SingleCellExperiment (SCE) and Seurat objects in RDS format to directly use in R. ECs+MoMac files is the merged dataset of ECs and MoMac cells used in the cell communication 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.

This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the benchmarking dataset based on simulated linear trajectories.

FILES: Data processing code

adapted_traj_sim_milo_paper.R Lightly adapted code from Dann et al. to simulate single-cell RNAseq datasets that form linear trajectories . generate_test_data_linear_traj_sim_milo_paper.R R code to assign simulated labels to datatsets generated from adapted_traj_sim_milo_paper.R. Seurat objects saved as cells_sim_linear_traj_gex_seed_*.rds. Simulated labels saved as benchmark_dataset_sim_linear_traj.csv.

Resulting datasets

cells_sim_linear_traj_gex_seed_*.rds Seurat objects generated by generate_test_data_linear_traj_sim_milo_paper.R. benchmark_dataset_sim_linear_traj.csv Cell labels generated by generate_test_data_linear_traj_sim_milo_paper.R.

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.

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

RSTUDIO and Seurat package analysis of 4 primary GBM

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

These are the raw data for HuPSA and MoPSA scRNAseq datasets. Both RDS files can be loaded into R and processed through the Seurat package.https://doi.org/10.1038/s41698-024-00667-x

Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration"

Structure

ST_berghei_liver

contains data generated during stpipeline analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4) infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h.

• count contains gene expression matrix output from stpipeline in .tsv format
• spotfiles contains coordinate files for count matrices
• images contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.
• masks contains image masks for hepaquery analysis
• distances contains distance measurements from original section sorted by timepoint as well as combined across timepoints
• cluster contains clustering information across spatial positions used in spatial enrichment analysis

STUtiility_mus_pb_ST.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in ST_berghei_liver

visium_berghei_liver

contains data generated with the spaceranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h.

• V10S29-135_A1 contains spaceranger output for section 1 for infected and control sections at 38h post-infection

• V10S29-135_B1 contains spaceranger output for section 1 for infected and control sections at 12h post-infection

• V10S29-135_C1 contains spaceranger output for section 1 for infected and control sections at 24h post-infection

• V10S29-135_D1 contains spaceranger output for section 2 for infected and control sections at 38h post-infection

se_visium.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in visium_berghei_liver

snSeq_berghei_liver

contains data generated with the cellranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.

• cellranger_cnt_out contains feature count matrix information from cell ranger output

final_merged_curated_annotations_270623.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in snSeq_berghei_liver.tar.gz

raw images.zip contains raw images for supplementary figures 20-22

adjusted images.zip contains brightness and contrast adjusted images for supplementary figures 20-22

Dan R Laks Code of Seurat analysis integration of 8PDX scRNA-Seq datasets_Xie-Laks-Parada et al., 2021

Pathways from KEGG enrichment analysis with genes of cluster3 in the heatmap for humans (Fig 6C).

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.

In the context of the Human Cell Atlas, we have created a single-cell 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.

Understanding human cardiac development is essential to improving the diagnosis and treatment of congenital heart defects. This dataset is part of a multi-modal atlas of the developing human fetal heart during the critical first trimester. Using single-nucleus RNA sequencing, we sampled nearly 50,000 cardiac nuclei from three human fetuses at 8.6, 9.0, and 10.7 post-conceptional weeks (pcw). This dataset enabled distinction of 21 cell types, including novel contractile, conductive, and stromal cells. Lymphatic endothelial, epicardial and autonomic neural and glial cells were among the new, smaller populations for which we established high-resolution transcriptional profiles. This is the *.rds file of the cells from all three hearts generated after Seurat-based integration and analysis in R.This dataset is part of a greater project also comprising spatial transcriptomics, anatomy, histology, in situ hybridization and 2D and 3D immunofluorescence, most of which is also present on Figshare. The count matrices and the raw sequencing data for the spatial transcriptomics and this dataset will be available on GEO shortly.This publication is part of the Human Cell Atlas. https://www.humancellatlas.org/publications/

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.

Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection. Methods Study participants, blood draws and processing Participants were recruited as described previously from adults who had a positive SARS-COV-2 RT-PCR test at Stanford Health Care (NCT04373148). Collection of Covid samples occurred between May to December 2020. The cohort used in this study consisted of asymptomatic (n=2), mild (n=17), and moderate (n=3) COVID-19 infections, some of whom developed long term COVID-19 (n=15). The clinical case severities at the time of diagnosis were defined as asymptomatic, moderate or mild according to the guidelines released by NIH. Long term (LT) COVID was defined as symptoms occurring 30 or more days after infection, consistent with CDC guidelines. Some participants in our study continued to have LT COVID symptoms 90 days after diagnosis (n=12). Exclusion criteria for COVID sample study were NIH severity diagnosis of severe or critical at the time of positive covid test. Samples selected for this study were obtained within 76 days of positive PCR COVID-19 test date. Healthy controls were selected who had sample collection before 2020. Informed consent was obtained from all participants. All protocols were approved by the Stanford Administrative Panel on Human Subjects in Medical Research. Peripheral blood was drawn by venipuncture and using validated and published procedures, peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll-based density gradient centrifugation, frozen in aliquots and stored in liquid nitrogen at -80°C , until thawing. A summary of participant demographics is presented in Supp. Table 1.
ILC Enrichment, single cell captures for Abseq and targeted mRNAseq Participant PBMCs were thawed, and each sample stained with Sample Tag (BD #633781) at room temperature for 20 minutes. Samples were combined in healthy control or COVID-19 tubes. Cells were surface stained with a panel of fluorochrome-conjugated antibodies (Supp. Table 2) in buffer (PBS with 0.25% BSA and 1mM EDTA) for 20 minutes at room temperature prior to immunomagnetic negative selection for ILCs. Following ILC enrichment using the EasySep human Pan-ILC enrichment kit (StemCell Technologies #17975), cells from healthy and COVID-19 recovered participants were counted and normalized before combining. ILCs were sorted using a BD FACS Aria at the Stanford FACS facility prior to incubation with AbSeq oligo-linked mAbs (Supp. Table 3). Sorted cells were processed by the Stanford Human Immune Monitoring Center (HIMC) using the BD Rhapsody platform. Library was prepared using the BD Immune Response Targeting Panel (BD Kit #633750) with addition of custom gene panel reagents (Supp. Table 4) and sequenced on Illumina NovaSeq 6000 at Stanford Genomics Sequencing Center (SGSC). ILCs were identified as Lineageneg (CD3neg, CD14neg, CD34neg, CD19neg), NKG2Aneg, CD45+ and ILCs further defined as CD127+CD161+ and as subsets: ILC1 (CD117negCRTH2neg), ILC2 (CRTH2+) and ILCp (CD117+CRTH2neg) (Supp. Fig. 1). Computational data analysis The above multi-modal setup allowed paired measurements of cellular transcriptome and cell surface protein abundance. The ILC1, ILC2 and ILCp cells were manually gated based on the abundance profile of CD127, CD117, CD161 and CRTH2 (Supp. Fig. 1). Before the integrative analysis, the complete multi-modal single cell dataset containing ILC subsets was converted into single Seurat object. All the subsequent protein-level and gene-level analyses were performed using multimodal data analysis pipeline of Seurat R package version 4.0. The normalized and scaled protein abundance profile was used for estimating the integrated harmony dimensions using runHarmony function in Seurat R package (reduction= ‘apca’ and group.by.vars = ‘batch’) . The batch corrected harmony embeddings were then used for computing the Uniform Manifold Approximation and Projection (UMAP) dimensions to visualize the clusters of ILC subsets. Differential marker analysis of surface proteins, between two groups of cells (COVID-19 and Healthy cohort), from abseq panels was computed with normalized and scaled expression values using FindMarkers function from Seurat R package (test.use=’wilcox’). Similarly, differential gene expression was performed on normalized and scaled gene expression values from between two groups of cells (COVID-19 and Healthy cohort) using the FindMarkers function from Seurat R package (test.use=’MAST’ and latent.vars=’batch’). Genes with log-fold change > 0.5 and adjusted p-value < 0.05 (method: Benjamini-Hochberg) (were considered as significant for further evaluation. The resulting adjusted p-values box-plots were plotted using ggplot2 R package (version 3.4.2) after computing the number of cells expressing a given protein or gene in each sample. Pathway enrichment analysis of DE genes was performed using web-server metascape (version 3.5). The AUCells score and gene regulatory network analysis was performed using pySCENIC pipeline (version 0.12.1). Gene regulatory network was reconstructed using GRNBoost2 algorithm and the list of TFs in humans (genome version: hg38) were obtained from cisTarget database. (https://resources.aertslab.org/cistarget). Cellular enrichment (aka AUCell) analysis that measures the activity of TF or gene signatures across all single cells was performed using aucell function in pySCENIC python library. The ggplot2 R package (version 3.4.2) was used for boxplot visualization. The differential gene co-expression analysis was performed using scSFMnet R package. Circular plots were generated using the R package circlize (version 0.4.15).

