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Material for Single-cell RNA analysis (I) BIO463 Week 9
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Teaching Datasets for Single-Cell RNA-seq Analysis Course
<p>This repository contains teaching datasets used in the Single-Cell RNA-seq Analysis Course, which is part of the SeuratExtend project (<a href="https://github.com/huayc09/SeuratExtend">https://github.com/huayc09/SeuratExtend</a>). The course materials are available at <a href="https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1">https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1</a>, with code and scripts hosted at <a href="https://github.com/huayc09/single-cell-course">https://github.com/huayc09/single-cell-course</a>.</p> <p>The datasets include:</p> <ol> <li>3k Peripheral Blood Mononuclear Cells (PBMCs)</li> <li>Paired PBMC samples processed with 10x Genomics' 3' kit</li> <li>Paired PBMC samples processed with 10x Genomics' 5' kit</li> </ol> <p>All original data were obtained from 10x Genomics (<a href="https://www.10xgenomics.com/resources/datasets">https://www.10xgenomics.com/resources/datasets</a>) and processed for educational purposes.</p>
Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy
<p>This repository contains code used to produce the results in: Till Braun, Michael Rade and Maximilian Merz et al., Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy</p> <p>Contents:<br>- Instructions for using the singularity image and R packages can be found here: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity">https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity</a><br>- "seurat_harmony.Rds" and "seurat_harmony_t.Rds": These Seurat objects were used to produce Figure 2 in this publication. "seurat_tcell_obj.Rds" is a subset of "seurat_harmony.Rds" and contains only T-cells. In addition, the metadata was extended by the output of TCR-Seq (using the R package scRepertoire). The following script uses the objects: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R">https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R</a></p> <p> </p>
Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions
<p><em><strong>Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions</strong></em></p> <p> </p> <p>Tools and scripts were used to generate results in Zhang et al 2024.</p> <p>Supplementary files that were not included in the initial submission.</p> <p>Full summary statistics of eQTLs including top eQTLs and all SNP-gene pairs of each cell type, consistent.tar.gz for consistent eQTLs per cell and response.tar.gz for response eQTLs, respectively.</p>
The single-cell spatial landscape of stage III colorectal cancers
<p>We added H&E images with pathologist annotations in this version (v2). All other files are available in version 1. </p> <p> </p> <p>The Seurat and Scanpy objects for single-cell data, along with a CSV file containing coordinates and cell types for all single cells, have been deposited. Additionally, the original IMC image data has also been uploaded.</p> <p>The Seurat object is: <em>20220215_COAD_cancer_control_umapped_annotated<strong>.rds</strong></em></p> <p>The Scanpy object is: <em>20220215_COAD_cancer_control_umapped_annotated.<strong>h5ad</strong></em></p> <p> </p>
Single-cell and spatial transcriptomics of stricturing Crohn's disease
<p>This folder contains the spatial transcriptomics data + code. This code was generated by members of the Smillie Lab @ MGH and Harvard Medical School.</p> <ul> <li><strong>github.tar.gz: </strong>spatial analysis code and data</li> <li><strong>anndata.h5ad:</strong> anndata object (scanpy)</li> <li><strong>V*tar.gz:</strong> raw spatial transcriptomics files</li> </ul> <p>The <strong>github.tar.gz</strong> folder contains everything you need to reproduce the spatial transcriptomics figures. It is structured as follows:</p> <ul> <li><strong>1.BayesPrism:</strong> code for running BayesPrism on spatial data</li> <li><strong>2.SparCC:</strong> code for running SparCC on spatial data</li> <li><strong>3.Lasso: </strong>code for running lasso regression on spatial data</li> <li><strong>4.Analysis: </strong>code for reproducing all figures in the paper</li> <li><strong>4.Analysis/1.analysis.r</strong><strong>: </strong>script to reproduce all figures in the paper ***</li> <li><strong>code:</strong> code library containing all necessary functions</li> <li><strong>load_data.r: </strong>code to load the single-cell and spatial datasets</li> <li><strong>sco.rds:</strong> single-cell analysis object (10X Chromium) formatted as an R list</li> <li><strong>vis.rds:</strong> spatial analysis object (10X Visium) formatted as an R list</li> </ul> <p>All scripts are numbered. You need to run everything in order. For convenience, we include the output files for <strong>1.BayesPrism</strong>, <strong>2.SparCC</strong>, and <strong>3.Lasso</strong>, allowing you to skip straight to the analysis code in <strong>4.Analysis.</strong></p> <p>To reproduce all figures in the paper, you need to do the following:</p> <ol> <li>Edit your PROJECT_FOLDER in the header of <strong>load_data.r</strong></li> <li>Install the packages listed at the top of <strong>load_data.r</strong></li> <li>Go to the <strong>4.Analysis</strong> directory, start an interactive R session, and type:<br>> source('1.analysis.r')</li> </ol> <p>This will load the beginning of the <strong>1.analysis.r</strong> script (until the stop() statement on line 68). You can run the code in two different ways:</p> <ol> <li>You can step through the code line by line in your interactive R session (starting at line 68)</li> <li>Alternatively, remove the stop() statement from the script, then run the code start to finish</li> </ol> <p>If you encounter any errors, try to debug them using a combination of Google+ChatGPT. If you still have trouble, please contact the Smillie Lab.</p> <p><strong>Note: </strong>the single-cell and spatial code are also available on GitHub. However, the spatial analysis requires large files that cannot be hosted on GitHub. Therefore, it is better to download the code + files from Zenodo. The GitHub link is provided below:</p> <p><a href="https://github.com/LJ-Kong/fibrosis_scRNA_stRNA">https://github.com/LJ-Kong/fibrosis_scRNA_stRNA</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
<p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133</p>
Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of Ms4a3Ai14, BM chimeric mice (CD45.2 Csf2rb-/-: CD45.1 Csf2rb+/+ and CD45.2 Ifngr1-/-: CD45.1 Ifngr1+/+) using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE, BM chimeric mice (CD45.2 <em>Csf2rb</em><sup>-/-</sup>: CD45.1 <em>Csf2rb</em><sup>+/+</sup> and CD45.2 <em>Ifngr1<sup>-/-</sup></em>: CD45.1 <em>Ifngr1<sup>+/+</sup></em>) using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
