Portal used to find and download any of data sets published on CELLxGENE. Allows to download and visually explore data to understand functionality of human tissues at cellular level. Optimized for finding, exploring, and reusing single cell data. Collections Page lists collections hosted on CELLxGENE Discover and metadata that define tissue, assay, disease, organism, and cell count for each collection. Once you find published dataset of interest on CELLxGENE Discover, you can click on the explore button below the dataset description to explore the cells of that dataset using the CELLxGENE Explorer.

h5ad objects for cellxgene visualization of scDRS results: - scdrs_tmsfacs_thin.h5ad: scDRS results for the TMS FACS data of 110,096 cells (gene count matrix removed to save space)- scdrs_demo.h5ad: demo scDRS results for 3 TMS FACS cell types and 3 diseases (gene count matrix removed to save space)

Portal used to find and download any of data sets published on CELLxGENE. Allows to download and visually explore data to understand functionality of human tissues at cellular level. Optimized for finding, exploring, and reusing single cell data. Collections Page lists collections hosted on CELLxGENE Discover and metadata that define tissue, assay, disease, organism, and cell count for each collection. Once you find published dataset of interest on CELLxGENE Discover, you can click on the explore button below the dataset description to explore the cells of that dataset using the CELLxGENE Explorer.

Assigns identifiers to datasets indexed by CELLxGENE, such those resulting from scRNA-seq experiments

Assigns identifiers to collections of datasets indexed by CELLxGENE.

CELLxGENE is an interactive data visualization and exploration tool developed by the Chan Zuckerberg Initiative that enables researchers to analyze and share single-cell genomics datasets. It provides a user-friendly interface for biologists and computational scientists to interrogate gene expression patterns across different cell types.

H5ad file can be used as demo input for Cellxgene VIP. Dataset was the re-process from Schirmer et al Nature 2019 paper by using the raw fastq files. In order to reproduce the h5ad file, details could be found in https://github.com/interactivereport/cellxgene_VIP/blob/master/notebook/MS_Nature_Rowitch_snRNAseq.ipynb
Two rds files are also included here which are the input files for sample differential expression (DE) analysis scripts (glmmTMB and Nebula)

4 Visium Spatial Transcriptomics datasets downloaded 10X Genomics data site ,and organized in the way to be used for Cellxgene VIP input.

10X_demo_data_Breast_Cancer_Block_A_Section_1 10X_demo_data_Breast_Cancer_Block_A_Section_2 10X_demo_data_Human_Heart 10X_demo_data_Human_Lymph_Node

This project utilizes the scCompass and CELLxGENE datasets with data scales of 100K, 200K, 500K, 1M, 2M, and 5M to pre-train model: GeneCompass.

snRNASeq data generated at Biogen from 3 control mouse brains. Each brain picked 3 brain regions.

Animal IDs 1, 4 and 7

Brain region codes: W: WhiteMatter H: Hippo G: GreyMatter

10X standard mm10 (3.0.0) reference was used, on cellranger 5.0.0 with --include-introns on.

Visium data generated from 3 control mouse full half brains

Animal IDs 13, 14 and 53

10X standard mm10 (2020-A) reference and spaceranger 1.1.0 was used

Cellxgene Human Aging Meta Summary

A curated summary of human single-cell RNA-seq datasets from cellxgene, focused on aging and development, with standardized metadata across experiments and assays.

  Intended Use

This summary is intended as a scaffold for integrating other curated or custom single-cell datasets (e.g., aging or disease-focused studies) with Cellxgene metadata. Enables exploration, QC, and pre-integration filtering at the experiment level. The seafront… See the full description on the dataset page: https://huggingface.co/datasets/longevity-db/cellxgene_standard.

Single-Cell RNA Data Portal for Alzheimer's Disease

The single cell Alzheimer's Disease Data Portal is an aggregated data portal created as part of the Enfield EU Funded program for the single-cell Generative Pretrained Transformer (scGPT-AD) model research. The data portal contains data from the ssREAD data portal, along with single-cell AD data from latest studies (dharsini et al, pan et al, rexach et al). The data from the individual studies where accessed through the cellXgene data portal, a vast portal for single cell data. The data have been uploaded in two seperate .zip files (part1, part2).

The single cell data follow the Annotated Data format. The core data for each sample is the gene-expression matrix, which refers to the level of expression of each gene in a single cell. Additionally, the dataset contains the `.obs` attributed which includes core cell metadata for each of the sample (cell type, brain region, braak stage, donor age, disease condition, donor gender, etc.), along with the gene names accessed via `.var` attribute.

