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.

Scripts used for analysis of V1 and V2 Datasets.seurat_v1.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, PCA analysis, clustering, tSNE visualization. Used for v1 datasets. merge_seurat.R - merge two or more seurat objects into one seurat object. Perform linear regression to remove batch effects from separate objects. Used for v1 datasets. subcluster_seurat_v1.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA. Used for v1 datasets.seurat_v2.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, and PCA analysis. Used for v2 datasets. clustering_markers_v2.R - clustering and tSNE visualization for v2 datasets. subcluster_seurat_v2.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA analysis. Used for v2 datasets.seurat_object_analysis_v1_and_v2.R - downstream analysis and plotting functions for seurat object created by seurat_v1.R or seurat_v2.R. merge_clusters.R - merge clusters that do not meet gene threshold. Used for both v1 and v2 datasets. prepare_for_monocle_v1.R - subcluster cells of interest and perform linear regression, but not scaling in order to input normalized, regressed values into monocle with monocle_seurat_input_v1.R monocle_seurat_input_v1.R - monocle script using seurat batch corrected values as input for v1 merged timecourse datasets. monocle_lineage_trace.R - monocle script using nUMI as input for v2 lineage traced dataset. monocle_object_analysis.R - downstream analysis for monocle object - BEAM and plotting. CCA_merging_v2.R - script for merging v2 endocrine datasets with canonical correlation analysis and determining the number of CCs to include in downstream analysis. CCA_alignment_v2.R - script for downstream alignment, clustering, tSNE visualization, and differential gene expression analysis.

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.

Preprint version of the Single-Cell Tumor Immune Atlas

This upload contains:

• TICAtlas.rds: an rds file containing a Seurat object with the whole Atlas (317111 cells, RNA and integrated assays, PCA and UMAP reductions)
• TICAtlas.h5ad: an h5ad file with the whole Atlas (317111 cells, RNA assay, PCA and UMAP)
• TICAtlas_RNA.rds: an rds file containing a Seurat object of the whole Atlas but only the RNA assay (317111 cells, UMAP embedding)
• TICAtlas_downsampled_1000.rds: an rds file containing a downsampled version of the Seurat object of the whole Atlas (24834 cells, RNA and integrated assay, PCA and UMAP reductions)
• TICAtlas_downsampled_1000.h5ad: an rds file containing a downsampled version of the Seurat object of the whole Atlas (24834 cells, RNA assay, PCA and UMAP reductions)
• TICAtlas_metadata.csv: a comma-separated text file with the metadata for each of the cells

For the h5ad files, the .X slot contains the normalized data, while the .X.raw slot contains the raw counts as they were in the original datasets.

All the files contain the following patient/sample metadata variables:

• patient: assigned patient identifiers
• gender: the patient's gender (male/female/unknown)
• source: dataset of origin
• subtype: cancer type (abbreviations as indicated in the preprint)
• cluster_kmeans_k6: patients clusters, NA if filtered out
• cell_type: annotated cell type for each of the cells

If you have any issues with the metadata you can use the TICAtlas_metadata.csv file.

For more information, read our preprint and check our GitHub.

h5ad files can be read with Python using Scanpy, rds files can be read in R using Seurat. For format conversion between AnnData and Seurat we recommend SeuratDisk. For other single-cell data formats you can use sceasy.

