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What is CZ CELLxGENE Discover?
Select two customized cell groups by leveraging metadata to identify their most distinctly expressed genes. Use the vast repository of millions of cells from the integrated CZ CELLxGENE database for comprehensive analysis. Engage in dynamic examinations of datasets to explore how gene expression patterns are shaped by spatial, environmental, and genetic factors through an intuitive no-code interface. This approach allows researchers to gain insights into existing datasets or utilize them as a springboard to uncover novel cell subtypes and states. Census enables access to any tailored segment of standardized cell data within the CZ CELLxGENE, with options for exploration in both R and Python environments. Immerse yourself in an interactive encyclopedia that features over 700 cell types, complete with detailed definitions, marker genes, lineage details, and related datasets all accessible in a single platform. In addition, researchers can browse and acquire an extensive array of standardized data collections, alongside more than 1,000 datasets that illuminate the functions of both healthy mouse and human tissues, significantly enhancing the study of cellular biology. This resource serves as an invaluable tool for scientists striving to deepen their understanding of cellular dynamics and gene expression, ultimately driving innovation in the field. Furthermore, the user-friendly interface promotes collaborative efforts among researchers, fostering a community of shared knowledge and discoveries.
What is Aspect Analytics?
Aspect Analytics introduces an advanced spatial multi-omics platform that revolutionizes collaborative data analysis and management across diverse scientific fields. This innovative tool is specifically designed for research teams engaged in drug development, biomarker discovery, pathology evaluation, and various other applications, enabling them to achieve remarkable research breakthroughs. By integrating multi-omics data into a cohesive and collaborative interface, Aspect Analytics significantly boosts teamwork across different disciplines and promotes innovative thinking. Users can effortlessly merge multiple spatial omics measurements into a singular visual format, facilitating simultaneous examination of the data. The platform adeptly amalgamates information from all spatial multi-omics assays, ensuring that critical insights are easily accessible. It also boasts the ability to securely store and manage extensive datasets, scaling up to petabytes, allowing users to retrieve their information from anywhere at any time. Additionally, the platform is designed with a scalable and customizable data infrastructure that is tailored to meet specific requirements, accommodating spatial biology data from numerous technologies and vendors, irrespective of the data format. An added advantage for users is the capability to set up automated workflows, which streamline the analysis of large datasets concurrently, ultimately enhancing the efficiency of research efforts. This groundbreaking approach not only simplifies data management but also significantly increases the opportunities for scientific discovery, thereby shaping the future of research in the life sciences. As the platform continues to evolve, it promises to keep pushing the boundaries of what is possible in the realm of multi-omics research.
Integrations Supported
API Availability
Has API
API Availability
Pricing Information
Pricing not provided
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Windows
Mac
Linux
Supported Platforms
SaaS
Customer Service / Support
Web-Based Support
Customer Service / Support
Standard Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
On-Site Training
Company Facts
Organization Name
CZ CELLxGENE
Company Location
United States
Company Website
cellxgene.cziscience.com
Company Facts
Organization Name
Aspect Analytics
Date Founded
2018
Company Location
Belgium
Company Website
www.aspect-analytics.com
Categories and Features
Bioinformatics
Not specified
Genomics Data Analysis
Not specified
Categories and Features
Genomics Data Analysis
Not specified