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r-httpgd
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A graphics device for R that is accessible via network protocols. This package was created to make it easier to embed live R graphics in integrated development environments and other applications. The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics. The device asynchronously serves graphics via 'HTTP' and 'WebSockets'.
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2025-03-25 |
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r-asioheaders
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'Asio' is a cross-platform C++ library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C++ approach. It is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only (provided a recent compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and released under the 'Boost Software License', Version 1.0.
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2025-03-25 |
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r-unigd
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A unified R graphics backend. Render R graphics fast and easy to many common file formats. Provides a thread safe 'C' interface for asynchronous rendering of R graphics.
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2025-03-25 |
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r-renv
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A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
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2025-03-25 |
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r-seuratdisk
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The h5Seurat file format is specifically designed for the storage and analysis of multi-modal single-cell and spatially-resolved expression experiments, for example, from CITE-seq or 10X Visium technologies. It holds all molecular information and associated metadata, including (for example) nearest-neighbor graphs, dimensional reduction information, spatial coordinates and image data, and cluster labels. We also support rapid and on-disk conversion between h5Seurat and AnnData objects, with the goal of enhancing interoperability between Seurat and Scanpy.
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2025-03-25 |
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r-soupx
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Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments. Implements the method described in Young et al. (2018) <doi:10.1101/303727>.
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2025-03-25 |
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bioconductor-milor
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This package performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear model.
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2025-03-25 |
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bioconductor-bluster
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Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results.
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2025-03-25 |
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r-seuratobject
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Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details.
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2025-03-25 |
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r-sctransform
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A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija 2019 <doi:10.1186/s13059-019-1874-1> for more details.
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2025-03-25 |
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bioconductor-cogaps
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Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.
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2025-03-25 |
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r-seurat
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A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
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2025-03-25 |
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r-seuratwrappers
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SeuratWrappers is a collection of community-provided methods and extensions for Seurat, curated by the Satija Lab at NYGC. These methods comprise functionality not presently found in Seurat, and are able to be updated much more frequently.
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2025-03-25 |
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r-clustree
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Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.
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2025-03-25 |
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r-signac
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A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart and Butler et al. (2019) <doi:10.1016/j.cell.2019.05.031>.
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2025-03-25 |
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r-harmony
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Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi.org/10.1101/461954>. Package includes a standalone Harmony function and interfaces to external frameworks.
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2025-03-25 |
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r-doubletfinder
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DoubletFinder identifies doublets by generating artificial doublets from existing scRNA-seq data and defining which real cells preferentially co-localize with artificial doublets in gene expression space. Other DoubletFinder package functions are used for fitting DoubletFinder to different scRNA-seq datasets. For example, ideal DoubletFinder performance in real-world contexts requires (I) Optimal pK selection and (2) Homotypic doublet proportion estimation. pK selection is achieved using pN-pK parameter sweeps and maxima identification in mean-variance-normalized bimodality coefficient distributions. Homotypic doublet proportion estimation is achieved by finding the sum of squared cell annotation frequencies. For more information, see our Cell Sysmtes paper https://www.cell.com/cell-systems/fulltext/S2405-4712(19)30073-0 and our github https://github.com/chris-mcginnis-ucsf/DoubletFinder
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2025-03-25 |
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r-doubletdecon
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This package is designed to identify doublets in single-cell RNA-seq data sets. It achieves this in a two-fold manner: 1) Identifying putative doublets by comparing deconvolution-based profiles of each cell to those of average synthetic doublets, which are created based on specified initial groups clusters. 2) Identifying doublet clusters by combining frequency of called doublets within a cluster (from step 1) and lack of unique gene expression compared to other clusters. Together, these results can identify individual doublets and doublet clusters for removal before continuing data analysis.
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2025-03-25 |
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r-shinydirectoryinput
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Provides an input for users to select directories via an interactive, and os native dialog, rather than having to type in paths in a textInput().
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2025-03-25 |
