InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell comm
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Launch the interactive Shiny application to analyze cell-cell communication from precomputed ligand-receptor interactions.
library(InterCellar)
InterCellar::run_app(reproducible = TRUE)
Input: Precomputed ligand-receptor interaction tables (e.g., from CellPhoneDB, CellChat, ICELLNET, or SingleCellSignalR). Output: Interactive Shiny interface in a web browser for filtering, annotation, and visualization.
graphite or biomaRt).CellChat, CellPhoneDB, or SingleCellSignalR instead.CellChat or scran directly in R.TRUE): Flag in run_app() to ensure results are reproducible across R sessions.reproducible = TRUE to ensure analysis consistency across sessions.http://127.0.0.1:6134) shown in the R console.CellChat for R-based mechanistic modeling and comparison of cell-cell communication.SingleCellSignalR for simple ligand-receptor interaction predictions and network construction.scater / scran for general single-cell RNA-seq preprocessing and clustering.还没有评价。欢迎分享你的使用感受。
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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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