ObjectiveTo guide animal experiments, we investigated the similarities and differences between humans and mice in aging and Alzheimer’s disease (AD) at the single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing (scRNA-seq) level.MethodsMicroglia cells were extracted from dataset GSE198323 of human post-mortem hippocampus. The distributions and proportions of microglia subpopulation cell numbers related to AD or age were compared. This comparison was done between GSE198323 for humans and GSE127892 for mice, respectively. The Seurat R package and harmony R package were used for data analysis and batch effect correction. Differentially expressed genes (DEGs) were identified by FindMarkers function with MAST test. Comparative analyses were conducted on shared genes in DEGs associated with age and AD. The analyses were done between human and mouse using various bioinformatics techniques. The analysis of genes in DEGs related to age was conducted. Similarly, the analysis of genes in DEGs related to AD was performed. Cross-species analyses were conducted using orthologous genes. Comparative analyses of pseudotime between humans and mice were performed using Monocle2.Results(1) Similarities: The proportion of microglial subpopulation Cell_APOE/Apoe shows consistent trends, whether in AD or normal control (NC) groups in both humans and mice. The proportion of Cell_CX3CR1/Cx3cr1, representing homeostatic microglia, remains stable with age in NC groups across species. Tuberculosis and Fc gamma R-mediated phagocytosis pathways are shared in microglia responses to age and AD across species, respectively. (2) Differences: IL1RAPL1 and SPP1 as marker genes are more identifiable in human microglia compared to their mouse counterparts. Most genes of DEGs associated with age or AD exhibit different trends between humans and mice. Pseudotime analyses demonstrate varying cell density trends in microglial subpopulations, depending on age or AD across species.ConclusionsMouse Apoe and Cell_Apoe maybe serve as proxies for studying human AD, while Cx3cr1 and Cell_Cx3cr1 are suitable for human aging studies. However, AD mouse models (App_NL_G_F) have limitations in studying human genes like IL1RAPL1 and SPP1 related to AD. Thus, mouse models cannot fully replace human samples for AD and aging research.

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.

Code for RSTUDIO with Seurat package integration and analysis of scRNA-Seq data for 20 GBM from Neftel et al., 2019

Single cell RNA-sequencing dataset of peripheral blood mononuclear cells (pbmc: T, B, NK and monocytes) extracted from two healthy donors.

Cells labeled as C26 come from a 30 years old female and cells labeled as C27 come from a 53 years old male. Cells have been isolated from blood using ficoll. Samples were sequenced using standard 3' v3 chemistry protocols by 10x genomics. Cellranger v4.0.0 was used for the processing, and reads were aligned to the ensembl GRCg38 human genome (GRCg38_r98-ensembl_Sept2019). QC metrics were calculated on the count matrix generated by cellranger (filtered_feature_bc_matrix). Cells with less than 3 genes per cells, less than 500 reads per cell and more than 20% of mithocondrial genes were discarded.

The processing steps was performed with the R package Seurat (https://satijalab.org/seurat/), including sample integration, data normalisation and scaling, dimensional reduction, and clustering. SCTransform method was adopted for the normalisation and scaling steps. The clustered cells were manually annotated using known cell type markers.

Files content:

- raw_dataset.csv: raw gene counts

- normalized_dataset.csv: normalized gene counts (single cell matrix)

- cell_types.csv: cell types identified from annotated cell clusters

- cell_types_macro.csv: cell macro types

- UMAP_coordinates.csv: 2d cell coordinates computed with UMAP algorithm in Seurat

This record includes training materials associated with the Australian BioCommons workshop 'Single cell RNAseq analysis in R'. This workshop took place over two, 3.5 hour sessions on 26 and 27 October 2023. Event description Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data. We will discuss the 'why' behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data. This workshop is presented by the Australian BioCommons, Queensland Cyber Infrastructure Foundation (QCIF) and the Monash Genomics and Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Lead trainers: Sarah Williams, Adele Barugahare, Paul Harrison, Laura Perlaza Jimenez Facilitators: Nick Matigan, Valentine Murigneux, Magdalena (Magda) Antczak Infrastructure provision: Uwe Winter Coordinator: Melissa Burke Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop Materials shared elsewhere: This workshop follows the tutorial 'scRNAseq Analysis in R with Seurat' https://swbioinf.github.io/scRNAseqInR_Doco/index.html Slides used to introduce key topics are available via GitHub https://github.com/swbioinf/scRNAseqInR_Doco/tree/main/slides This material is based on the introductory Guided Clustering Tutorial tutorial from Seurat. It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here.

Seurat R script.

RSTUDIO and Seurat package analysis of 4 primary GBM

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

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.

