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scFeatures

scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

Bioconductor version: 3.23 · Package version: 1.12.0

scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.

Installation

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

BiocManager::install("scFeatures")

Details

MaintainerYue Cao <yue.cao@sydney.edu.au>
AuthorYue Cao [aut, cre], Yingxin Lin [aut], Ellis Patrick [aut], Pengyi Yang [aut], Jean Yee Hwa Yang [aut]
LicenseGPL-3
URLhttps://sydneybiox.github.io/scFeatures/ https://github.com/SydneyBioX/scFeatures/
Bug Reportshttps://github.com/SydneyBioX/scFeatures/issues
Downloads rank248
Source branchRELEASE_3_23
biocViewsCellBasedAssays, SingleCell, Software, Spatial, Transcriptomics

Documentation

Download

Dependencies

Depends: R (>= 4.2.0)

Imports: DelayedArray, DelayedMatrixStats, EnsDb.Hsapiens.v79, EnsDb.Mmusculus.v79, GSVA, ape, glue, dplyr, ensembldb, gtools, msigdbr, proxyC, reshape2, spatstat.explore, spatstat.geom, tidyr, AUCell, BiocParallel, rmarkdown, methods, stats, cli, MatrixGenerics, Seurat, DT

Suggests: knitr, S4Vectors, survival, survminer, BiocStyle, ClassifyR, org.Hs.eg.db, clusterProfiler, pheatmap, limma, ggplot2, plotly, igraph, data.table, enrichplot, DOSE, rmarkdown