This data is used for the Seurat version of the batch correction and integration tutorial on the Galaxy Training Network. The input data was provided by Seurat in the 'Integrative Analysis in Seurat v5' tutorial. The input dataset provided here has been filtered to include only cells for which nFeature_RNA > 1000. The other datasets were produced on Galaxy. The original dataset was published as: Ding, J., Adiconis, X., Simmons, S.K. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020). https://doi.org/10.1038/s41587-020-0465-8.

Test Data for Galaxy Tutorial "Clustering 3k PBMCs with Seurat"

`bcr_phylo_tutorial.zip` is used in the Reconstruction and analysis of B-cell lineage trees from single cell data using Immcantation tutorial.

`immcantation-BCR-Seurat-tutorial.zip` is used in the Integration of BCR and GEX data tutorial.

Necessary datasets to run the Immcantation 10x Tutorial. Below is the description of the files in the data set.

BCR_data_sample1.tsv: data corresponding to the first sample (sample 1) of the two samples analyzed in the 10x tutorial. This is the sample used to show the Change-O steps.

filtered_contig_annotations.csv: filtered contig annotations file for sample 1, output of cellranger vdj.

filtered_contig.fasta: sequence fasta file for sample 1, output of cellranger vdj.

BCR_data.tsv: AIRR rearrangement file containing the data for both samples 1 and 2 used in the 10x tutorial.

BCR.data_08112023.rds: R dataframe object containing the single-cell BCR sequencing data for both samples 1 and 2 used in the 10x tutorial.

GEX.data_08112023.rds: Seurat object containing the single-cell gene expression data used in the 10x tutorial.

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.

IFN-beta treated and control PBMCs from 8 donorstwo groups of PBMCs from Kang et al. 2017 (https://www.nature.com/articles/nbt.4042), analyzed using the Seurat sample alignment strategy as explained in the tutorial at https://satijalab.org/seurat/v2.4/immune_alignment.html and then converted to h5ad format* ~14000 cells* IFN-treated and unstimulated PBMCs from 8 donors* donor identities determined using demuxlet (see GSE96583)

This is a single cell transcriptomics dataset containing roughly 3,000 PBMCs. The original data was downloaded from the Seurat 3k PBMC tutorial: https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html. We reprocessed the dataset using the Besca package (https://github.com/bedapub/besca).

The demo datasets for scMetabolismThe demo data is the dataset of Peripheral Blood Mononuclear Cells (PBMC) from 10X Genomics open access dataset (~2,700 single cells, also used by Seurat tutorial).

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 domino2 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 domino2 package.