Code for RSTUDIO Seurat package analysis of 2 recurrent GBM from Yuan, Sims et al., 2018

This archive contains data of scRNAseq and CyTOF in form of Seurat objects, txt and csv files as well as R scripts for data analysis and Figure generation. A summary of the content is provided in the following. R scripts Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R Script to run inferCNV analysis: inferCNV_Zenodo.R Script to plot NATMI analysis results:NATMI_CvsA_FC0.32_Updown_Column_plot_Zenodo.R Script to conduct sub-clustering and filtering of NK cells NK_Marker_Detection_Zenodo.R Helper scripts for plotting and DEG calculation:ComputePairWiseDE_v2.R, Seurat_DE_Heatmap_RCA_Style.R RDS files General scRNA-seq Seurat objects: scRNA-seq seurat object after QC, and cell type annotation used for most analysis in the manuscript: DUKE_DataSet_Doublets_Removed_Relabeled.RDS scRNA-seq including findings e.g. from NK analysis used in the shiny app: DUKE_final_for_Shiny_App.rds Neighborhood enrichment score computed for group A across all HSPCs: Enrichment_score_global_groupA.RDS UMAP coordinates used in the article: Layout_2D_nNeighbours_25_Metric_cosine_TCU_removed.RDS SCENIC files: Regulon set used in SCENIC: 2.6_regulons_asGeneSet.Rds AUC values computed for regulons: 3.4_regulonAUC.Rds MetaData used in SCENIC cellInfo.Rds Group specific regulons for LCS: groupSpecificRegulonsBCRAblP.RDS Patient specific regulons for LSC: patientSpecificRegulonsBCRAblP.RDS Patient specificity score for LSC: PatientSpecificRegulonSpecificityScoreBCRAblP.RDS Regulon specificty score for LSC: RegulonSpecificityScoreBCRAblP.RDS BCR-ABL1 inference: HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label.RDS UMAP for HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label_UMAP.RDS HSPCs with BCR-ABL1 module scores: HSPC_metacluster_74K_with_modscore_27thmay.RDS NK sub-clustering and filtering: NK object with module scores: NK_8617cells_with_modscore_1stjune.RDS Feature genes for NK cells computed with DubStepR: NK_Cells_DubStepR NK cells Seurat object excluding contaminating T and B cells: NK_cells_T_B_17_removed.RDS NK Seurat object including neighbourhood enrichment score calculations: NK_seurat_object_with_enrichment_labels_V2.RDS txt and csv files: Proportions per cluster calculated from CyTOF: CyTOF_Proportions.txt Correlation between scRNAseq and CyTOF cell type abundance: scRNAseq_Cor_Cytof.txt Correlation between manual gating and FlowSOM clustering: Manual_vs_FlowSOM.txt GSEA results: HSPC, HSC and LSC results: FINAL_GSEA_DATA_For_GGPLOT.txt NK: NK_For_Plotting.txt TFRC and HLA expression: TFRC_and_HLA_Values.txt NATMI result files: UP-regulated_mean.csv DOWN-regulated_mean.csv Gene position file used in inferCNV: inferCNV_gene_positions_hg38.txt Module scores for NK subclusters per cell: NK_Supplementary_Module_Scores.csv Compressed folders: All CyTOF raw data files: CyTOF_Data_raw.zip Results of the patient-based classifier: PatientwiseClassifier.zip Results of the single-cell based classifier: SingleCellClassifierResults.zip For general new data analysis approaches, we recommend the readers to use the Seruat object stored in DUKE_final_for_Shiny_App.rds or to use the shiny app(http://scdbm.ddnetbio.com/) and perform further analysis from there. RAW data is available at EGA upon request using Study ID: EGAS00001005509 Revision The for_CML_manuscript_revision.tar.gz folder contains scripts and data for the paper revision including 1) Detection of the BCR-ABL fusion with long read sequencing; 2) Identification of BCR-ABL junction reads with scRNAseq; 3) Detection of expressed mutations using scRNAseq.

In the context of the Human Cell Atlas, we have created a single-cell-driven taxonomy of cell types and states in human tonsils. This repository contains the Seurat objects derived from this effort. In particular, we have datasets for each modality (scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics), as well as cell type-specific datasets. Most importantly, this is the input that we used to create the HCATonsilData package, which allows programmatic access to all this datasets within R.

