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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#> 'scAnnotatR'
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