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Coralysis

Coralysis sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration

Bioconductor version: 3.23 · Package version: 1.2.0

Coralysis is an R package featuring a multi-level integration algorithm for sensitive integration, reference-mapping, and cell-state identification in single-cell data. The multi-level integration algorithm is inspired by the process of assembling a puzzle - where one begins by grouping pieces based on low-to high-level features, such as color and shading, before looking into shape and patterns. This approach progressively blends the batch effects and separates cell types across multiple rounds of divisive clustering.

Installation

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

BiocManager::install("Coralysis")

Details

MaintainerAntónio Sousa <aggode@utu.fi>
AuthorAntónio Sousa [cre, aut] (ORCID: <https://orcid.org/0000-0003-4779-6459>), Johannes Smolander [ctb, aut] (ORCID: <https://orcid.org/0000-0003-3872-9668>), Sini Junttila [aut] (ORCID: <https://orcid.org/0000-0003-3754-5584>), Laura L Elo [aut] (ORCID: <https://orcid.org/0000-0001-5648-4532>)
LicenseGPL-3
URLhttps://github.com/elolab/Coralysis, https://elolab.github.io/Coralysis/
Bug Reportshttps://github.com/elolab/Coralysis/issues
Downloads rank318
Source branchRELEASE_3_23
biocViewsAnnotation, BatchEffect, Classification, Clustering, DifferentialExpression, DimensionReduction, GeneExpression, Proteomics, RNASeq, SingleCell, Software, Transcriptomics

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageCoralysis_1.2.0.tar.gz
Windows binary (x86_64)Coralysis_1.2.0.zip
macOS binary (arm64)Coralysis_1.2.0.tgz
macOS binary (x86_64)Coralysis_1.2.0.tgz
Dependencies

Depends: R (>= 4.2.0)

Imports: Matrix, aricode, LiblineaR, SparseM, ggplot2, umap, Rtsne, pheatmap, reshape2, dplyr, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, utils, RANN, sparseMatrixStats, irlba, flexclust, scran, class, matrixStats, tidyr, cowplot, uwot, scatterpie, RColorBrewer, ggrastr, ggrepel, RSpectra, BiocParallel, withr

Suggests: knitr, rmarkdown, bluster, ComplexHeatmap, circlize, scater, viridis, scRNAseq, SingleR, MouseGastrulationData, testthat (>= 3.0.0), BiocStyle, scrapper