This package contains functions that trains a Random Forest Model with a labelled Seurat Object, for predicting cell types/states in unlabelled datasets. It also contains a pre-trained Random Forest model, as well as example datasets.
Manual cell annotation of scRNAseq datasets, typically based on marker genes, can be time-consuming and biased. Being able to automatically predict cell types/states in a cell-by-cell and cluster-unbiased way is useful for fast and accurate phenotyping.
In addition, despite the increasing amounts of scRNAseq datasets being generated, thorough analysis of these datasets is lagging, and/or done in silos. This package comes with a preloaded Random Forest model based on different datasets and cell types/states, that will be constantly updated.
You can install this package from GitHub with
devtools::install_github("kimberle9/rfca")
View the manual here: https://kimberle9.github.io/rfca/articles/vignette.html
# Load the rfca and Seurat libraries, as well as example datasets
library(rfca)
library(Seurat)
# Create Seurat Object with PCA and UMAP calculated
# Cell ranger raw data ("/filtered_feature_bc_matrix" directory) is required for this step
mySeuratObject <- createSeuratObjectPipeline(data.dir = "~/filtered_feature_bc_matrix",
nFeature_RNA_lower = 500,
nFeature_RNA_upper = 5000,
percent.mt = 5,
nfeatures = 2000,
dims = 20,
clusterResolution = 0.8)
# Assign cell type Idents to mySeuratObject manually, if you want to use it as a training datasetlibrary(rfca)
library(Seurat)
data("exampleSeuratObjectUnlabelled")
data("exampleSeuratObjectLabelled")
# Create Random Forest Model with your labelled Seurat Object
myRandomForestModel <- createRFModel(exampleSeuratObjectLabelled)##
## Call:
## randomForest(formula = V1 ~ ., data = merged, importance = TRUE, proximity = TRUE, na.action = na.roughfix)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 13
##
## OOB estimate of error rate: 3.96%
## Confusion matrix:
## Astrocytes Endothelial Microglia Neurons Oligodendrocytes OPCs
## Astrocytes 46 0 0 0 1 0
## Endothelial 0 53 1 0 0 0
## Microglia 0 0 47 0 0 0
## Neurons 1 0 0 47 1 2
## Oligodendrocytes 0 0 0 1 46 0
## OPCs 0 0 1 1 1 47
## Tcells 0 0 2 0 0 0
## VSMCs 1 0 1 0 0 0
## Tcells VSMCs class.error
## Astrocytes 0 0 0.02127660
## Endothelial 0 0 0.01851852
## Microglia 0 0 0.00000000
## Neurons 1 0 0.09615385
## Oligodendrocytes 0 0 0.02127660
## OPCs 1 0 0.07843137
## Tcells 50 0 0.03846154
## VSMCs 0 52 0.03703704
# Create marker gene list from random forest model
markerGeneList <- createGeneLists(myRandomForestModel)## [1] "Adding gene list for Astrocytes"
## [1] "Adding gene list for Endothelial"
## [1] "Adding gene list for Microglia"
## [1] "Adding gene list for Neurons"
## [1] "Adding gene list for Oligodendrocytes"
## [1] "Adding gene list for OPCs"
## [1] "Adding gene list for Tcells"
## [1] "Adding gene list for VSMCs"
# Visualize Feature Plot based on marker gene list
myPlot <- cellMarkerPlots(exampleSeuratObjectLabelled, geneList = markerGeneList)## [1] "Calculating average expression of genes in Astrocytes list."
## [1] "Calculating average expression of genes in Endothelial list."
## [1] "Calculating average expression of genes in Microglia list."
## [1] "Calculating average expression of genes in Neurons list."
## [1] "Calculating average expression of genes in Oligodendrocytes list."
## [1] "Calculating average expression of genes in OPCs list."
## [1] "Calculating average expression of genes in Tcells list."
## [1] "Calculating average expression of genes in VSMCs list."
myPlot
# Predict cells based on your own Random Forest Model created above
autoLabelledSeuratObject <- predictCells(exampleSeuratObjectUnlabelled, myRandomForestModel)##
## Astrocytes Endothelial Microglia Neurons
## 56 45 59 43
## Oligodendrocytes OPCs Tcells VSMCs
## 54 49 47 43
# Predict cells based on my pre-loaded and pre-trained Random Forest Model
# With no model passed in, default model used is mouseBrain.
# See documentation for available tissue types.
autoLabelledSeuratObject <- predictCells(exampleSeuratObjectUnlabelled)##
## Astrocytes Endothelial Microglia Neurons
## 53 46 53 48
## Oligodendrocytes OPCs Tcells VSMCs
## 53 49 48 46
# Visualize autoLabelledSeuratObject
DimPlot(autoLabelledSeuratObject)
library(rfca)
library(Seurat)
# Create Seurat object (microglia-only data)
microglia <- createSeuratObjectPipeline(data.dir = "~/filtered_feature_bc_matrix")
# Define microglia subtypes based on cluster numbers
DimPlot(microglia)
# Build Random Forest Model with cluster number as labels
microgliaModel <- createRFModel(microglia)
# Phenotype subtype proportion for different mouse models
mouseModel1 <- predictCells(mouseModel1, microgliaModel)
mouseModel2 <- predictCells(mouseModel2, microgliaModel)
mouseModel3 <- predictCells(mouseModel3, microgliaModel)