Version 2 of this repository includes cells from 7 additional donors, which we used as a validation cohort to validate the cell types and states defined in the atlas. In addition, in this version we also provide the Seurat object associated with the spatial transcriptomics data (10X Visium), as well as the fragments files for scATAC-seq and Multiome

These are processed Seurat objects for the two biological datasets in GeneTrajectory inference (https://github.com/KlugerLab/GeneTrajectory/):Human myeloid dataset analysisMyeloid cells were extracted from a publicly available 10x scRNA-seq dataset (https:// support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc 10k v3). QC was performed using the same workflow in (https://github.com/satijalab/ Integration2019/blob/master/preprocessing scripts/pbmc 10k v3.R). After standard normalization, highly-variable gene selection and scaling using the Seurat R package, we applied PCA and retained the top 30 principal components. Four sub-clusters of myeloid cells were identified based on Louvian clustering with a resolution of 0.3. Wilcoxon rank-sum test was employed to find cluster-specific gene markers for cell type annotation.For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel, each bandwidth is determined by the distance to its k-nearest neighbor, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 5 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 0.5% − 75% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (11,21,8) to extract three gene trajectories. Mouse embryo skin data analysisWe separated out dermal cell populations from the newly collected mouse embryo skin samples. Cells from the wildtype and the Wls mutant were pooled for analyses. After standard normalization, highly-variable gene selection and scaling using Seurat, we applied PCA and retained the top 30 principal components. Three dermal celltypes were stratified based on the expression of canonical dermal markers, including Sox2, Dkk1, and Dkk2. For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel bandwidth, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 10 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 1% − 50% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (9,16,5) to sequentially extract three gene trajectories. To compare the differences between the wiltype and the Wls mutant, we stratified Wnt-active UD cells into seven stages according to their expression profiles of the genes binned along the DC gene trajectory.

This repository contains R Seurat objects associated with our study titled "A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma".

Single cell data contained within this object comes from MMRF Immune Atlas Consortium work.

The .rds files contains a Seurat object saved with version 4.3. This can be loaded in R with the readRDS command.

Two .RDS files are included in this version of the release.

• Discovery object: MMRF_ImmuneAtlas_Full_With_Corrected_Censored_Metadata.rds contains all aliquots belonging to the 'discovery' cohort as used in the initial paper. This represents the dataset used for initial clustering, cell annotation, and analysis.
  
  
• Discovery + Validation object: COMBINED_VALIDATION_MMRF_ImmuneAtlas_Full_Censored_Metadata.rds contains both aliquots belonging to the initial 'discovery' cohort, and aliquots belonging to the 'validation' cohort. The group each cell is derived from is listed under the 'cohort' variable. Labels related to cell annotation, including doublet status, are derived from a label transfer process as described in the paper. Labels for the original 'discovery' cohort are unchanged. UMAPs have been reconstructed with both the discovery and validation cohorts integrated.

--

The discovery object contains two assays:

• "RNA" - The raw count matrix
• "RNA_Batch_Corrected" - Counts adjusted for the combination of 'Study_Site' and 'Batch'.
  • Analysis should prefer the original RNA assay, unless using pipelines which does not support adjusting for technical covariates.

Currently, the validation object only includes the uncorrected RNA assay.

--

The object contains two umaps in the reduction slot:

• umap - will render the UMAP for the full object with all cells.
• umap.sub -contains the UMAP embeddings for individual 'compartments', as indicated by 'subcluster_V03072023_compartment'

--

Each sample has three different identifiers:

