This dataset contains single cell RNA sequencing data of PBMC samples from 10 bladder cancer patients. cDNAs and single cell RNA libraries were prepared following manufacturer’s user guide (10x Genomics). Each library was sequenced in HiSeq4000 (Illumina) to achieve ~300 million reads following manufacturer’s sequencing specification.

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

The data is derived from the 3k PBMC data used in scanpy & Seurat tutorials. In comes in the AnnData h5ad format.

Processed 3k PBMCs from a Healthy Donor from 10x Genomics, available at https://scanpy.readthedocs.io/en/stable/generated/scanpy.datasets.pbmc3k_processed.html Original 10X data available at http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz from this website: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k

The changes made to the original scanpy.datasets.pbmc3k_processed() data are described in this github issue: https://github.com/scverse/scverse-tutorials/issues/51

See jupyter notebook for details.

Sample Procurement

Samples were collected directly from participants as part of ImYoo's "Single-cell immune profiling from self-collected capillary blood" study, approved by Advarra IRB (Protocol #Pro00057361).

Sample Processing

Whole capillary blood samples were self-collected from participants using the TAP II device. Samples were either processed shortly after collection, or if being stored for longer than 4 hours, were kept in a styrofoam cooler with ice packs. Cells were isolated using EasySep Direct Human PBMC Isolation Kit (STEMCELL Technologies Catalog #19654) and cryopreserved using CryoStor CS10 (STEMCELL Technologies Catalog #07930). Upon thawing, samples were labeled in accordance with the MULTI-seq protocol (https://www.nature.com/articles/s41592-019-0433-8) and then processed on a 10X Genomics Chromium, using either the Chromium Next GEM Single Cell 3' Kit v3.1 (10X Genomics Product Code 1000269) or Chromium Next GEM Single Cell 3’ HT Kit v3.1 (10X Genomics Product Code 1000370). DNA libraries were sequenced on either a NovaSeq 6000 or NextSeq 550.

Data Processing

Transcriptomic sequencing data was processed using Cell Ranger v7.0.1 with default parameters. Multiplexing oligo sequencing data was processed through a custom python script that counts the number of occurrences of each sample barcode sequence and assigns it to the corresponding cell barcode. Samples were demultiplexed using a custom algorithm that estimates the background sample barcode counts, and assigns each cell a probability of belonging to each sample. Cell typing was done as part of a larger dataset and consisted of iterative manual assignments of clusters to cell types. For each cell subtype detected, a new model was trained on just the cells of that type, and the process was repeated.

Metadata Fields

• barcode: Original chromium cell barcode
• Sample IDs: Unique ID for the experimental sample that was processed with 10x Chromium, could have come from the same biological sample (identified by original_sample_id)
• Participant IDs: Unique ID for participant (here there are three participants: 2, 3 and 51)
• Cell Barcoding Runs: Unique ID for the 10x Chromium cell barcoding run in which that sample was processed. Multiple samples can be processed in a cell barcoding run.
• Lane: ID of which Chromium chip lane the cell came from
• extraction_protocol: How the PBMCs were isolated from whole blood. In this dataset all samples were processed with the TAP device.
• sample_processing_delay_seconds: The amount of time (in seconds) between when the blood was extracted from the participant and when PBMC isolation + cryopreservation was performed
• cell_barcoding_delay_days: How long PBMC samples were stored in liquid nitrogen prior to being thawed and processed on 10x
• cell_barcoding_protocol: Which single cell RNA sequencing experimental protocol was used. Here all samples were processed with 10x v3.1 chemistry.
• run_lane_batch: Concatenation of columns Cell Barcoding Runs and Lane to provide a unique ID for experimental processing batch (i.e. the DNA library)
• cell_type_level_1: Level 1 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.
• cell_type_level_2: Level 2 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.
• cell_type_level_3: Level 3 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.
• cell_type_level_4: Level 4 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.
• c1: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.
• c2: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.
• c3: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.
• c4: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.
• original_sample_id: Some samples were derived from the same originating whole blood sample. This field specifies the source of the whole blood sample.

