EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. Thi
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("EBImage")readImage, writeImage, and display.translate, rotate, resize, flip, flop, and affine to image arrays.filter2, gblur) or non-linear filters (medianFilter).bwlabel or separating touching cells using distmap, watershed, and propagate (Voronoi tessellation).QuPath instead because EBImage is designed for programmatic, batch-oriented processing.ImageJ/Fiji instead because EBImage's visualization (display) is primarily 2D (browser or raster).cellpose because EBImage relies on classical intensity-based and morphological segmentation algorithms.Image class objects extending the R base class array. Supports multi-dimensional data (e.g., color channels, z-positions, time points).display (either "browser" for a JavaScript viewer or "raster" for R's built-in plotting).makeBrush or gblur.thresh) to separate foreground from background.propagate.thresh.display with method="raster" to combine image data with R's built-in plotting facilities (e.g., adding text labels).gblur before thresholding to smooth out high-frequency noise and prevent over-segmentation.distmap to generate a distance map from a binary image before applying the watershed transformation to separate touching objects.colorLabels to visualize segmentation results by color-coding objects with a random permutation of unique colors.watershed algorithm on the distance map (distmap) to separate them.colorMode of the image to Color using colorMode().fillHull function or morphological closing to fill small holes.watershed.readImage.This skill is the knowledge layer — when, why, and how to use ebimage. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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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
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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
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