_cluster, "Cluster")

names(scdata_cl$data)

scdata_cl$meta

Explore and compute statistics

Most functions will work out-of-the-box with paired chain data.

p1 <- repOverlap(scdata_cl$data) %>% vis()
p2 <- repDiversity(scdata_cl$data) %>% vis()

target <- c("CARAGYLRGFDYW;CQQYGSSPLTF", "CARATSFYYFHHW;CTSYTTRTTLIF", "CARDLSRGDYFPYFSYHMNVW;CQSDDTANHVIF", "CARGFDTNAFDIW;CTAWDDSLSGVVF", "CTREDYW;CMQTIQLRTF")
p3 <- trackClonotypes(scdata_cl$data, target, .col = "aa") %>% vis()

(p1 + p2) / p3

Several functions may work incorrectly with paired chain data in this release of immunarch. Let us know via GitHub Issues!