single-cell RNA-seq of EHT process
<p>This file 'atfr.rds' contains onject including the raw and log-normalizd expression counts, and TF activity matrix generated from metaRegulon. In addition, the file 'cell_weight_pseudotime.rds' contains the pseudotime information.</p>
utility: Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR
<p><strong>Introduction</strong></p> <p>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 <a href="https://github.com/ncborcherding/scRepertoire">scRepertoire</a> 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. </p> <p><strong>Methods</strong></p> <p><em>Single-Cell Data Processing</em></p> <p>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 (<a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a>), where the cells with > 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.</p> <p><em>Annotation of Cells</em></p> <p>Automatic annotation was performed using the singler (v1.4.1) R package (<a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a>) with the HPCA (<a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a>) and Monaco (<a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a>) 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 (<a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a>) was used for automatic annotation as a partially orthogonal approach. </p> <p><em>Addition of TCR data</em></p> <p>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 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a>). 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.</p> <p><strong>Citations</strong></p> <p>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.</p> <p><em>Itemized List of the Software Used</em></p> <ul> <li>Seurat v4.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a></li> <li>harmony v1.0 - <a href="https://pubmed.ncbi.nlm.nih.gov/31740819/">citation</a></li> <li>singler v1.4.1 - <a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a></li> <li>ProjecTILs v2.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a></li> <li>UCell v1.0.0 - <a href="https://www.biorxiv.org/content/10.1101/2021.04.13.439670v1">citation</a></li> <li>scRepertoire v1.3.5 - <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a></li> </ul> <p><em>Itemized List of Reference Data Used</em></p> <ul> <li>Human Primary Cell Atlas (HPCA) - <a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a></li> <li>Monaco Data Set - <a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a></li> </ul> <p><strong>Future Directions</strong></p> <ul> <li>Data Hosting for Interactive Analysis</li> <li>Easy Submission Portal for Researchers to Add Data</li> <li>Using the Data to Build a Reference Atlas</li> </ul> <p>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.</p> <p><strong>Contact</strong></p> <p>Questions, comments, and suggestions, please feel free to contact Nick Borcherding via this repository, <a href="mailto:ncborch@gmail.com">email</a>, or using <a href="https://twitter.com/theHumanBorch">twitter</a>.</p>
Single-cell RNA-seq supplementary data
<p>These files are supplementary data for the article Laczik M, Erdős E, Ozgyin L, Hevessy Zs, Csősz É, Kalló G, Nagy T, Barta E, Póliska Sz, Szatmári I and Bálint BL: Extensive proteome and functional genomic profiling of variability between genetically identical human B-lymphoblastoid cells. The files are outputs from the 10x Genomics software Cell Ranger and Loupe Browser, they contain QC data for the single cell sequencing described in the article, and also comparative analyses between 3 datasets (GM22648old, GM22648new, GM22649). For further details please see the article.</p>
Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization
<p>To better understand dendritic cell states and subtypes, we collected individual single-cell RNAseq datasets from various studies and further integrated, batch corrected, and reprocessed the data using Besca (https://github.com/bedapub/besca).</p> <p>The following files are included:<br> 1) study_table_integrated_DCs.xlsx - contains a list of studies from where the datasets were gathered.<br> 2) int_dcs.raw.h5ad - An anndata object file containing the combined raw single-cell counts for DCs from individual studies. The datasets were joined based on the union of variables.<br> 3) intersection_genes_integrated_dcs.tsv - List of genes if the datasets were joined based on the intersection of variables. These variables were used in the subsequent analyses.</p> <p>4) int_dcs.annotated.h5ad - An anndata object file containing single-cell logarithmized counts for DCs data that have been integrated and reprocessed. The rows of the file contain cells, and the columns contain highly variable genes. A sparse matrix containing the logarithmized counts from all the genes (from the intersection genes integrated dcs.tsv file) can also be found (adata.raw.X) in the object. In the observations, cell-type annotation is available at three different hierarchal levels.<br> <br> This data was further used to produce results for the publication (https://jitc.bmj.com/content/10/6/e004268) on the effects of Toll-like receptor 8 agonists on conventional DCs.</p>
Inferring causal cell-cell communication from single-cell transcriptomics
<p>Data used to produce the analysis in "Inferring causal cell-cell communication from single-cell transcriptomics" by Almet and Nie (2022)</p>
Fast Intratumor Heterogeneity Inference from Single-Cell Sequencing Data (simulated data - Extended Data Figures)
<p>This data repository contains simulated data used for benchmarking HUNTRESS against the existing alternative tools. Results of the benchmarking are shown in Extended Data Figures 1-10 of the paper "Fast Intratumor Heterogeneity Inference from Single-Cell Sequencing Data" (to appear in Nature Computational Science). </p>
scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
<p>The increasing availability of single-cell multi-omics data allow to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) to infer gene regulatory network by combining single cell gene expression and chromatin accessibility profiles. This allows to study complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease development. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.</p>
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
<p>Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"</p> <p>Code available at https://github.com/MTreppner/multiomics_dgms</p>
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