Hello! Welcome to the Lionmap package :)
+Hello! Welcome to the Lionmap package :)
This package is meant to provide efficient and robust cell type classification while being easy to use.
-Optional features that might increase mapping accuracy include using a custom hierarchy that describes the relationship between cell types, and setting different confidence thresholds for a classification to proceed to the next level of the hierarchy.
+Optional features that might increase accuracy include using a custom hierarchy that describes the relationship between cell types, and setting different confidence thresholds for a classification to proceed to the next level of the hierarchy.
An example using a sample dataset where we run the entire pipeline (finding marker genes, creating models, and classifying cells) in a few lines of code is shown below.
-```{r, eval=FALSE} #load reference and query datasets data(“hierarchy”,“train_ex_data_bpcells”,“test_ex_data_bpcells”,“train_ex_metadata”) #find marker genes marker_genes = FindMarkerGenes(train_ex_data_bpcells, train_ex_metadata, tree = hierarchy, metadata_cluster_column = “seurat_annotations”, metadata_cell_label_column = “cell_label”) #create models models = GetModels(marker_genes, train_ex_data_bpcells, train_ex_metadata, tree = hierarchy, metadata_cluster_column = “seurat_annotations”, metadata_cell_label_column = “cell_label”) #classify cells using models classifications = Classify(test_ex_data_bpcells, models, hierarchy)
-```
-The package only works using BPCells matrices as gene expression input. BPCells is a package that enables fast gene expression transformations and IO operations while requiring little memory on your computing environment. For more info on BPCells, check out the package here.
- +Example +
+
+library(lionmap)
+#load reference and query datasets
+data("hierarchy","train_ex_data_bpcells","test_ex_data_bpcells","train_ex_metadata")
+#find marker genes
+marker_genes = FindMarkerGenes(train_ex_data_bpcells, train_ex_metadata, tree = hierarchy, metadata_cluster_column = "seurat_annotations", metadata_cell_label_column = "cell_label")
+#create models
+models = GetModels(marker_genes, train_ex_data_bpcells, train_ex_metadata, tree = hierarchy, metadata_cluster_column = "seurat_annotations", metadata_cell_label_column = "cell_label")
+#classify cells using models
+classifications = Classify(test_ex_data_bpcells, models, hierarchy)Installation +
+You can install the development version of lionmap from GitHub with:
+
+# install.packages("devtools")
+devtools::install_github("jonathan-columbiau/lionmap")