• public_id
  • Indicates a specific patient (n=263).
  • MMRF_####
  • This is a standard identifier which is used across all MMRF CoMMpass datasets
  • public_ids can map to multiple d_visit_specimen_ids and aliquot_ids
  • As of now, all public_ids have a single sample collected at Baseline.
    • This can be accessed by filtering for 'collection_event' %in% c("Baseline", "Screening") or VJ_INTERVAL == 'Baseline'
• d_visit_specimen_id
  • Indicates a specific visit by a patient (n=358)
  • MMRF_####_Y
    • Y is a number indicate that this is the 'Y' sample obtained from said patient. This does not correspond to a specific timepoint.
  • This is a standard identifier, which is used across all MMRF CoMMpass datasets
  • The purpose of the visit is indicated in 'collection_event' (Baseline, Relapse, Remmission, etc.). The approximate interval the visit corresponds to is in "VJ_INTERVAL"
  • d_visit_specimen_id uniquely maps to one public_id
  • d_visit_specimen_id can map to multiple aliquot_ids
• aliquot_id
  • Refers to the specific bone marrow aliquot sample processed (n=361)
  • MMRFA-######
  • This is a unique identifier for each processed scRNA-seq sample.
  • As of now, this uniquely maps to a combination of d_visit_specimen_id, Study_Site, and Batch
  • As of now, is an identifier specific to the MMRF ImmuneAtlas

Each cell has the following annotation information:

• subcluster_V03072023
  • These refer to an individual cluster derived from 'Seurat'.
  • Format is 'Compartment'.'Compartment-cluster'.'Compartment-subcluster'
    • 'NkT.2.2', indicates this cell is in the 'Natural Killer + T Cell compartment', was originally part of 'Cluster 2', and then was further separated into a refined subcluster 2.2'
    • If a parent cluster did not need to be further seprated, the 'Compartment-subcluster' part is omitted (e.g., 'NkT.6')
  • As of now, this uniquely maps to a specific cellID_short annotation.
  • Clustering was done on a per compartment basis
    • For most immune cell types, clustering was based on embeddings corrected for 'siteXbatch'. For Plasma, clustering was performed on embeddings corrected on a per-sample basis.
  • In the combined validation object, DISCOVERY.subcluster_V03072023 will contain values only for the discovery cohort, and have NA values for validation samples.
• subcluster_V03072023_compartment
  • These refer to one of five major compartments as identified roughly on the original UMAP. Clustering was performed on a per-compartment basis following a first pass rough annotation.
  • The possible compartments are
    • NkT (T cell + Natural Killer Cells)
    • Myeloid (Monocytes, Macrophages, Dendritic cells, Neutrophil/Granulocyte populations)
    • BEry (B Cell, Erythroblasts, bone marrow progenitor populations, pDCs)
    • Ery (Erythrocyte population)
    • Plasma (Plasma cell populations)
  • Each compartment has it's own UMAP generated, which can be accessed in the 'umap.sub' reduction
  • One cluster was isolated from all other populations, and was not assigned to a compartment. This cluster is labeled as 'Full.23'.
  • In the combined validation object, DISCOVERY.subcluster_V03072023_compartment will contain values only for the discovery cohort, and have NA values for validation samples.
• cellID_short
  • This is the individual annotation for each cluster.
  • Please see the 'Cell Population Annotation Dictionary' for further details.
  • If different seurat clusters were assigned similar annotations, the celltype annotation will be appended with a distinct cluster gene, or with '_b', '_c'
• lineage_group
  • This is an annotation driven grouping of clusters into major immune populations, as shown in Figure 2.
  • This includes "CD8", "CD4", "M" (Myeloid), "B" (B cell), "E" (Erythroid), "P" (Plasma), "Other" (HSC, Fibro, pDC_a), "LQ" (Doublet)
• isDoublet
  • This is a binary 'True' or 'False' derived from manual review of clusters following doublet analysis, as described in the paper.
  • True indicates the cluster was determined to be a doublet population.
  • This is derived from 'doublet_pred', in which 'dblet_cluster' and 'poss_dblet_cluster' were flagged as doublet populations for subsequent analysis.
  • In the validation object, the doublet status of new samples were inferred by if label transfer from the discovery cohort mapped the cell from the new sample as one of the previously identified doublet populations. The raw doublet scores from doublet finder, pegasus, or scrublet, are not included in this release.