This is the PBMC training dataset used for training Scaden models to perform deconvolution on PBMC RNA-seq datasets. It is compiled from four different PBMC scRNA-seq datasets downloaded from the 10X Genomics website (donorA, donorC, data6k, data8k).The datasets downloaded from 10X Genomics were processed and used to generate artificial bulk RNA-seq samples, which result in this dataset. A link to the 10X Genomics datasets site is provided.

Datasets for ShinyCell2 Example Applications, which include:

spatial_brain.rds: Example spatial transcriptomics dataset of sagital mouse brain slices generated using the 10x Visium v1 chemistry, processed using the Seurat spatial pipeline (https://satijalab.org/seurat/articles/spatial_vignette)

multimodal_pbmc.rds: Example CITE-seq dataset of PBMC reference containing 162,000 PBMC cells measured with 228 antibodies (https://satijalab.org/seurat/articles/multimodal_reference_mapping.html)

ArchR-ProjHeme.tar.gz: Example scATAC-seq dataset of bone marrow and peripheral blood mononuclear cells, which is used as the tutorial dataset for the ArchR pipeline (https://www.archrproject.com/articles/Articles/tutorial.html). As ArchR objects are stored in a directory containing many files, the entire folder is tarred and compressed here.

signac_pbmc.rds: Example scATAC-seq dataset of PBMC provided by 10x Genomics, which is used as the tutorial dataset for the signac pipeline (https://stuartlab.org/signac/articles/pbmc_vignette.html). Signac objects store the full list of all unique fragments across all single cells in a separate fragment file, uploaded as signac_pbmc_fragments.tsv.gz here

Gene expression (counts) scRNA-seq of co-cultured cancer- and immune cells treated with trifluridine and DMSO control assayed at two time-points (12h and 72h).

HCT116 were seeded in 6-well Nunc plates (50,000 cells/3mL/well) and precultured for 24 h before PBMCs were added at a 1:8 ratio. Co-cultures were treated with DMSO vehicle (0.1%) or FTD (3mM) for 12 h or 72 h. MACS Dead Cell Removal Kit (Miltenyi Biotec, Gladbach, DEU) was performed according to the manufacturer’s instructions on cells treated for 72 h to increase the viability of the samples before RNA-sequencing. The viability of the samples treated for 12 h was not subjected to Dead Cell Removal as the viability was already sufficient. All samples were washed in PBS with 0.04% BSA (2x1mL). Chromium Next GEM Single Cell 3’ library preparation and RNA-sequencing were performed by the SNP&SEQ Technology Platform (National Genomics Infrastructure (NGI), Science for Life Laboratory, Uppsala University, Sweden).

This data set contains processed data using Cell Ranger toolkit version 5.0.1 provided by 10x Genomics, for demultiplexing, aligning reads to the human reference genome GRCh38, and generating gene-cell unique molecular identifiers

The preprocessed Seurat object and the two Scanpy objects that can be used to run the scANANSE pipeline with.

Seurat object: preprocessed_PBMC.Rds

Scanpy objects: rna_PBMC.h5ad, atac_PBMC.h5ad

Additional raw data, used to construct the preprocessed objects, supplemented from:

https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_filtered_feature_bc_matrix.h5, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz.tbi, https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat

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 hosts example data for reproducible analysis of intra- and intercellular signaling in single cell RNA sequencing (scRNAseq) data based on transcription factor (TF) activation. We demonstrate analysis using dominoSignal on the 10X Genomics Peripheral Blood Mononuclear Cells (PBMC) data set of 2,700 cells PBMC3K. scRNA-seq data is preprocessed following the Satija Lab's Guided Clustering Tutorial. Quantification of TF activation is conducted using pySCENIC. For more details on how this analysis is conducted, please refer to the vignettes in the dominoSignal package.

Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus and cigarette smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment. Methods

The rhinovirus infection data: Primary human bronchial epithelial cells from healthy adult donors were obtained from a commercial vendor (Lonza) and cultured at air-liquid interface according to the manufacturer's instructions (Stem Cell Technologies) using reduced hydrocortisone. Cells were kept at air-liquid interface for 4 weeks before experiment; maturation of beating cilia and mucus production was confirmed using light microscope. Cells were then infected with mock or 105 PFU human rhinovirus 1A per organoid, with or without exposure to 2% cigarette smoke extract (CSE). Single cell suspension is collected by trypsin digestion at 5 days post-infection and submitted to single cell RNA sequencing using The 10X Genomics single-cell 3′ protocol. The combinatorial interferon stimulation data:

The study was approved by Institutional Review Boards at Yale University (following Yale melanoma skin SPORE IRB protocol). Healthy donors consented to donation of peripheral blood for research use. Human PBMC were isolated using Lymphoprep density gradient medium (STEMCELL). PBMC were plated at 1 million cells per ml and stimulated with 1000U/ml human IFNα2 (R&D systems), 1000U/ml human IFNβ (pbl assay science 11415), 1000U/ml human IFNγ (pbl assay science), 1ug/ml human IFN-III /IL-29 (R&D systems), 100ng/ml human IL-6 (NCI Biological Resources Branch Preclinical Biologics Repository), 20ng/ml human TNFα (R&D systems), and combinatorial cytokines IFNβ + IL-6, IFNβ + TNFα, IFNβ+ IFNγ at indicated concentrations above for up to 48 hours. Single cell RNA sequencing libraries were sequenced on Illumina NovaSeq at read length of 150bp pair-end and depth of 300 million reads per sample.

All files below are in "h5ad" format, which can be opened by the Python package AnnData (see https://anndata.readthedocs.io/en/latest). The files are organized as:

1. Single-nucleus methylcytosine sequencing (snmC-seq) of neurons from the frontal cortex of young adult mouse brains from Luo et al., 2017. The files are the preprocessed input for EpiScanpy for multiple CG imputed feature spaces, 100k base pair windows, enhancers, gene bodies, promoters and CH gene bodies.
2. processed_enhancers_CG_luo_et_al_nov2020_paper_resubmission.h5ad
3. processed_genebodies_CG_luo_et_al_nov2020_paper_resubmission.h5ad
4. processed_genebodies_CH_luo_et_al_nov2020_paper_resubmission.h5ad
5. processed_promoter_CG_luo_et_al_nov2020_paper_resubmission.h5ad

6. processed_windows_CG_luo_et_al_nov2020_paper_resubmission.h5ad

7. Single cell ATAC sequencing (scATAC-seq) from 10X Genomics, preprocessed peak count matrix of Next GEM v1.1 10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor. Peak matrices of Next GEM v1.1 10k PBMCs and whole blood fresh data (GEO:GSE129785) from Satpathy et al. 2019 (Greenleaf's lab). Concatenated 5000bp matrix of Fresh cortex from adult mouse brain (P50) from 10X Genomics and CEMBA180312_3B mouse brain sample from Fang et al. 2019.

8. atac_pbmc_10k_nextgem_fragments_macs2_peaks_outter_all_chrom.h5ad

9. atac_pbmc_10k_nextgem_fragments_merged_peaks_for_integration_greenleaf_outter_all_chrom.h5ad

10. raw_greenleaf_pre_integration_oct_2020.h5ad (Satpathy et al. 2019)

11. preprocessed_10x_genomics_5k_adulte_mouse_brain_Fang_et_al_2019_CEMBA180312_3B_5kb_windows.h5ad

Additional file 5: Table S4. List of exonic PASs identified by SCAPTURE in six PBMC scRNA-seq datasets from 10x Genomics. SCAPTURE-identified exonic PASs in each PBMC sample were individually listed. (Related Fig. S4A).

Additional file 9: Table S8. List of barcode, sample and cell type information of each single cell by DGE and DTE analyses in six PBMC scRNA-seq datasets from 10x Genomics. (Related Fig. 4f and S9A-C).

10 samples (one baseline, 9 on-treatment). Fastq files containing 5'GEx data, prepared using 10x Genomics pipeline, sequenced on Illumina HiSeq4000.

