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 dataset contains single-cell RNA sequencing (scRNA-seq) data for human cells.

The data is derived from the publicly available GSE115189 dataset.

It provides preprocessed gene expression matrices suitable for downstream analysis.

The dataset is ideal for exploring cellular heterogeneity and gene expression patterns.

Includes quality control (QC) metrics and visualizations to assess data reliability.

Data can be used for dimensionality reduction, clustering, and visualization analyses.

Researchers can study differential gene expression across distinct cell populations.

The dataset supports the development and benchmarking of single-cell analysis tools.

Contains visual outputs such as violin plots to illustrate QC and gene expression.

Suitable for bioinformaticians, computational biologists, and single-cell researchers.

Enables exploration of transcriptional programs in various cell types.

Offers opportunities for educational purposes, tutorials, and hands-on analysis.

Provides insights into single-cell RNA-seq data preprocessing and quality assessment.

This is the GitHub repository for the single cell RNA sequencing data analysis for the human manuscript. The following essential libraries are required for script execution: Seurat scReportoire ggplot2 dplyr ggridges ggrepel ComplexHeatmap Linked File: -------------------------------------- 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. Provided below are descriptions of the linked datasets: 1. Gene Expression Omnibus (GEO) ID: GSE229626 - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - 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). 2. Sequence read archive (SRA) repository - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - 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). Please note that since the GSE submission is private, the raw data deposited at SRA may not be accessible until the embargo on GSE229626 has been lifted. 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. The following code can be used to set working directory in R: > setwd(directory) Steps: 1. Download the "Human_code_April2023.R" and "Install_Packages.R" R scripts, and the processed data from GSE229626. 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: - Human_code_April2023.R - Install_Packages.R 4. 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. 5. Open the Human_code_April2023.R R script and execute commands as necessary.

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.

singlecell seq result.rds : The Seurat Object which contains the single cell seq result in R. cellchat result.csv : The Ligand and receptor pairs by cellchat

molecular dock.pdb : The molecular dock result of BMP7 and adriamycin

Quantitative results for WB and qPCR.pzfx :

Quantitative results for WB and qPCR in Prism

Original Images for Westernblot.pdf:

Original Images for Westernblot (PDF Version)

Context

Dataset from NeurIPS2021 challenge similar to Kaggle 2022 competition: https://www.kaggle.com/competitions/open-problems-multimodal "Open Problems - Multimodal Single-Cell Integration Predict how DNA, RNA & protein measurements co-vary in single cells"

CITE-seq - joint single cell RNA sequencing + single cell measurements of CD** proteins. (https://en.wikipedia.org/wiki/CITE-Seq) (For companion dataset on scRNA-seq + scATAC-seq, see: https://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021 )

Single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122

Expression profiling by high throughput sequencing Genome binding/occupancy profiling by high throughput sequencing Summary Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors. Half the samples were measured using the 10X Multiome Gene Expression and Chromatin Accessability kit and half were measured using the 10X 3' Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site. In the competition, participants were tasked with challenges including modality prediction, matching profiles from different modalities, and learning a joint embedding from multiple modalities.

Overall design Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors.

Contributor(s) Burkhardt DB, Lücken MD, Lance C, Cannoodt R, Pisco AO, Krishnaswamy S, Theis FJ, Bloom JM Citation https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/158f3069a435b314a80bdcb024f8e422-Abstract-round2.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

Particular data: https://pubmed.ncbi.nlm.nih.gov/30673604/ Cell Rep. 2019 Jan 22;26(4):815-824.e4. doi: 10.1016/j.celrep.2018.12.099. Transcriptional Heterogeneity in Naive and Primed Human Pluripotent Stem Cells at Single-Cell Resolution Tobias Messmer 1, Ferdinand von Meyenn 2, Aurora Savino 3, Fátima Santos 3, Hisham Mohammed 3, Aaron Tin Long Lun 4, John C Marioni 5, Wolf Reik 6

Data in two variants: 1) scRNA-seq count matrix, downloaded from database of R-package "scRNAseq", see script: https://www.kaggle.com/alexandervc/rpackage-scrnaseq-downloads-datasets 2) Directly uploaded from E-MTAB-6819 https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6819/

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

This dataset contains all the Seurat objects that were used for generating all the figures in Pal et al. 2021 (https://doi.org/10.15252/embj.2020107333). All the Seurat objects were created under R v3.6.1 using the Seurat package v3.1.1. The detailed information of each object is listed in a table in Chen et al. 2021.

