This is the accompanying data set for the paper entitled ‘T cell receptor-centric approach to streamline multimodal single-cell data analysis.’, which is currently available as a preprint (https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2). Details on the origin of the datasets, and processing steps can be found there.

The purpose of this atlas both the full dataset and down sampling version is to aid in improving the interpretability of other T cell based datasets. This can be done by adding in the down sampled object that contains up to 500 cells per annotation model or all 12 dataset to your new sample. This dataset aims to improve the capacity to identify TCR-specific signature by ensuring a well covered background, which will improve the robustness of the FindMarker Function in Seurat package.

Single-cell RNA-seq dataset for the first EV-Net use case, applied directly to a Seurat object.

The dataset originates from the Tabula Muris Atlas (Tabula Muris Consortium, 2018), and we extracted the liver (FACS) subset. To ensure data quality and relevance, we filtered the Seurat object to retain only features with at least 10 counts, present in at least 5% of either Kupffer cells or hepatocytes populations.

This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have >2 TB of disk space and >64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable.

R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html

Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘test-all-new.bash’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘test-all.log’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.

We profile the transcriptomes of non-parenchymal cell types present in healthy and cirrhotic human liver. In particular, we uncover a novel scar-associated TREM2+CD9+ macrophage subpopulation, which expands in liver fibrosis, differentiates from circulating monocytes, has a corollary population in mouse liver fibrosis and is pro-fibrogenic. We also define novel ACKR1+ and PLVAP+ endothelial cells which expand in cirrhosis, are topographically scar-restricted and enhance leucocyte transmigration.