This dataset contains single-cell RNA sequencing (scRNA-seq) data of 3,000 peripheral blood mononuclear cells (PBMCs) from a healthy donor, processed using the 10x Genomics Chromium platform. The raw data was obtained from 10x Genomics and subsequently aligned using Cell Ranger 8.0.1 with the GENCODE Release 47 (GRCh38.p14) reference genome.The dataset includes the following output files from the Cell Ranger pipeline:filtered_feature_bc_matrix.h5 – Filtered count matrix in HDF5 formatfiltered_feature_bc_matrix – Filtered gene-barcode matrix in directory formatraw_feature_bc_matrix – Raw gene-barcode matrix in directory formatraw_feature_bc_matrix.h5 – Raw count matrix in HDF5 formatThis dataset is valuable for researchers studying single-cell transcriptomics, immune cell profiling, and bioinformatics pipeline benchmarking.File format: HDF5 and Matrix Market (MTX)Reference Genome: GENCODE Release 47 (GRCh38.p14)Processing Pipeline: Cell Ranger 8.0.1For any questions or collaborations, please feel free to contact the uploader.

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

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 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.

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.

Data are given as counts or means ±SD. AIR–acute insulin response; AUC–area under the curve; BMI–body mass index; C-Pep–C-peptide; CRP–C-reactive protein; Glc–glucose; HEC–hyperinsulinaemic-euglycaemic clamp; IFG–impaired fasting glycaemia; IGT–impaired glucose tolerance; IHL–intrahepatic lipids; IL-6—interleukin-6; Ins—insulin; ISI–insulin sensitivity index; IVGTT–intravenous glucose tolerance test; MCP-1 –monocyte chemoattractant protein 1; MRI–magnetic resonance imaging; MRS–magnetic resonance spectroscopy; NGT–normal glucose tolerance; OGTT–oral glucose tolerance test; TAT–total adipose tissue; TNF-α –tumor necrosis factor α; VAT–visceral adipose tissueClinical data of the overall study population and the major subgroups.

Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, current start-up costs and recommended data volumes for statistical analysis remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow that combines measurement reliability metrics with latent variable extraction to infer robust expression profiles from ultra-sparse sc-RNAseq data. SALSA uses a matrix focusing approach that starts by identifying facultative genes with expression levels greater than experimental measurement precision and ends with cell clustering based on a minimal set of Profiler genes, each one a putative biomarker of cluster-specific expression profiles. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard from human frozen peripheral blood comprising 2,700 single-cell barcodes, and identified 7 major cell groups matching transcriptional profiles of peripheral blood cell types and driven agnostically by < 500 Profiler genes. Finally, we demonstrate successful implementation of SALSA in a replicative scRNA-seq scenario by using previously published DropSeq data from a multi-batch mouse retina experimental design, thereby identifying 10 transcriptionally distinct cell types from > 64,000 single cells across 7 independent biological replicates based on < 630 Profiler genes. With these results, SALSA demonstrates that robust pattern detection from scRNA-seq expression matrices only requires a fraction of the accrued data, suggesting that single-cell sequencing technologies can become affordable and widespread if meant as hypothesis-generation tools to extract large-scale differential expression effects.

Gene expression fold changes comparing infected versus non-infected cells. Up: up-regulated genes; down—down-regulated genes; UM—unmodulated genes.The most modulated genes in HIV-1 infected cells from 6h to 36h hours after infection.

Details of miRNA expression changes in cultured equine PBMCs.