This repository gathers the data and code used to generate hepatocellular carcinoma analyses in the paper presenting SeuratIntegrate. It contains the scripts to reproduce the figures presented in the article. Some figures are also available as pdf files.

To be able to fully reproduce the results from the paper, one shoud:

• download all the files
• install R 4.3.3, with correspondig base R packages (stats, graphics, grDevices, utils, datasets, methods and base)
• install R packages listed in the file sessionInfo.txt
• install the provided version of SeuratIntegrate. In an R session, run:

remotes::install_local("path/to/SeuratIntegrate_0.4.1.tar.gz")

• install (mini)conda if necessary (we used miniconda version 23.11.0)
• install the conda environments (if it fails with the *package-list.yml files, use the *package-list-from-history.yml files instead):

conda env create --file SeuratIntegrate_bbknn_package-list.yml
conda env create --file SeuratIntegrate_scanorama_package-list.yml
conda env create --file SeuratIntegrate_scvi-tools_package-list.yml
conda env create --file SeuratIntegrate_trvae_package-list.yml

• open an R session to make the conda environments usable by SeuratIntegrate:

library(SeuratIntegrate)

UpdateEnvCache("bbknn", conda.env = "SeuratIntegrate_bbknn", conda.env.is.path = FALSE)
UpdateEnvCache("scanorama", conda.env = "SeuratIntegrate_scanorama", conda.env.is.path = FALSE)
UpdateEnvCache("scvi", conda.env = "SeuratIntegrate_scvi-tools", conda.env.is.path = FALSE)
UpdateEnvCache("trvae", conda.env = "SeuratIntegrate_trvae", conda.env.is.path = FALSE)

Once done, running the code in integrate.R should produce reproducible results. Note that lines 3 to 6 from integrate.R should be adapted to the user's setup.
integrate.R is subdivided into six main parts:

1. Preparation: lines 1-56
2. Preprocessing: lines 58-74
3. Integration: lines 76-121
4. Processing of integration outputs: lines 126-267
5. Scoring of integration outputs: lines 269-353
6. Plotting: lines 380-507

Intermediate SeuratObjects have been saved between steps 3 and 4 and 5 and 6 (liver10k_integrated_object.RDS and liver10k_integrated_scored_object.RDS respectively). It is possible to start with these intermediate SeuratObjects to avoid the preceding steps, given that the Preparation step is always run before.

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.

Table of Contents

Main Description File Descriptions Linked Files Installation and Instructions

1. Main Description

This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled marengo_code_for_paper_jan_2023.R was used to generate the figures from the single-cell RNA sequencing data. The following libraries are required for script execution:

Seurat scReportoire ggplot2 stringr dplyr ggridges ggrepel ComplexHeatmap

File Descriptions

The code can be downloaded and opened in RStudios. The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113). The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots. The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.

Linked Files

This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:

Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the raw sequencing or .fastq.gz files, which are tab delimited text files. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)

Title: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity. Description: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code. Submission type: Restricted Acess. In order to gain access to the repository, you must contact the author.

Installation and Instructions

The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:

Ensure you have R version 4.1.2 or higher for compatibility.

Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.

1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).
2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.
3. Set your working directory to where the following files are located:

marengo_code_for_paper_jan_2023.R Install_Packages.R Marengo_newID_March242023.rds genes_for_heatmap_fig5F.xlsx all_res_deg_for_heat_updated_march2023.txt

You can use the following code to set the working directory in R:

setwd(directory)

1. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.
2. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.
3. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.
4. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.

This site provides access to datasets from the CPA-Perturb-seq manuscript Kowalski*, Wessels*, Linder* et al., including processed Perturb-seq datasets from HEK293FT and K562. We release these data as Seurat objects, where each object contains single-cell quantifications of gene expression (RNA assay), and in addition, quantifications of polyA site usage (polyA site assay). To explore these data, please install the PASTA (PolyA Site analysis using relative Transcript Abundance) package, which provides infrastructure and analytical tools to explore alternative polyadenylation at single-cell resolution. For each dataset, we also include a fragment file which enables visualization of read coverage plots across groups of cells.

The files include:

1. CPA_HEK293FT.Rds: Seurat object containing the HEK293 CPA-Perturb-seq dataset

2. CPA_HEK293FT_fragments.tsv.gz : Fragment file for the HEK293 dataset

3. CPA_HEK293FT_fragments.tsv.gz.tbi : Fragment file index for the HEK293 dataset

4. CPA_K562.Rds : Seurat object containing the K562 CPA-Perturb-seq dataset

5. CPA_K562_fragments.tsv.gz : Fragment file for the K562 dataset

6. CPA_K562_fragments.tsv.gz.tbi : Fragment file index for the K562 dataset

R code below:

library(PASTA)

hek <- readRDS("CPA_HEK293FT.Rds")

# remove fragment file information
Fragments(hek) <- NULL
# Update the path of the fragment file 
Fragments(hek) <- CreateFragmentObject(path = "download/CPA_HEK293FT_fragments.tsv.gz", cells = Cells(hek))

# visualize polyA site usage
PolyACoveragePlot(hek, region ="7-26212195-26213351")