README.md

In martijn-cordes/ImSpectR: R package to quantify CDR3 data for immune repertoire diversity

ImSpectR

R package to quantify CDR3 data for immune repertoire diversity

Version: 1.7.6 Author and Maintainer: Martijn Cordes Description: With this package .fsa intensity files or files with sequencing lengths representing the CDR3 region of T-cell receptors can be loaded, visualized and scored for quantification of the immune repertoire.

Installation

To install directly from GitHub, run this:

if (!require("devtools"))
install.packages("devtools")
devtools::install_github("martijn-cordes/ImSpectR")

If the devtools-based approach does not work, you can download one of the built tar-balls from the builds directory and manually install the package from source by executing the following lines (replace DOWNLOAD_FOLDER with the absolute path to the tar-ball and VERSION with the version number of the downloaded package):

install.packages("/DOWNLOAD_FOLDER/ImSpectR_VERSION.tar.gz",
                     repos = NULL,
                     type  = "source")

Analyze Spectratype data

Quick Loading .fsa files and analyze dataset with standard settings

The 22 raw fsa files of 22 Vb families of a single C57Bl/6 mouse are included as external data as an example how to start analyzing Spectratype data from scratch:

Preprocessing

library(ImSpectR)
folder <- system.file("extdata/spectratype_data/", package="ImSpectR")
spectratype_dataset <- preprocess_cdr3_spectratype(folder)

Score a single sample

score_sample(spectratype_dataset[1], no.peaks = 7)

Score complete dataset

score_dataset(spectratype_dataset, no.peaks = 7)

Load Spectratype data with advanced settings

#Load raw data
folder <- system.file("extdata/spectratype_data/", package="ImSpectR")
fsa.loaded <- storing.inds(folder)

Parameters which should be considered changing for each run when loading spectratype dataset are: - my.ladder() <- the known DNA fragment sizes used for the ladder to estimate the lengths of the sample fragments - ladd.init.thresh from ladder.info.attach() and get_basepair_positions() <- minimum fluorescent intensity (RFU) number for each ladder peak to be called a actual fragment peak.

#Preprocess
my.ladder <- c(35, 50, 75, 100, 139, 150, 160, 200, 250,300, 340, 350, 400, 450, 490, 500)
ladder.info.attach(stored=fsa.loaded, ladder=my.ladder, ladd.init.thresh=1000, draw=F,method="iter2")
spectratype_dataset <- get_basepair_positions(fsa.loaded, cols = 1, my.ladder, channel.ladder=NULL,  init.thresh=1750, ladd.init.thresh=1000)

#Score dataset
score_dataset(spectratype_dataset, no.peaks = 7)

Analyze CDR3 sequencing data

Mouse data

A control data set of healthy mice was included as a test set. The data set is comprised of 2 C57Bl/6 mice from Charles River Laboratories. Samples include spleen tissue from each mouse. Tab seperated (tsv) files were dowloaded from the Adaptive Biotech database for immunoSEQ data which contains downloadable TCR datasets from both human and mice from a multitute of research projects: https://clients.adaptivebiotech.com/pub/b4ac7a84-1e69-4d60-8254-845720454d7d

Preprocessing

library(ImSpectR)
folder <- system.file("extdata/cdr_sequencing_data/", package="ImSpectR")
sequencing_dataset <- preprocess_cdr3_seq(folder, cdr3Length_column = 5, geneFamily_column = 7)

Score a single sample

score_sample(sequencing_dataset[1], no.peaks = 7)

Score complete dataset

score_dataset(sequencing_dataset, no.peaks = 7)

Human data

We also used ImSpectR to score CDR3 data from human TCR sequencing data sequenced with the ImmunoSeq platform. The CDR3 data scored by ImSpectR was data from CMV+ PBMCs published by Suessmuth et al. (Blood, 2015). Tab seperated (tsv) files were dowloaded from the Adaptive Biotech database for immunoSEQ data: https://clients.adaptivebiotech.com/pub/b4ac7a84-1e69-4d60-8254-845720454d7d

Preprocessing

library(ImSpectR)
folder <- system.file("extdata/cdr_sequencing_data/", package="ImSpectR")
sequencing_dataset <- preprocess_cdr3_seq(folder, cdr3Length_column = 57, geneFamily_column = 58)

Score a single sample

score_sample(sequencing_dataset[1], no.peaks = 8)

Score complete dataset

score_dataset(sequencing_dataset, no.peaks = 8)

Analyze CDR3 data from 10X TCR enriched scRNA data.

Also CDR3 sequence length data from scRNA data enriched for TCR run on the 10X Chromium can be processed and scored with ImSpectR. For this analysis the "All contig anotations" csv file generated by the Cellranger pipeline is used. We tested ImSpectR on a (human) CD8+ T cells of Healthy Donor 1 freely available for download on the 10x website: https://support.10xgenomics.com/single-cell-vdj/datasets/3.0.2/vdj_v1_hs_aggregated_donor1

Preprocessing

library(ImSpectR)
folder <- "link_to_10X_data_folder/"
sequencing_10x <- preprocess_cdr3_10x(folder)

Score a single sample

score_sample(sequencing_10x[1], no.peaks = 9)

Score complete dataset

score_dataset(sequencing_10x, no.peaks = 9)

martijn-cordes/ImSpectR documentation built on Oct. 1, 2019, 5:10 a.m.