This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image do
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This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image do
library(cytoviewer)
library(cytomapper)
# Load example datasets
data("pancreasImages")
data("pancreasMasks")
data("pancreasSCE")
# Initialize the interactive cytoviewer application
app <- cytoviewer(image = pancreasImages,
mask = pancreasMasks,
object = pancreasSCE,
img_id = "ImageNb",
cell_id = "CellNb")
if (interactive()) {
shiny::runApp(app, launch.browser = TRUE)
}
Input: CytoImageList objects for images and masks, and a SingleCellExperiment object for cell metadata. Output: A runnable interactive Shiny application.
cytomapper directly instead of launching cytoviewer.iSEE or scater.image): A CytoImageList object containing one or multiple multi-channel images where each channel represents pixel-level intensities of a marker.mask): A CytoImageList object containing single-channel segmentation masks with integer values representing cell IDs or background.object): A SingleCellExperiment or SpatialExperiment object containing cell-specific metadata in its colData slot.img_id (image identifier column) and cell_id (cell identifier column) to link the single-cell metadata with the images and masks.NULL): A CytoImageList object containing multi-channel images.NULL): A CytoImageList object containing segmentation masks.NULL): A SingleCellExperiment or SpatialExperiment object containing cell metadata.NULL): A character string indicating the metadata column containing image identifiers.NULL): A character string indicating the metadata column containing cell identifiers.as.is = TRUE when reading in segmentation masks using loadImages() to ensure that pixel values (representing cell IDs) are scaled correctly.elementMetadata slot of both the image and mask CytoImageList objects (e.g., using mcols()) to link them properly.channelNames(cur_images) <- ... to ensure markers are correctly labeled in the interactive interface.measureObjects() to calculate cell-specific intensities and morphological features from images and masks to populate the SingleCellExperiment object.img_id or cell_id do not match exactly between the SingleCellExperiment object and the CytoImageList objects, metadata overlay and cell-level visualization will fail.as.is = TRUE in loadImages() can scale integer cell IDs, breaking the link between masks and single-cell metadata.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。