This repository contains example datasets specifically curated for the SeuratExtend tutorial, aimed at facilitating advanced analyses and visualization techniques in single-cell genomics. The datasets have been derived from publicly available data obtained from the 10X Genomics website and have undergone careful preprocessing to serve specific tutorial goals.

The collection includes the following datasets:

1. Myeloid Subset from PBMC 10k Dataset: This subset focuses on myeloid cells extracted from the larger PBMC 10k dataset, showcasing a preprocessed SeuratObject stored as an RDS file. The data serve as a primary example for demonstrating the capabilities of SeuratExtend differentiation trajectory analysis.

2. Velocyto LOOM File of Myeloid Subset from PBMC 10k Dataset: Accompanying the first dataset, this Velocyto-generated LOOM file represents a subset of the same myeloid cells, focusing on RNA velocity analyses. It provides a dynamic perspective on gene expression changes over time, enriching the tutorial with advanced single-cell transcriptomics insights.

3. SCENIC-Processed PBMC 3k Dataset: An outcome of running the SCENIC workflow on the PBMC 3k dataset, this LOOM file represents a refined dataset highlighting gene regulation networks. It serves as an advanced example for users interested in exploring gene regulatory mechanisms using SeuratExtend.

Each dataset has been subsetted and processed, making them ideal for users ranging from beginners to advanced researchers in the field of single-cell genomics. The provided data are intended for educational and tutorial purposes, allowing users to gain hands-on experience with real-world single-cell analysis scenarios.

These are processed AnnData objects (converted from Seurat objects) for GeneTrajectory tutorials (https://github.com/KlugerLab/GeneTrajectory-python/):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.

A growing number of patients presenting severe combined immunodeficiencies attributed to monoallelic RAC2 variants have been identified. The expression of the RHO GTPase RAC2 is restricted to the hematopoietic lineage. RAC2 variants have been described to cause immunodeficiencies associated with high frequency of infection, leukopenia, and autoinflammatory features. Here we show that RAC2 activating mutations induce the NLRP3 inflammasome leading to the secretion of IL-1 and IL-18 from macrophages. This induction depends on the RAC2 mutation and in particular their activation state. This suggests that inhibiting the RAC2-PAK1-NLRP3 inflammasome pathway might be considered as a potential treatment for these patients. To investigate in depth the impact of the activating variant RAC2 A59S we performed a single cell RNAseq analysis of blood circulating cells. This analysis showed increased numbers of both classical and non classical monocytes as well as myeloid dendritic cells when compared to a healthy control. In addition, and supporting our hypothesis, NLRP3 and IL-1b expression levels were increased in both monocytes and myeloid dendritic cells, while their expression in lymphocytes was not affected in our analysis. To further confirm our data, we compared monocytes isolated from PBMCs of patients harboring the RAC2 A59S with monocytes isolated from RAC2 E62K mutation. In both cases, the PBMCs were collected and frozen using the same protocol and PBMCs from patients and control healthy donors were processed in parallel. In general, the results of this study identified the RAC2 A59S mutation as a gain of function variant activating NLRP3. Methods Blood samples were processed for Chromium Single Cell Gene Expression Flex analysis according to manufacturer protocol. Whole blood cells from control (n= 8078 cells) and RAC2 A59S patient (n= 14524 cells) were analyzed. Raw sequencing data were processed using the 10× Chromium CellRanger "multi" analysis pipeline (version 7.0.0). Reads were aligned to the human reference genome (GRCh38-3.0.0) (10x Genomics).

This repository contains supplementary files from the paper: "scANANSE: gene regulatory network and motif analysis of single-cell clusters" as well as several accompanying datasets.

The supplementary files:

- Install_Rstudio.pdf

- AnanseScanpy_equivalent.pdf

The pre-processed Seurat object and the two pre-processed Scanpy objects can be used to run the scANANSE pipeline with:

- preprocessed_PBMC.Rds

- rna_PBMC.h5ad

- atac_PBMC.h5ad

Additional raw data, used to construct the pre-processed objects, supplemented from:

https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_filtered_feature_bc_matrix.h5, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz.tbi,
https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seura