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Description

Choose two tailored cell groups by utilizing metadata to uncover their most significantly differentially expressed genes. Utilize the extensive collection of millions of cells from the integrated CZ CELLxGENE corpus for in-depth analysis. Conduct interactive examinations of datasets to investigate how gene expression patterns are influenced by spatial, environmental, and genetic variables through an intuitive no-code user interface. Gain insights into existing datasets or leverage them as a foundation to discover new cell subtypes and states. Census offers the capability to access any customized segment of standardized cell data available within CZ CELLxGENE, with opportunities for exploration in both R and Python. Delve into an interactive encyclopedia containing over 700 cell types that includes comprehensive definitions, marker genes, lineage information, and associated datasets all in one location. Additionally, you can browse and obtain hundreds of standardized data collections along with more than 1,000 datasets that detail the functionality of both healthy mouse and human tissues, enriching your research and understanding of cellular biology. This resource provides a valuable tool for researchers aiming to enhance their exploration of cellular dynamics and gene expression.

Description

By leveraging visual analytics through TIBCO Spotfire®, PerkinElmer Signals Translational offers a comprehensive suite of tools designed to harmonize, manage, search, aggregate, and analyze extensive datasets consistently for translational research, all while ensuring scalability. This platform, driven by TIBCO Spotfire®, supports precision medicine initiatives by providing an unparalleled solution for biomarker discovery and patient stratification. The Linear Mixed Effect App (LME) within Signals Translational empowers researchers to evaluate the influence of various factors on specific phenotypes, allowing for adjustments related to random variables during analysis. Furthermore, it enables the identification of genes significantly affecting cancer stage progression, irrespective of patient origins. Notably, the LME models excel at addressing issues such as missing values and outliers, making them a robust choice for discovering potential biomarkers. Consequently, the integration of these advanced analytics tools enhances the efficacy of translational research in identifying key biomarkers that can lead to more personalized treatment approaches.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Python
R
Signals Research Suite

Integrations

Python
R
Signals Research Suite

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

CZ CELLxGENE

Country

United States

Website

cellxgene.cziscience.com

Vendor Details

Company Name

PerkinElmer

Founded

1937

Country

United States

Website

perkinelmerinformatics.com/products/clinical-translational/signals-translational/

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