The source data have been processed to create a unified data portal ready to be used as training dataset for a Transformer model. The main processing steps were:

• convert ssREAD data from `.qsave` format to `.h5ad` format that aligns with the AnnData framework
• discard some unprocessable data samples
• standardize metadata column names
• process categorical data to create a unified namespace (e.g.: merge `microglia` and `microgrial` cell type names into one)
• discard dimensionality reduction and clustering attributes, to make a lightweight version of the data portal, since they are not meant to be used in Transformer model training

Aggregated Data Statistics

Characteristics of Dataset grouped by Data Source

ScCompass and CELLxGENE Training Datasets: Human and Mouse for GeneCompass and Geneformer.

The single-cell analysis software market is experiencing robust growth, driven by the increasing adoption of single-cell technologies in research and clinical settings. The market's expansion is fueled by several key factors, including the decreasing cost of single-cell sequencing technologies, the rising demand for personalized medicine, and the growing need for a deeper understanding of complex biological systems. Advancements in algorithms and computational power are enabling the analysis of increasingly larger and more complex datasets, leading to more accurate and insightful results. Furthermore, the development of user-friendly software interfaces is making single-cell analysis more accessible to a broader range of researchers, fostering wider adoption across diverse research areas such as oncology, immunology, and neuroscience. The competitive landscape is characterized by a mix of established players and emerging companies, each offering unique software features and capabilities. This competitive environment fosters innovation and drives the development of more sophisticated and comprehensive analysis tools. Looking ahead, the market is projected to maintain a healthy Compound Annual Growth Rate (CAGR) throughout the forecast period (2025-2033), exceeding 15% annually. This growth is expected to be driven by continued technological advancements, expanding applications in drug discovery and development, and increased funding for research initiatives focusing on single-cell technologies. The market segmentation will likely see continued growth across various research areas and therapeutic applications. While challenges such as data storage and management, and the need for specialized expertise, will remain, the overall outlook for the single-cell analysis software market is positive, indicating significant future opportunities for both established and emerging players in this rapidly evolving sector. The integration of artificial intelligence and machine learning within these software platforms will further enhance their analytical capabilities and accelerate market growth.

Human hematopoiesis is a dynamic process that starts in utero 18 – 21 days postconception. Understanding the site- and stage-specific variation in hematopoiesis is important if we are to understand the origin of hematological disorders, many of which occur at specific points in the human lifespan. To unravel how the hematopoietic stem/progenitor cell (HSPC) compartments change during human ontogeny and the underlying gene regulatory mechanisms, we used 10x genomics platform to profile single-cell transcriptome of HSPCs sampled throughout the course of human ontogeny. This included early fetal liver (eFL) from the first trimester fetuses, paired fetal bone marrow (FBM) and FL from the same second trimester fetuses, paediatric BM (PBM), and adult BM (ABM). To increase the utility and accessibility of our single-cell dataset, we have shared the processed file in h5ad format, which is suitable for the cellxgene (https://chanzuckerberg.github.io/cellxgene/) single-cell visualization software. This platform will enable users to easily interrogate the expression of gene(s) of interest in our dataset. The R object file generated from the SingCellaR analysis (https://github.com/supatt-lab/SingCellaR) is also shared in this database. The users can use the SingCellaR functions to analyse and visualise multiple plots from this R object file.

High-throughput data are available through the NCBI database with SuperSeries accession no. GSE244594. In addition, these data are publicly available through the CellXGene database of the Chan Zuckerberg Initiative.

This is the Seurat Object of the related work.

This folder contains pre-filtered files of Tabula sapiens per tissue used to generate scvi models stored in scvi-hub. Due to inconsistencies in the cell-type resolution across donors data was filtered. Please refer to pre-processed files as adata object for the trained scvi models which contains gene filtered and minified data for the models.

Data is preprocessed data downloaded from https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5. Please refer to their data usage guide before reusing the data.

Additional file 2. Table S1: Cellxgene datasets used for annotation accuracy evaluation.

These are pre-trained models and AnnData datasets based on Tabula sapiens. Models were subsequentially uploaded to scvi-hub and this repository is there to restore the models on hugging face.

Data is preprocessed data downloaded from https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5. Please refer to their data usage guide before reusing the data.

Covid Tissue Atlas Annotated h5ad objects to use with scanpy and cellxgene.