Introduction to scAnnotatR (original) (raw)

The scAnnotatR package comes with several pre-trained models to classify cell types.

# load scAnnotatR into working space
library(scAnnotatR)
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default_models <- load_models("default")
#> 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 model