This repository comprises the intermediate results, data, and the respective script to create it, to be use on the second day of the course The Hitchhiker's Guide to scRNA-seq (08-12/07/2024, iMM, Lisbon, Portugal), focused on integration.

File description:

• data:
  • pbmcref.rds: a Seurat R object of a reference of PBMCs retrieved from the R package SeuratData (v.0.2.2.9001)
  • pbmc3k_panc8.rds: a Seurat R object of two data sets - 3k human PBMCs from 10X Genomics and pancreatic islets from indrop1 - retrieved from SeuratData package (v.0.2.2.9001)
  • jurkat.rds: a Seurat R object comprising three data sets - Jurkat, HEK293T and Jurkat:HEK293T (50:50) - retrieved from 10X genomics and published by Zheng et al., 2017
  • ifnb.rds: a Seurat R object of two human PBMCs data sets - resting/control and interferon-stimulated - retrieved from the R package SeuratData (v.0.2.2.9001)
  • covid.rds: a Seurat R object of a COVID-19 PBMCs data set from Guo et al., 2020 retrieved from cziscience
• 01_create_datasets.R: R script used to retrieve and parse all the Seurat R objects mentioned above
• results.zip: compressed folder with intermediate results used for the hands-on exercises

The attached datasets comprised of the merging of 21 high quality single cell T cell based dataset that had both the TCR-seq and GEx. The object contains ~1.3 paired TCR-seq with GEx in the Seurat Object (supercluster_added_ID-240531.rds). We also included the original identifiers in the Sup_Update_labels.csv a. See our https://stegor.readthedocs.io/en/latest/ for how we processed the 12 datasets (V2) and decided on the current 47 T cell annotation models using scGate (TcellFunction). Additionally, based on collaborator recommendataion, we have also now included a simpler T cell annotion model in STEGO.R process (Tsimplefunctions). This is the accompanying data set for the paper entitled ‘T cell receptor-centric approach to streamline multimodal single-cell data analysis.’, which is currently available as a preprint (https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2). Details on the origin of the datasets, and processing steps can be found there. The purpose of this atlas both the full dataset and down sampling version is to aid in improving the interpretability of other T cell based datasets. This can be done by adding in the down sampled object that contains up to 500 cells per annotation model. This dataset aims to improve the capacity to identify TCR-specific signature by ensuring a well covered background, which will improve the robustness of the FindMarker Function in Seurat package.

