R Data Integration Tools

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Browse free open source R Data Integration Tools and projects below. Use the toggles on the left to filter open source R Data Integration Tools by OS, license, language, programming language, and project status.

  • Passwordless authentication enables a secure and frictionless experience for your users | Auth0 Icon
    Over two-thirds of people reuse passwords across sites, resulting in an increasingly insecure e-commerce ecosystem. Learn how passwordless can not only mitigate these issues but make the authentication experience delightful. Implement Auth0 in any application in just five minutes
  • The only retail POS that helps you run your entire business Icon
    The only retail POS that helps you run your entire business

    Built for retail stores and restaurants

    Lightspeed is a cloud-based Point of Sales (POS) and eCommerce solution. Built for retail stores and restaurants, Lightspeed provides businesses with a simple way to build, manage, and grow their operations, and create an exceptional customer experience. Lightspeed offers a complete set of functionalities, including inventory management, reporting and analytics, multi-payments, customer loyalty, and training and support.
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    Harmony Data Integration

    Harmony Data Integration

    Fast, sensitive and accurate integration of single-cell data

    Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets. It is especially useful for large single-cell datasets such as single-cell RNA-seq. Harmony has been tested on R versions =4. Please consult the DESCRIPTION file for more details on required R packages. Harmony has been tested on Linux, OS X, and Windows platforms.
    Downloads: 0 This Week
    Last Update:
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  • 2
    nichenetr

    nichenetr

    NicheNet: predict active ligand-target links between interacting cells

    nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. This model of prior information on potential ligand-target links can then be used to infer active ligand-target links between interacting cells. NicheNet prioritizes ligands according to their activity (i.e., how well they predict observed changes in gene expression in the receiver cell) and looks for affected targets with high potential to be regulated by these prioritized ligands.
    Downloads: 0 This Week
    Last Update:
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