The scAnnotatR.models packages contains a set of pre-trained models to classify various (immune) cell types in human data to be used by the scAnnotatR package.
scAnnotatR is an R package for cell type prediction on single cell RNA-sequencing data. Currently, this package supports data in the forms of a Seurat object or a SingleCellExperiment object.
If you are interested in directly applying these models to your data, please refer to the vignettes of the scAnnotatR package.
The scAnnotatR.models package is a AnnotationHub package. Normally, it is automatically loaded by the scAnnotatR package.
To load the package manually into your R session, please use the Bioconductor AnnotationHub package:
# use the AnnotationHub to load the scAnnotatR.models package
eh <- AnnotationHub::AnnotationHub()
#> snapshotDate(): 2021-09-10
# load the stored models
query <- AnnotationHub::query(eh, "scAnnotatR.models")
models <- query[["AH95906"]]
#> loading from cacheThe models object is a named list containing the cell type's name as key and the respective classifier as value:
# print the available cell types
names(models)
#> [1] "B cells" "Plasma cells" "NK"
#> [4] "CD16 NK" "CD56 NK" "T cells"
#> [7] "CD4 T cells" "CD8 T cells" "Treg"
#> [10] "NKT" "ILC" "Monocytes"
#> [13] "CD14 Mono" "CD16 Mono" "DC"
#> [16] "pDC" "Endothelial cells" "LEC"
#> [19] "VEC" "Platelets" "RBC"
#> [22] "Melanocyte" "Schwann cells" "Pericytes"
#> [25] "Mast cells" "Keratinocytes" "alpha"
#> [28] "beta" "delta" "gamma"
#> [31] "acinar" "ductal" "Fibroblasts"Each classifier is an instance of the scAnnotatR S4 class. For example:
models[['B cells']]
#> Loading required package: scAnnotatR
#> Loading required package: Seurat
#> Attaching SeuratObject
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
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#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
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#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
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#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
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#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
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#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#>
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#> Loading required package: S4Vectors
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#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
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#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
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#> Attaching package: 'SummarizedExperiment'
#> The following object is masked from 'package:SeuratObject':
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#> Assays
#> The following object is masked from 'package:Seurat':
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#> Assays
#> An object of class scAnnotatR for B cells
#> * 31 marker genes applied: CD38, CD79B, CD74, CD84, RASGRP2, TCF3, SP140, MEF2C, DERL3, CD37, CD79A, POU2AF1, MVK, CD83, BACH2, LY86, CD86, SDC1, CR2, LRMP, VPREB3, IL2RA, BLK, IRF8, FLI1, MS4A1, CD14, MZB1, PTEN, CD19, MME
#> * Predicting probability threshold: 0.5
#> * No parent modelThe scAnnotatR package comes with several pre-trained models to classify cell types.
# Load the scAnnotatR package to view the models
library(scAnnotatR)The models are stored in the default_models object:
default_models <- load_models("default")
#> snapshotDate(): 2021-09-10
#> loading from cache
names(default_models)
#> [1] "B cells" "Plasma cells" "NK"
#> [4] "CD16 NK" "CD56 NK" "T cells"
#> [7] "CD4 T cells" "CD8 T cells" "Treg"
#> [10] "NKT" "ILC" "Monocytes"
#> [13] "CD14 Mono" "CD16 Mono" "DC"
#> [16] "pDC" "Endothelial cells" "LEC"
#> [19] "VEC" "Platelets" "RBC"
#> [22] "Melanocyte" "Schwann cells" "Pericytes"
#> [25] "Mast cells" "Keratinocytes" "alpha"
#> [28] "beta" "delta" "gamma"
#> [31] "acinar" "ductal" "Fibroblasts"The default_models object is named a list of classifiers. Each classifier is an instance of the scAnnotatR S4 class. For example:
default_models[['B cells']]
#> An object of class scAnnotatR for B cells
#> * 31 marker genes applied: CD38, CD79B, CD74, CD84, RASGRP2, TCF3, SP140, MEF2C, DERL3, CD37, CD79A, POU2AF1, MVK, CD83, BACH2, LY86, CD86, SDC1, CR2, LRMP, VPREB3, IL2RA, BLK, IRF8, FLI1, MS4A1, CD14, MZB1, PTEN, CD19, MME
#> * Predicting probability threshold: 0.5
#> * No parent modelPlease refer to the scAnnotatR package documentation for detailed information about how to use these classifiers.
sessionInfo()
#> R version 4.1.1 Patched (2021-09-10 r80880)
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