Package: clustermole 1.1.1

clustermole: Unbiased Single-Cell Transcriptomic Data Cell Type Identification

Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.

Authors:Igor Dolgalev [aut, cre]

clustermole_1.1.1.tar.gz
clustermole_1.1.1.zip(r-4.7)clustermole_1.1.1.zip(r-4.6)clustermole_1.1.1.zip(r-4.5)
clustermole_1.1.1.tgz(r-4.6-any)clustermole_1.1.1.tgz(r-4.5-any)
clustermole_1.1.1.tar.gz(r-4.7-any)clustermole_1.1.1.tar.gz(r-4.6-any)
clustermole_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
clustermole/json (API)

# Install 'clustermole' in R:

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

Bug tracker:https://github.com/igordot/clustermole/issues

Pkgdown/docs site:https://igordot.github.io

On CRAN:

cell-type cell-type-annotation cell-type-classification cell-type-identification cell-type-matching gene-expression-signatures scrna-seq single-cell

5.55 score 15 stars 47 scripts 479 downloads 5 exports 137 dependencies

Last updated from:f343fa5e2d (on v1.1.1). Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	456
source / vignettes	OK	304
linux-release-x86_64	OK	385
macos-release-arm64	OK	271
macos-oldrel-arm64	OK	225
windows-devel	OK	341
windows-release	OK	336
windows-oldrel	OK	352
wasm-release	OK	205

Exports:%>%clustermole_enrichment clustermole_markers clustermole_overlaps read_gmt

Dependencies:abind annotate AnnotationDbi askpass assorthead base64enc beachmat BH Biobase BiocFileCache BiocGenerics biocmake BiocParallel BiocSingular Biostrings bit bit64 blob bslib cachem cli codetools cpp11 crayon crosstalk curl data.table DBI dbplyr DelayedArray DelayedMatrixStats digest dir.expiry dplyr edgeR evaluate farver fastmap filelock fontawesome formatR fs futile.logger futile.options generics GenomicRanges ggplot2 ggrepel glue graph GSEABase GSVA gtable h5mread HDF5Array highr htmltools htmlwidgets httr httr2 IRanges irlba isoband jquerylib jsonlite KEGGREST knitr labeling lambda.r later lattice lazyeval lifecycle limma locfit magick magrittr Matrix MatrixGenerics matrixStats memoise memuse mime openssl otel pillar pkgconfig plotly plyr png promises purrr R6 rappdirs RColorBrewer Rcpp reshape reshape2 rhdf5 rhdf5filters Rhdf5lib rjson rlang rmarkdown RSQLite rsvd S4Arrays S4Vectors S7 sass ScaledMatrix scales Seqinfo SingleCellExperiment singscore snow SparseArray sparseMatrixStats SpatialExperiment statmod stringi stringr SummarizedExperiment sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun XML xtable XVector yaml

Introduction to clustermole

Overview | Setup | Cell type markers | Cell types based on marker genes | Cell types based on an expression matrix

Rendered fromclustermole-intro.Rmdusingknitr::rmarkdown

Last update: 2024-01-08
Started: 2020-01-03

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Cell types based on the expression of all genes	clustermole_enrichment
Available cell type markers	clustermole_markers
Cell types based on overlap of marker genes	clustermole_overlaps
Read a GMT file into a data frame	read_gmt