Package: RankMap 1.1.1

Jinming Cheng

RankMap: Rank-based reference mapping for fast and robust cell type annotation in spatial and single-cell transcriptomics

RankMap is a fast and scalable tool for reference-based cell type annotation of single-cell and spatial transcriptomics data. It uses ranked gene expression and multinomial regression to achieve robust predictions, even with partial gene coverage. Compatible with Seurat, SingleCellExperiment, and SpatialExperiment objects, RankMap offers flexible preprocessing and significantly faster runtime than tools like SingleR, Azimuth, and RCTD.

Authors:Jinming Cheng [aut, cre]

RankMap_1.1.1.tar.gz
RankMap_1.1.1.zip(r-4.7)RankMap_1.1.1.zip(r-4.6)RankMap_1.1.1.zip(r-4.5)
RankMap_1.1.1.tgz(r-4.6-any)RankMap_1.1.1.tgz(r-4.5-any)
RankMap_1.1.1.tar.gz(r-4.7-any)RankMap_1.1.1.tar.gz(r-4.6-any)
RankMap_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RankMap/json (API)

# Install 'RankMap' in R:

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

Bug tracker:https://github.com/jinming-cheng/rankmap/issues

On BioConductor:RankMap-1.1.1(bioc 3.24)RankMap-1.0.0(bioc 3.23)

spatial singlecell transcriptomics geneexpression annotation regression preprocessing software

5.08 score 2 stars 4 scripts 156 downloads 12 exports 158 dependencies

Last updated from:3795f06b33. Checks:1 WARNING, 9 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	173
linux-devel-x86_64	OK	308
source / vignettes	OK	258
linux-release-x86_64	OK	279
macos-release-arm64	OK	145
macos-oldrel-arm64	OK	118
windows-devel	OK	165
windows-release	OK	180
windows-oldrel	OK	194
wasm-release	OK	188

Exports:computeRankedMatrix evaluatePredictionPerformance extractData factorSorted filterLowConfidenceCells maskTopKGenes optimizeConfidenceThreshold predictRankModel RankMap sampleCellsByType topCellTypeGenes trainRankModel

Dependencies:abind askpass base64enc BH Biobase BiocGenerics bitops bslib cachem caTools cli cluster codetools commonmark cowplot cpp11 crosstalk curl data.table DelayedArray deldir digest dotCall64 dplyr dqrng evaluate farver fastDummies fastmap fitdistrplus FNN fontawesome foreach fs future future.apply generics GenomicRanges ggplot2 ggrepel ggridges glmnet globals glue goftest gplots gridExtra gtable gtools here highr htmltools htmlwidgets httpuv httr ica igraph IRanges irlba isoband iterators jquerylib jsonlite KernSmooth knitr labeling later lattice lazyeval lifecycle listenv lmtest magrittr MASS Matrix MatrixGenerics matrixStats memoise mime miniUI nlme openssl otel parallelly patchwork pbapply pillar pkgconfig plotly plyr png polyclip progressr promises purrr R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML reshape2 reticulate rlang rmarkdown ROCR rprojroot RSpectra Rtsne S4Arrays S4Vectors S7 sass scales scattermore sctransform Seqinfo Seurat SeuratObject shape shiny sitmo sourcetools sp spam SparseArray spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils stringi stringr SummarizedExperiment survival sys tensor tibble tidyr tidyselect tinytex utf8 uwot vctrs viridisLite withr xfun xtable XVector yaml zoo

Getting Started with RankMap

Rendered fromRankMap.Rmdusingknitr::rmarkdown

Last update: 2026-04-03
Started: 2026-01-15

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compute Ranked Gene Expression Matrix	computeRankedMatrix
Evaluate Prediction Accuracy and Confusion by Cell Type	evaluatePredictionPerformance
Extract Expression Matrix from Seurat, SummarizedExperiment, or Matrix	extractData
Order factor levels by frequency	factorSorted
Tag Low-Confidence Predictions and Add Status Column	filterLowConfidenceCells
Mask Non-Top-K Genes in Each Cell or Spot	maskTopKGenes
Optimize Confidence Threshold for Cell Type Prediction	optimizeConfidenceThreshold
Predict Cell Types, Probabilities, or Confidence Scores	predictRankModel
RankMap: Train and Predict Cell Types Using Top-Ranked Genes	RankMap
Sample Cells by Cell Type with Minimum Representation	sampleCellsByType
Get top expressed genes for each cell type	topCellTypeGenes
Train Multinomial Rank-Based Model	trainRankModel