Package: RankMap 1.0.0
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:
RankMap_1.0.0.tar.gz
RankMap_1.0.0.zip(r-4.7)RankMap_1.0.0.zip(r-4.6)RankMap_1.0.0.zip(r-4.5)
RankMap_1.0.0.tgz(r-4.6-any)RankMap_1.0.0.tgz(r-4.5-any)
RankMap_1.0.0.tar.gz(r-4.7-any)RankMap_1.0.0.tar.gz(r-4.6-any)
RankMap_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RankMap/json (API)
NEWS
| # Install 'RankMap' in R: |
| install.packages('RankMap', repos = c('https://bioc-release.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)
spatialsinglecelltranscriptomicsgeneexpressionannotationregressionpreprocessingsoftware
Last updated from:4a491310e5 (on RELEASE_3_23). Checks:1 NOTE, 9 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | NOTE | 175 | ||
| linux-devel-x86_64 | OK | 259 | ||
| source / vignettes | OK | 305 | ||
| linux-release-x86_64 | OK | 311 | ||
| macos-release-arm64 | OK | 219 | ||
| macos-oldrel-arm64 | OK | 108 | ||
| windows-devel | OK | 250 | ||
| windows-release | OK | 171 | ||
| windows-oldrel | OK | 190 | ||
| wasm-release | OK | 154 |
Exports:computeRankedMatrixevaluatePredictionPerformanceextractDatafactorSortedfilterLowConfidenceCellsmaskTopKGenesoptimizeConfidenceThresholdpredictRankModelRankMapsampleCellsByTypetrainRankModel
Dependencies:abindaskpassbase64encBHBiobaseBiocGenericsbitopsbslibcachemcaToolscliclustercodetoolscommonmarkcowplotcpp11crosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngevaluatefarverfastDummiesfastmapfitdistrplusFNNfontawesomeforeachfsfuturefuture.applygenericsGenomicRangesggplot2ggrepelggridgesglmnetglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallifecyclelistenvlmtestmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemimeminiUInlmeopensslotelparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS4ArraysS4VectorsS7sassscalesscattermoresctransformSeqinfoSeuratSeuratObjectshapeshinysitmosourcetoolsspspamSparseArrayspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableXVectoryamlzoo
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 |
| Train Multinomial Rank-Based Model | trainRankModel |
