Package: scGate 1.6.2

Massimo Andreatta

scGate: Marker-Based Cell Type Purification for Single-Cell Sequencing Data

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.

Authors:Massimo Andreatta [aut, cre], Ariel Berenstein [aut], Josep Garnica [aut], Santiago Carmona [aut]

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Bug tracker:https://github.com/carmonalab/scgate/issues

On CRAN:

filteringmarker-genesscgatesignaturessingle-cell

11 exports 88 stars 4.18 score 171 dependencies 138 scripts 964 downloads

Last updated 3 months agofrom:7d3189607d. Checks:OK: 7. Indexed: yes.

Exports:combine_scGate_multiclassgating_modelget_scGateDBget_testing_dataload_scGate_modelperformance.metricsplot_levelsplot_treeplot_UCell_scoresscGatetest_my_model

Dependencies:abindaskpassassortheadbase64encBHBiobaseBiocGenericsBiocNeighborsBiocParallelbitopsbslibcachemcaToolscliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngevaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomeformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglambda.rlaterlatticelazyevalleidenlifecyclelistenvlmtestmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS4ArraysS4VectorssassscalesscattermoresctransformSeuratSeuratObjectshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArrayspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexUCellUCSC.utilsutf8uwotvctrsviridisLitewithrxfunxtableXVectoryamlzlibbioczoo