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

Particular data: Data from the paper: Kolodziejczyk, A. A., J. K. Kim, J. C. Tsang, T. Ilicic, J. Henriksson, K. N. Natarajan, A. C. Tuck, et al. 2015. “Single cell RNA-Sequencing of pluripotent states unlocks modular transcriptional variation.” Cell Stem Cell 17 (4): 471–85. https://pubmed.ncbi.nlm.nih.gov/26431182/

scRNA-seq count matrix, downloaded from database of R-package "scRNAseq", see script: https://www.kaggle.com/alexandervc/rpackage-scrnaseq-downloads-datasets

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article Published: 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

The dataset contains an integrated, annotated Seurat v4 object. One can load the dataset into the R environment using the code below:

seurat_obj <- readRDS('PATH/TO/DOWNLOAD/seurat.rds')

The object has three assays: (I) RNA, (II) SCT and (III) integrated.

Single cell RNA sequencing (scRNA-seq) data originating from the SeuratData package under the name "pbmc3k". This dataset is utilized for vignette demonstrations of the DamageDetective R package.

Single cell RNA seq dataset at the rds format. Readable using the R programming language.

This dataset is derived from https://zenodo.org/records/6120249 by first subsetting cells clustered as T-cells and then downsampling to 15,000 cells. We also converted it into SEURAT format in the .rds file for a tutorial on how to run starCAT from data in R. We did not generate this dataset and don't claim any rights to it.

On this Zenodo link, we share the data that is required to reproduce all the analyses from our publication "satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications".

This repository includes input transcript-level expression matrices and metadata for all datasets, as well as intermediate results and final outputs of the respective DTU analyses. For a more elaborate description of the data, we refer to the companion GitHub for our publications; https://github.com/statOmics/satuRnPaper. Note that this is version 1.0.3 of the data (uploaded on 2022-07-08). If any changes were to be made to the datasets in the future, this will also be communicated on our companion GitHub page.

This dataset contains files reconstructing single-cell data presented in 'Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing' by Herrera-Uribe & Wiarda et al. 2021. Samples of peripheral blood mononuclear cells (PBMCs) were collected from seven pigs and processed for single-cell RNA sequencing (scRNA-seq) in order to provide a reference annotation of porcine immune cell transcriptomics at enhanced, single-cell resolution. Analysis of single-cell data allowed identification of 36 cell clusters that were further classified into 13 cell types, including monocytes, dendritic cells, B cells, antibody-secreting cells, numerous populations of T cells, NK cells, and erythrocytes. Files may be used to reconstruct the data as presented in the manuscript, allowing for individual query by other users. Scripts for original data analysis are available at https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq. Raw data are available at https://www.ebi.ac.uk/ena/browser/view/PRJEB43826. Funding for this dataset was also provided by NRSP8: National Animal Genome Research Program (https://www.nimss.org/projects/view/mrp/outline/18464). Resources in this dataset:Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells 10X Format. File Name: PBMC7_AllCells.zipResource Description: Zipped folder containing PBMC counts matrix, gene names, and cell IDs. Files are as follows: matrix of gene counts* (matrix.mtx.gx) gene names (features.tsv.gz) cell IDs (barcodes.tsv.gz) *The ‘raw’ count matrix is actually gene counts obtained following ambient RNA removal. During ambient RNA removal, we specified to calculate non-integer count estimations, so most gene counts are actually non-integer values in this matrix but should still be treated as raw/unnormalized data that requires further normalization/transformation. Data can be read into R using the function Read10X().Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells Metadata. File Name: PBMC7_AllCells_meta.csvResource Description: .csv file containing metadata for cells included in the final dataset. Metadata columns include: nCount_RNA = the number of transcripts detected in a cell nFeature_RNA = the number of genes detected in a cell Loupe = cell barcodes; correspond to the cell IDs found in the .h5Seurat and 10X formatted objects for all cells prcntMito = percent mitochondrial reads in a cell Scrublet = doublet probability score assigned to a cell seurat_clusters = cluster ID assigned to a cell PaperIDs = sample ID for a cell celltypes = cell type ID assigned to a cellResource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells PCA Coordinates. File Name: PBMC7_AllCells_PCAcoord.csvResource Description: .csv file containing first 100 PCA coordinates for cells. Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells t-SNE Coordinates. File Name: PBMC7_AllCells_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells UMAP Coordinates. File Name: PBMC7_AllCells_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells t-SNE Coordinates. File Name: PBMC7_CD4only_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells UMAP Coordinates. File Name: PBMC7_CD4only_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells UMAP Coordinates. File Name: PBMC7_GDonly_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells t-SNE Coordinates. File Name: PBMC7_GDonly_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gene Annotation Information. File Name: UnfilteredGeneInfo.txtResource Description: .txt file containing gene nomenclature information used to assign gene names in the dataset. 'Name' column corresponds to the name assigned to a feature in the dataset.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells H5Seurat. File Name: PBMC7.tarResource Description: .h5Seurat object of all cells in PBMC dataset. File needs to be untarred, then read into R using function LoadH5Seurat().

