muscat - Multi-sample multi-group scRNA-seq data analysis tools
`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.
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immunooncologydifferentialexpressionsequencingsinglecellsoftwarestatisticalmethodvisualization
11.96 score 246 stars 1 dependents 932 scripts 2.3k downloadsCATALYST - Cytometry dATa anALYSis Tools
CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.
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clusteringdataimportdifferentialexpressionexperimentaldesignflowcytometryimmunooncologymassspectrometrynormalizationpreprocessingsinglecellsoftwarestatisticalmethodvisualization
11.48 score 75 stars 4 dependents 1.0k scripts 1.2k downloadsspatialdataR - Representation of Python's spatialdata in R
R interface to Python/scverse's 'spatialdata' framework for unified spatial omics data handling. Adheres to OME-NGFF standards, providing lazy, on-disk representations for multiscale images and labels (ZarrArray), as well as points and shapes (DuckDB-backed tables). Includes handling of coordinate transformation systems and spatial utilities for cropping, masking, and querying. Integrates tabular annotations as 'SingleCellExperiment's (via 'anndataR'), enabling interoperable spatial omics workflows across R and Python.
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dataimportdatarepresentationinfrastructureimmunooncologygeneexpressiontranscriptomicssinglecellspatial
8.80 score 37 stars 2 dependents 27 scripts 129 downloadsTENxVisiumData - Visium spatial gene expression data by 10X Genomics
Collection of Visium spatial gene expression datasets by 10X Genomics, formatted into objects of class SpatialExperiment. Data cover various organisms and tissues, and include: single- and multi-section experiments, as well as single sections subjected to both whole transcriptome and targeted panel analysis. Datasets may be used for testing of and as examples in packages, for tutorials and workflow demonstrations, or similar purposes.
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experimenthubexpressiondataexperimentdatahomo_sapiens_datamus_musculus_datareproducibleresearchdatapkg
5.35 score 8 stars 28 scriptsmuscData - Multi-sample multi-group scRNA-seq data
Data package containing a collection of multi-sample multi-group scRNA-seq datasets in SingleCellExperiment Bioconductor object format.
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experimenthubexperimentdataexpressiondatageohomo_sapiens_dataimmunooncologydatasinglecelldatadatapkg
5.10 score 42 scriptsSpatialData.data - SpatialData datasets
A companion package to access scverse-curated example SpatialData stores. The datasets span spatial omics technologies such as Visium, Visium HD, Xenium, MERFISH, MIBI-TOF, and imaging mass cytometry, and are distributed as zipped Zarr stores through the Bioconductor bucket on the NSF Open Storage Network (OSN) or the scverse spatialdata sandbox. Available datasets and their metadata can be listed, and each dataset is downloaded once, cached locally with 'BiocFileCache', and read into R as a SpatialData object via 'spatialdataR' package.
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dataimportdatarepresentationinfrastructureimmunooncologygeneexpressiontranscriptomicssinglecellspatial
5.05 score 2 stars 28 scriptsSpatialData.plot - SpatialData visualization
Visualization suit for 'spatialdataR'. Current functionality includes handling of multiscale 'images', visualizing 'labels', 'points', and 'shapes'. For the latter, POINT, POLYGON, and MULTIPOLYGON geometries are supported. For all but 'images', there is support to pass information from annotation 'tables' as aesthetics (e.g., 'colData' or 'assay' data).
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spatialsinglecellvisualizationinfrastructureimmunooncologygeneexpressiontranscriptomics
4.81 score 26 scripts