Coralysis
This is the released version of Coralysis; for the devel version, see Coralysis.
Coralysis sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration
Bioconductor version: Release (3.23)
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.
Author: António Sousa [cre, aut] 
, Johannes Smolander [ctb, aut] , Sini Junttila [aut] , Laura L Elo [aut]
Maintainer: António Sousa <aggode at utu.fi>
citation("Coralysis")):António Sousa, Johannes Smolander, Sini Junttila, Laura L Elo. Coralysis: Coralysis sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration. doi:10.18129/B9.bioc.Coralysis, R package version 1.2.0, https://bioconductor.org/packages/Coralysis.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Coralysis")For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("Coralysis")| Cell States | HTML | R Script |
| Get started | HTML | R Script |
| Integration | HTML | R Script |
| Reference-mapping | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | Annotation, BatchEffect, Classification, Clustering, DifferentialExpression, DimensionReduction, GeneExpression, Proteomics, RNASeq, SingleCell, Software, Transcriptomics |
| Version | 1.2.0 |
| In Bioconductor since | BioC 3.22 (R-4.5) (1 year) |
| License | GPL-3 |
| 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 |
| System Requirements | |
| URL | https://github.com/elolab/Coralysis https://elolab.github.io/Coralysis/ |
| Bug Reports | https://github.com/elolab/Coralysis/issues |
See More
| Suggests | knitr, rmarkdown, bluster, ComplexHeatmap, circlize, scater, viridis, scRNAseq, SingleR, MouseGastrulationData, testthat (>= 3.0.0), BiocStyle, scrapper |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report, r-universe |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | Coralysis_1.2.0.tar.gz |
| Windows Binary (x86_64) | Coralysis_1.2.0.zip |
| macOS Binary (big-sur-x86_64) | Coralysis_1.2.0.tgz |
| macOS Binary (sonoma-arm64) | Coralysis_1.2.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/Coralysis |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/Coralysis |
| Package Short Url | https://bioconductor.org/packages/Coralysis/ |
| Package Downloads Report | Download Stats |