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.

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  • 1
    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: 2 This Week
    Last Update:
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  • 2
    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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  • 3
    reticulate

    reticulate

    R Interface to Python

    reticulate is an R package from Posit that creates seamless interoperability between R and Python. It lets you call Python modules, classes, and functions from within R, automatically translating between R and Python data structures. Useful for combining Python tooling with R projects, data analysis, and RMarkdown reports.
    Downloads: 0 This Week
    Last Update:
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