DOI: 10.5281/zenodo.6325603
title: utility: Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR

description: <strong>Introduction</strong> The original intent of assembling a data set of publicly-available tumor-infiltrating T cells (TILs) with paired TCR sequencing was to expand and improve the scRepertoire R package. However, after some discussion, we decided to release the data set for everyone, a complete summary of the sequencing runs and the sample information can be found in the meta data of the Seurat object. This repository is the 4th version of the data, with addition of cells and changes to the workflow. <strong>Methods</strong> <em>Single-Cell Data Processing</em> The filtered gene matrices output from Cell Ranger align function from individual sequencing runs (10x Genomics, Pleasanton, CA) loaded into the R global environment. For each sequencing run cell barcodes were appended to contain a unique prefix to prevent issues with duplicate barcodes. The results were then ported into individual Seurat objects (citation), where the cells with &gt; 10% mitochondrial genes and/or 2.5x natural log distribution of counts were excluded for quality control purposes. At the individual sequencing run level, doublets were estimated using the scDblFinder (v1.4.0) R package. <em>Annotation of Cells</em> Automatic annotation was performed using the singler (v1.4.1) R package (citation) with the HPCA (citation) and Monaco (citation) data sets as references and the fine label discriminators. Individual sequencing runs were subsetted to run through the singleR algorithm in order to reduce memory demands. The output of all the singleR analyses were collated and appended to the meta data of the seurat object. Likewise, the ProjecTILs (v0.4.1) R Package (citation) was used for automatic annotation as a partially orthogonal approach. <em>Addition of TCR data</em> The filtered contig annotation T cell receptor (TCR) data for available sequencing runs were loaded into the R global environment. Individual contigs were combined using the combineTCR() function of scRepertoire (v1.3.5) R Package (citation). Clonotypes were assigned to barcodes and were multiple duplicate chains for individual cells were filtered to select for the top expressing contig by read count. The clonotype data was then added to the Seurat Object with proportion across individual patients being used to calculate frequency. <strong>Citations</strong> As of right now, there is no citation associated with the assembled data set. However if using the data, please find the corresponding manuscript for each data set in the meta.data of the single-cell object. In addition, if using the processed data, feel free to modify the language in the methods section (above) and please cite the appropriate manuscripts of the software or references that were used. <em>Itemized List of the Software Used</em> Seurat v4.0.3 - citation harmony v1.0 - citation singler v1.4.1 - citation ProjecTILs v2.0.3 - citation UCell v1.0.0 - citation scRepertoire v1.3.5 - citation <em>Itemized List of Reference Data Used</em> Human Primary Cell Atlas (HPCA) - citation Monaco Data Set - citation <strong>Future Directions</strong> Data Hosting for Interactive Analysis Easy Submission Portal for Researchers to Add Data Using the Data to Build a Reference Atlas There are areas in which we are actively hoping to develop to further facilitate the usage of the data set - if you have other suggestions, please reach out using the contact information below. <strong>Contact</strong> Questions, comments, and suggestions, please feel free to contact Nick Borcherding via this repository, email, or using twitter.