Package: scmap 1.34.0

Vladimir Kiselev

scmap: A tool for unsupervised projection of single cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment.

Authors:Vladimir Kiselev

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manual.pdf |manual.html
card.svg |card.png
scmap/json (API)
NEWS

# Install 'scmap' in R:
install.packages('scmap', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hemberg-lab/scmap/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • ann - Cell type annotations for data extracted from a publication by Yan et al.
  • yan - Single cell RNA-Seq data extracted from a publication by Yan et al.

On BioConductor:scmap-1.35.0(bioc 3.24)scmap-1.34.0(bioc 3.23)

immunooncologysinglecellsoftwareclassificationsupportvectormachinernaseqvisualizationtranscriptomicsdatarepresentationtranscriptionsequencingpreprocessinggeneexpressiondataimportbioconductor-packagehuman-cell-atlasprojection-mappingsingle-cell-rna-seqopenblascpp

9.07 score 100 stars 291 scripts 770 downloads 34 mentions 8 exports 55 dependencies

Last updated from:6ff20f6d37 (on RELEASE_3_23). Checks:1 WARNING, 11 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksWARNING186
linux-devel-arm64NOTE257
linux-devel-x86_64NOTE335
source / vignettesOK270
linux-release-arm64NOTE258
linux-release-x86_64NOTE296
macos-release-arm64NOTE203
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macos-oldrel-arm64NOTE171
macos-oldrel-x86_64NOTE439
windows-develNOTE260
windows-releaseNOTE235
windows-oldrelNOTE292
wasm-releaseOK127

Exports:getSankeyindexCellindexClusterscmapCellscmapCell2ClusterscmapClusterselectFeaturessetFeatures

Dependencies:abindBiobaseBiocGenericsclassclicpp11DelayedArraydplyre1071farvergenericsGenomicRangesggplot2gluegoogleVisgtableIRangesisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatspillarpkgconfigplyrproxyR6randomForestRColorBrewerRcppRcppArmadilloreshape2rlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentSparseArraystringistringrSummarizedExperimenttibbletidyselectutf8vctrsviridisLitewithrXVector

scmap package vignette

Rendered fromscmap.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2023-05-15
Started: 2017-06-28

Readme and manuals

Help Manual

Help pageTopics
Cell type annotations for data extracted from a publication by Yan et al.ann
The Euclidean Squared Norm of each column of a matrix is computed and the whole result is returned as a vector. Used as part of the approx. calculations of the cosine similarity between the query and the reference.EuclSqNorm
Plot Sankey diagram comparing two clusteringsgetSankey
Create an index for a dataset to enable fast approximate nearest neighbour searchindexCell indexCell,SingleCellExperiment-method indexCell.SingleCellExperiment
Create a precomputed ReferenceindexCluster indexCluster,SingleCellExperiment-method indexCluster.SingleCellExperiment
Main nearest neighbour calculation function. Used on the first reference dataset. Returns a list of three objects: 1) the cell indices of the w nearest neighbours 2) the corresponding approx. cosine similaritiesNN
Normalises each column of a matrixnormalise
For each cell in a query dataset, we search for the nearest neighbours by cosine distance within a collection of reference datasets.scmapCell scmapCell,SingleCellExperiment-method scmapCell.SingleCellExperiment
Approximate k-NN cell-type classification using scfinemapscmapCell2Cluster scmapCell2Cluster,list-method scmapCell2Cluster.SingleCellExperiment
scmap main functionscmapCluster scmapCluster,SingleCellExperiment-method scmapCluster.SingleCellExperiment
Find the most informative features (genes/transcripts) for projectionselectFeatures selectFeatures,SingleCellExperiment-method selectFeatures.SingleCellExperiment
Set the most important features (genes/transcripts) for projectionsetFeatures setFeatures,SingleCellExperiment-method setFeatures.SingleCellExperiment
Computes the dot product between the subcentroids from the indexed reference and the subvectors of an element of the query dataset. Returns an M by k matrix. Used as an intermediate step (in NNfirst and NNmult) for calculating an approximation of the cosine similarity between the query and the reference.subdistsmult
Single cell RNA-Seq data extracted from a publication by Yan et al.yan