This dataset contains single-cell multimodal data (scRNA-, scBCR-, and scADT-seq) for twenty-three cases of childhood BCP-ALL, including four samples each of BCR::ABL1-positive, ETV6::RUNX1-positive, and HeH, together with eleven samples of DUX4-rearranged ALL. In addition, the datset contains 9 samples of mononuclear cells, sorted CD34+ (n=3), CD19+ (n=3), and unsorted (n=3), from five normal bone marrow from healthy donors. The dataset is available as raw sequencing reads (fastq; restricted access) or as an annotated multimodal dataset (h5ad). Cells from bone marrow were used for single-cell library preparation, according to the manufacturer’s protocol (10X Genomics CG000208: Chromium Next GEM Single Cell V(D)J Reagent Kits v1.1). The libaries were sequenced on a Novaseq 6000.

This dataset contains processed single-cell RNA-sequencing (scRNA-seq) data from the PBMC 3K experiment.

It includes quality-control (QC) visualizations, cell-level metrics, clustering outputs, and exploratory analysis plots.

The dataset is designed to guide beginners and intermediate users through the essential steps of scRNA-seq preprocessing and analysis.

The PBMC 3K dataset represents human peripheral blood mononuclear cells sequenced using the 10x Genomics platform.

Included QC metrics help identify low-quality cells, doublets, stressed cells, and outliers based on standard thresholds.

The dataset covers filtering based on mitochondrial gene percentage, total UMIs, and number of detected genes.

All plots follow widely accepted scRNA-seq workflows commonly used in tools like Seurat, Scanpy, and SingleCellExperiment.

The QC violin plots illustrate distributions of nFeature_RNA, nCount_RNA, percent.mt, and other metrics used to assess cell quality.

The data also highlights the effect of filtering on overall dataset structure and variability.

Clustering-related files provide a visual understanding of how cells segregate into biologically meaningful groups.

Dimensionality-reduction plots also show patterns such as immune-cell diversity present in PBMC populations.

This dataset is suitable for hands-on learning, tutorial creation, classroom instruction, or benchmarking workflows.

It serves as a ready reference for researchers who wish to practice QC interpretation and cluster inspection.

The dataset allows quick reproduction of PBMC 3K quality-control visualizations without running the entire analysis pipeline.

It provides an accessible introduction to scRNA-seq analysis concepts for students, data scientists, and bioinformaticians.

The 10x Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform, which is capable of profiling expression of thousands of genes in tens of thousands of cells simultaneously. This platform can produce hundreds of million reads in a single experiment, making it a very challenging task to quantify expression levels of genes in individual cells due to the massive data volume. Here we present cellCounts, a new tool for efficient and accurate quanti-fication of 10x Chromium. cellCounts employs the seed-and-vote strategy to align reads to a refer-ence genome, collapses reads to UMIs (Unique Molecular Identifier) and then assigns UMIs to genes based on the featureCounts program. Using multiple real datasets, we showed that cell-Counts is ~3 times faster than cellRanger, a popular quantification program developed by 10x. Using simulation and real datasets with built-in ground truth, we demonstrated that cellCounts is markedly more accurate than cellRanger, cellCounts is implemented in R, making it easily inte-grated with other R programs for analysing Chromium data.

This dataset contains single-cell ATAC sequencing data from nineteen cases of childhood BCP-ALL and four samples of mononuclear cells from normal bone marrow from healthy donors. The dataset is available as raw sequencing reads (fastq; restricted access) or as an annotated ATAC dataset (h5ad). The libraries were prepared according to the manufacturer’s instructions (10x Genomics CG000169: Nuclei Isolation for Single Cell ATAC Sequencing; 10x Genomics CG000209: Chromium Single Cell ATAC Reagent Kits v1.1).) and sequenced on a Novaseq 6000.

Processed naive CD4 and CD8 T cell single-cell RNAseq data from human samples. The file contains a Seurat object stored as an .rds file which can be read into R with the readRDS() function. It was generated using the raw data of similar name in this project, as well as the code stored here: https://github.com/dtm2451/ProgressiveHematopoiesis