Showing 3 open source projects for "integration"

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  • 1
    Seurat

    Seurat

    R toolkit for single cell genomics

    Seurat is a comprehensive R toolkit for single-cell genomics analysis, introduced by the Satija Lab at NYGC. It supports quality control, normalization, clustering, integration of multimodal data (e.g., scRNA‑seq, spatial, CITE‑seq), and visualization. Seurat v5 introduces scalable workflows and spatial transcriptomics support, commonly used in academic and industry research for single-cell studies.
    Downloads: 1 This Week
    Last Update:
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  • 2
    Reproducible-research

    Reproducible-research

    A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.

    ...The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.
    Downloads: 0 This Week
    Last Update:
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  • 3
    TOFSIMS

    TOFSIMS

    R/Bioconductor toolkit for mass spectrometry data

    ...It supports importing raw and preprocessed data from popular instrument platforms (e.g. IONTOF, Ulvac-Phi) and provides methods for mass calibration, peak picking, and peak integration. The package allows transformation of spectra into 2D image structures (mass images), with operations such as binning, scaling, subsetting, and visual rendering. For data exploration and dimensionality reduction, it includes multivariate methods common in the ToF-SIMS community: PCA (Principal Component Analysis), MCR (Multivariate Curve Resolution), MAF (Maximum Autocorrelation Factors), and MNF (Minimum Noise Fraction). ...
    Downloads: 1 This Week
    Last Update:
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