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looking4clusters

Interactive Visualization of scRNA-Seq

Bioconductor version: 3.23 · Package version: 1.2.0

Enables the interactive visualization of dimensional reduction, clustering, and cell properties for scRNA-Seq results. It generates an interactive HTML page using either a numeric matrix, SummarizedExperiment, SingleCellExperiment or Seurat objects as input. The input data can be projected into two-dimensional representations by applying dimensionality reduction methods such as PCA, MDS, t-SNE, UMAP, and NMF. Displaying multiple dimensionality reduction results within the same interface, with interconnected graphs, provides different perspectives that facilitate accurate cell classification. The package also integrates unsupervised clustering techniques, whose results that can be viewed interactively in the graphical interface. In addition to visualization, this interface allows manual selection of groups, labeling of cell entities based on processed meta-information, generation of new graphs displaying gene expression values for each cell, sample identification, and visual comparison of samples and clusters.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("looking4clusters")

Details

MaintainerDavid Barrios <metal@usal.es>
AuthorDavid Barrios [aut, cre] (ORCID: <https://orcid.org/0000-0003-4465-0200>), Angela Villaverde [aut] (ORCID: <https://orcid.org/0000-0002-7337-7218>), Carlos Prieto [aut] (ORCID: <https://orcid.org/0000-0001-8178-9768>)
LicenseGPL-2 | GPL-3
URLhttps://github.com/BioinfoUSAL/looking4clusters/
Bug Reportshttps://github.com/BioinfoUSAL/looking4clusters/issues/
Downloads rank118
Source branchRELEASE_3_23
biocViewsClassification, Clustering, DataRepresentation, GeneExpression, MultipleComparison, Software, Visualization

Documentation

Download

Dependencies

Depends: R (>= 4.5.0)

Imports: stats, utils, SummarizedExperiment, SingleCellExperiment, BiocBaseUtils, jsonlite

Suggests: knitr, rmarkdown, Seurat, parallelDist, uwot, NMF, fpc, dendextend, cluster, Rtsne, scRNAseq, Matrix