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Each sample has the following information indicating shipment batches, for batch correction

• Study_Site
  • The center which processed a specific aliquot_id
  • EMORY, MSSM, WashU, MAYO
• Batch
  • The shipment batch the sample was associated with
  • Valued 1 to 3 for EMORY, MSSM, MAYO, and 1 to 4 for WashU
• siteXbatch
  • A combination of the above to variables, to be used for batch correction
• (Combined Validation Object only): cohort
  • Indicates if the sample was involved in the 'discovery' cohort, or 'validation' cohort. Samples in the 'validation' cohort will have labels inferred from label mapping

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Each public_id has limited demographic information based on publicly available information in the MMRF CoMMpass study.

• d_pt_sex
  • Patient sex (not self-identified). Male or Female
• d_pt_race_1
  • Patient self-identified race
• d_pt_ethnicity
  • Patient self-identified ethnicity
• d_dx_amm_age
  • Patient age at diagnosis.
  • Not reported for patients above 90 at diagnosis
• d_dx_amm_bmi
  • Patient BMI at diagnosis
• d_pt_height_cm
  • Patient height at diagnosis, in centimeters.
• d_dx_amm_weight_kg
  • Patient weight at diagnosis, in kilograms

d_specimen_visit_id contains two data points providing limited information about the visit

• collection_event
  • Description of why the sample was collected
    • e.g., 'Baseline' and 'Screening' indicates the sample was obtained prior to therapy
    • 'Relapse/Progression' indicates the sample was collected due to disease progression based on clinical assessment
    • 'Remission/Response' indicates the sample was collected due to patient entering remission based on clinical assessment
    • Samples may be collected for reasons independent of the above, such as 'Pre' or 'Post' ASCT, or for other unspecified reasons
• VJ_INTERVAL
  • Indicates the rough interval following start of therapy the sample is assigned to
    
    • "Baseline", "Month 3", "Year 2", etc.

All the single-cell raw data, along with outcome and cytogenetic information, is available at MMRF’s VLAB shared resource. Requests to access these data will be reviewed by data access committee at MMRF and any data shared will be released under a data transfer agreement that will protect the identities of patients involved in the study. Other information from the CoMMpass trial can also generally be

We profile the transcriptomes of ~30,000 mouse single cells to deconvolve the hepatic mesenchyme in healthy and fibrotic liver at high resolution. We reveal spatial zonation of hepatic stellate cells across the liver lobule, designated portal vein-associated HSC and central vein-associated HSC, and uncover an equivalent functional zonation in a mouse model of centrilobular fibrosis. Our work illustrates the power of single-cell transcriptomics to resolve key collagen-producing cells driving liver fibrosis with high precision. We provide the contents of these data as Seurat R objects.

The human adult intestinal system is a complex organ that is approximately 9 meters long and performs a variety of complex functions including digestion, nutrient absorption, and immune surveillance. We performed snRNA-seq on 8 regions of of the human intestine (duodenum, proximal-jejunum, mid-jejunum, ileum, ascending colon, transverse colon, descending colon, and sigmoid colon) from 9 donors (B001, B004, B005, B006, B008, B009, B010, B011, and B012). In the corresponding paper, we find cell compositions differ dramatically across regions of the intestine and demonstrate the complexity of epithelial subtypes. We map gene regulatory differences in these cells suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation, and organization in the human intestine, and serve as an important reference map for understanding human biology and disease. Methods For a detailed description of each of the steps to obtain this data see the detailed materials and methods in the associated manuscript. Briefly, intestine pieces from 8 different sites across the small intestine and colon were flash frozen. Nuclei were isolated from each sample and the resulting nuclei were processed with either 10x scRNA-seq using Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 (10x Genomics, 1000121) or Chromium Next GEM Chip G Single Cell Kits (10x Genomics, 1000120) or 10x multiome sequencing using Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Kits (10x Genomics, 1000283). Initial processing of snRNA-seq data was done with the Cell Ranger Pipeline (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) by first running cellranger mkfastq to demultiplex the bcl files and then running cellranger count. Since nuclear RNA was sequenced, data were aligned to a pre-mRNA reference. Initial processing of the mutiome data, including alignment and generation of fragments files and expression matrices, was performed with the Cell Ranger ARC Pipeline. The raw expression matrices from these pipelines are included here. Downstream processing was performed in R, using the Seurat package.