Package: scFeatures 1.7.0

Yue Cao

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

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.

Authors:Yue Cao [aut, cre], Yingxin Lin [aut], Ellis Patrick [aut], Pengyi Yang [aut], Jean Yee Hwa Yang [aut]

scFeatures_1.7.0.tar.gz
scFeatures_1.7.0.zip(r-4.5)scFeatures_1.7.0.zip(r-4.4)scFeatures_1.7.0.zip(r-4.3)
scFeatures_1.7.0.tgz(r-4.4-any)scFeatures_1.7.0.tgz(r-4.3-any)
scFeatures_1.7.0.tar.gz(r-4.5-noble)scFeatures_1.7.0.tar.gz(r-4.4-noble)
scFeatures_1.7.0.tgz(r-4.4-emscripten)scFeatures_1.7.0.tgz(r-4.3-emscripten)
scFeatures.pdf |scFeatures.html✨
scFeatures/json (API)

# Install 'scFeatures' in R:

install.packages('scFeatures', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sydneybiox/scfeatures/issues

Pkgdown:https://sydneybiox.github.io

Datasets:

example_scrnaseq - Example of scRNA-seq data
scfeatures_result - Example of scFeatures() output

On BioConductor:scFeatures-1.7.0(bioc 3.21)scFeatures-1.6.0(bioc 3.20)

cellbasedassays singlecell spatial software transcriptomics

6.31 score 10 stars 15 scripts 146 downloads 21 exports 242 dependencies

Last updated 2 months agofrom:e9d6bd3e14. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 30 2024
R-4.5-win	NOTE	Nov 30 2024
R-4.5-linux	NOTE	Nov 30 2024
R-4.4-win	NOTE	Nov 30 2024
R-4.4-mac	NOTE	Nov 30 2024
R-4.3-win	NOTE	Nov 30 2024
R-4.3-mac	NOTE	Nov 30 2024

Exports:get_num_cell_per_spot remove_mito_ribo run_association_study_report run_CCI run_celltype_interaction run_gene_cor run_gene_cor_celltype run_gene_mean run_gene_mean_celltype run_gene_prop run_gene_prop_celltype run_L_function run_Morans_I run_nn_correlation run_pathway_gsva run_pathway_mean run_pathway_prop run_proportion_logit run_proportion_ratio run_proportion_raw scFeatures

Dependencies:abind annotate AnnotationDbi AnnotationFilter ape askpass assorthead AUCell babelgene base64enc beachmat BH Biobase BiocFileCache BiocGenerics BiocIO BiocManager BiocNeighbors BiocParallel BiocSingular Biostrings bit bit64 bitops blob bluster bslib cachem caTools circlize cli cluster codetools colorspace commonmark cowplot cpp11 crayon crosstalk curl data.table DBI dbplyr DelayedArray DelayedMatrixStats deldir digest dotCall64 dplyr dqrng DT edgeR EnsDb.Hsapiens.v79 EnsDb.Mmusculus.v79 ensembldb evaluate fansi farver fastDummies fastmap filelock fitdistrplus FNN fontawesome foreach formatR fs futile.logger futile.options future future.apply generics GenomeInfoDb GenomeInfoDbData GenomicAlignments GenomicFeatures GenomicRanges ggplot2 ggrepel ggridges GlobalOptions globals glue goftest gplots graph gridExtra GSEABase GSVA gtable gtools HDF5Array here highr htmltools htmlwidgets httpuv httr ica igraph IRanges irlba isoband iterators jquerylib jsonlite KEGGREST kernlab KernSmooth knitr labeling lambda.r later lattice lazyeval leiden lifecycle limma listenv lmtest locfit magick magrittr MASS Matrix MatrixGenerics matrixStats memoise metapod mgcv mime miniUI mixtools msigdbr multtest munsell nlme openssl parallelly patchwork pbapply pheatmap pillar pkgconfig plogr plotly plyr png polyclip progressr promises ProtGenerics proxyC purrr R.methodsS3 R.oo R.utils R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML RCurl reshape2 restfulr reticulate rhdf5 rhdf5filters Rhdf5lib Rhtslib rjson rlang rmarkdown ROCR rprojroot Rsamtools RSpectra RSQLite rsvd rtracklayer Rtsne S4Arrays S4Vectors sass ScaledMatrix scales scattermore scran sctransform scuttle segmented Seurat SeuratObject shape shiny SingleCellExperiment SingleCellSignalR sitmo snow sourcetools sp spam SparseArray sparseMatrixStats SpatialExperiment spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils statmod stringi stringr SummarizedExperiment survival sys tensor tibble tidyr tidyselect tinytex UCSC.utils utf8 uwot vctrs viridisLite withr xfun XML xtable XVector yaml zlibbioc zoo

A detailed explanation of scFeatures' features

Rendered fromscFeatures_overview.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2023-11-28
Started: 2023-03-30

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Example of scRNA-seq data	example_scrnaseq
Estimate a relative number of cells per spot for spatial transcriptomics data	get_num_cell_per_spot
Remove mitochondrial and ribosomal genes, and other highly correlated genes	remove_mito_ribo
Create an association study report in HTML format	run_association_study_report
Generate cell cell communication score	run_CCI
Generate cell type interaction	run_celltype_interaction
Generate overall aggregated gene correlation	run_gene_cor
Generate cell type specific gene expression correlation	run_gene_cor_celltype
Generate overall aggregated mean expression	run_gene_mean
Generate cell type specific gene mean expression	run_gene_mean_celltype
Generate overall aggregated gene proportion expression	run_gene_prop
Generate cell type specific gene proportion expression	run_gene_prop_celltype
Generate L stats	run_L_function
Generate Moran's I	run_Morans_I
Generate nearest neighbour correlation	run_nn_correlation
Generate pathway score using gene set enrichement analysis	run_pathway_gsva
Generate pathway score using expression level	run_pathway_mean
Generate pathway score using proportion of expression	run_pathway_prop
Generate cell type proportions, with logit transformation	run_proportion_logit
Generate cell type proportion ratio	run_proportion_ratio
Generate cell type proportion raw	run_proportion_raw
Wrapper function to run all feature types in scFeatures	scFeatures
Example of scFeatures() output	scfeatures_result