Codes and single cell data for "Single cell atlas of the neonatal small intestine with necrotizing enterocolitis" paper. Folder: Cell_ranger_output Data produced using 10x sequencing. This folder contains all the Cell Ranger output raw files from all 11 subjects used for single cell RNA sequencing (5x Neonatal- S1056, S1127, S1212, S1214, S1082, 6x NECs- S1021, S1074, S1095, S1109, S1155, S1193). The neonatal subjects had two 10X runs (except subject 1082) - one done on CD45 negatively selected small intestinal cells and the other on non-selected small intestinal cells. The NEC subjects had one run each (subject 1074 was sequenced twice) with no selection due to low yield in the selected runs. Folder: Seurat_pipeline Done with Seurat package version 3.2.2 This folder contains R scripts for Seurat analysis and script for background (BG) subtraction of the data. BG_subtraction_before_seurat.R - Script that creates a BG subtracted matrix for each CellRanger output before Seurat analysis. seurat_pipline_all_cells_NEC.R - Script for Seurat analysis for the full atlas. Final_seurat_obj_after_filtration_all_cells.rds – rds file of final Seurat object of all cells after pipeline filtration Files description in “RDS_files_for_seurat_pipeline” folder: list_UMIs_after_BG_subtruction.rds – rds file containing a list of all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script. list_metadata_after_BG_subtruction.rds – rds file containing a list of metadata for all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script. Folder: Deconvolution_analysis Done on Matlab R2019b This folder contains the script for deconvolution analysis and its input files. Deconvolution_parsing_NEC_for_zenodo.m - Matlab script to create box plots of deconvolution results and their q- values. Files description: deconvolution_original8_clusters.mat – structure of deconvolution results. Signature cells were split into the 8 major cell type groups shown in figure 1. cellanneal_split_clust_results.mat – structure of deconvolution results. Signature cells were split into sub groups for each cell type group. Folder: Ligand-receptor_interaction_analysis This folder contains matlab and R scripts for ligand-receptor interaction analysis. run_permutations_for_lig_rec_ratio_tensor_Zenodo.m – main script to run ligand-receptor analysis. calculate_ratio_ligand_receptor.m – function used by the main script. Interactions_visualizations_heatmaps.R – R script for heatmap visualization. Files description in “Mat_files_for_analysis” folder: all_cells_split_for_interactions.mat – single cell structure of all cells. Cells are split into subgroups for ligand receptor analysis. ramilowsky.mat – ligand-receptor lists taken from Ramilowski et al. 2015 to use in main script and function. tensor_real_q100permutations.mat – output mat file of the interactions ratio, q values and ligand-receptor names for heatmaps visualization in R (input file for “Interactions_visualizations_heatmaps.R”).

Single cell RNA sequencing (drop-seq) data of forebrain organoids carrying pathogenic MAPT R406W and V337M mutations. Organoids were generated from 5 heterozygous donor lines (two R406W lines and three V337M lines) and respective CRISPR-corrected isogenic controls. Organoids were also generated from one homozygous R406W donor line. Single-cell sequencing was performed at 1, 2, 3, 4, 6 and 8 months of organoid maturation. Methods Single-cell transcriptomes were obtained using drop-seq (Macosko et al., 2015, https://doi.org/10.1016/j.cell.2015.05.002). Counts matrices were generated using the Drop-seq tools package (Macosko et al. 2015), with full details available online (https://github.com/broadinstitute/Drop-seq/files/2425535/Drop-seqAlignmentCookbookv1.2Jan2016.pdf). Briefly, raw reads were converted to BAM files, cell barcodes and UMIs were extracted, and low-quality reads were removed. Adapter sequences and polyA tails were trimmed, and reads were converted to Fastq for STAR alignment (STAR version 2.6). Mapping to human genome (hg19 build) was performed with default settings. Reads mapped to exons were kept and tagged with gene names, beads synthesis errors were corrected, and a digital gene expression matrix was extracted from the aligned library. We extracted data from twice as many cell barcodes as the number of cells targeted (NUM_CORE_BARCODES = 2x # targeted cells). Downstream analysis was performed using Seurat 3.0 in R version 3.6.3. An individual Seurat object was generated for each sample, and filtered and clustered individually. Cells with < 300 genes detected were filtered out, as were cells with > 10% mitochondrial gene content. Counts data were log-normalized using the default NormalizeData function and the default scale of 1e4. Then, the top 2000 variable genes were identified using the Seurat FindVariableFeatures function (selection.method = “vst”, nfeatures = 2000), followed by scaling and centering using the default ScaleData function. Principal Components Analysis was carried out on the scaled expression values of the 2000 top variable genes, and the cells were clustered using the first 50 principal components (PCs) as input in the FindNeighbors function, and a resolution of 0.4 in the FindClusters function. Non-linear dimensionality reduction was performed by running UMAP on the first 50 PCs. Following clustering and dimensionality reduction, putative cell doublets were identified using DoubletFinder (McGinnis et al. 2019; https://doi.org/10.1016/j.cels.2019.03.003), assuming a doublet formation rate of 5%. For each sample, the optimal pK value was identified based on the results of paramSweep_vs, summarizeSweep and find.pK functions of the DoubletFinder package. Instead of using the default paramSweep_vs function, we extended the upper range of computed pK values to 1.2. We visually verified cells identified as doublets had high nFeatures (number of genes expressed) by plotting the pANN metric against nFeatures. For samples not showing this correlation, we adjusted the pK value to the next highest peak in the pK/BCmetric plot. Finally, the individual Seurat objects were merged.