rfca

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.

Introduction

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.

Installation

You can install this package from GitHub with devtools::install_github("kimberle9/rfca")

Manual

View the manual here: https://kimberle9.github.io/rfca/articles/vignette.html

Examples

Example 1: Creating a Seurat Object

# 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 dataset

Example 2: Using Random Forest to train and predict cell types

library(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)

Example 3: Defining microglia subtypes

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)