scReClassify

scReClassify is a post hoc cell type classification of single-cell RNA-sequencing data. Using semi-supervised learning algorithm, adaSampling, to correct cell type annotation from noise.

Getting started

Vignette

For more detailed instuction, you can find the vignette at our website: https://sydneybiox.github.io/scdney/.

Installation

Bioconductor

if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("scReClassify")

GitHub

devtools::install_github("SydneyBioX/scReClassify", build_opts= c("--no-resave-data", "--no-manual"))
library(scReClassify)

For devtool version < 2.0.0,

devtools::install_github("SydneyBioX/scReClassify", build_vignettes=TRUE)
library(scReClassify)

suppressPackageStartupMessages({
library(scReClassify)
library(SingleCellExperiment)
library(SummarizedExperiment)
})

Usage

Current version of this package is implemented to run with svm and randomForest classifiers.

Load data

data(gse87795_subset_sce)
dat<-gse87795_subset_scecellTypes<-gse87795_subset_sce$cellTypes# number of clustersnCs<- length(table(cellTypes))
# This demo dataset is already pre-processed
dim(dat)

Part A. scReClassify (Demonstration)

Dimension reduction

reducedDim(dat, "matPCs") = matPCs(dat, assay="logNorm", 0.7)

Synthetic noise (Demonstration purpose)

Here in this example, we will synthetically generate varying degree of noise in sample labels.

lab<-cellTypes
set.seed(1)
noisyCls<-function(dat, rho, cls.truth){
cls.noisy<-cls.truth
names(cls.noisy) <- colnames(dat)
for(iin seq_len(length(table(cls.noisy)))) {
# class label starts from 0if (i!= length(table(cls.noisy))) {
cls.noisy[sample(which(cls.truth== names(table(cls.noisy))[i]), floor(sum(cls.truth== names(table(cls.noisy))[i]) *rho))] <- names(table(cls.noisy))[i+1]
} else {
cls.noisy[sample(which(cls.truth== names(table(cls.noisy))[i]), floor(sum(cls.truth== names(table(cls.noisy))[i]) *rho))] <- names(table(cls.noisy))[1]
}
}
print(sum(cls.truth!=cls.noisy))
return(cls.noisy)
}
cls.noisy01<- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.1, lab)
cls.noisy02<- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.2, lab)
cls.noisy03<- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.3, lab)
cls.noisy04<- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.4, lab)
cls.noisy05<- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.5, lab)

Use scReClassify to correct mislabeled cell types.

Here in this example, we will only use Support Vector machine (svm) as base classifier.

Benchmark evaluation

#################################### SVM###################################base<-"svm"
set.seed(1)
result= lapply(seq_len(10), function(j) {
final<- multiAdaSampling(dat, cls.noisy01, reducedDimName="matPCs", classifier=base, percent=1, L=10)$finalari01<-mclust::adjustedRandIndex(lab, final)
acc01<- bAccuracy(lab, final)
final<- multiAdaSampling(dat, cls.noisy02, reducedDimName="matPCs", classifier=base, percent=1, L=10)$finalari02<-mclust::adjustedRandIndex(lab, final)
acc02<- bAccuracy(lab, final)
final<- multiAdaSampling(dat, cls.noisy03, reducedDimName="matPCs", classifier=base, percent=1, L=10)$finalari03<-mclust::adjustedRandIndex(lab, final)
acc03<- bAccuracy(lab, final)
final<- multiAdaSampling(dat, cls.noisy04, reducedDimName="matPCs", classifier=base, percent=1, L=10)$finalari04<-mclust::adjustedRandIndex(lab, final)
acc04<- bAccuracy(lab, final)
final<- multiAdaSampling(dat, cls.noisy05, reducedDimName="matPCs", classifier=base, percent=1, L=10)$finalari05<-mclust::adjustedRandIndex(lab, final)
acc05<- bAccuracy(lab, final)
c(
acc01=acc01,
acc02=acc02,
acc03=acc03,
acc04=acc04,
acc05=acc05,
ari01=ari01,
ari02=ari02,
ari03=ari03,
ari04=ari04,
ari05=ari05
)
})
result= do.call(rbind, result)
acc=result[,seq_len(5)]
colnames(acc) = seq(from=0.1,to=0.5,by=0.1)
ari=result[,seq(from=6, to=10)]
colnames(ari) = seq(from=0.1,to=0.5,by=0.1)
plot.new()
par(mfrow= c(1,2))
boxplot(acc, col="lightblue", main="SVM Accuracy", ylim=c(0.45, 1), xlab="rho", ylab="Accuracy")
points(x=seq_len(5), y=c(
bAccuracy(lab, cls.noisy01), bAccuracy(lab, cls.noisy02),
bAccuracy(lab, cls.noisy03), bAccuracy(lab, cls.noisy04),
bAccuracy(lab, cls.noisy05)), col="red3", pch=c(2,3,4,5,6), cex=1)
boxplot(ari, col="lightblue", main="SVM ARI", ylim=c(0.25, 1), xlab="rho", ylab="ARI")
points(x=seq_len(5), y=c(
mclust::adjustedRandIndex(lab, cls.noisy01), mclust::adjustedRandIndex(lab, cls.noisy02),
mclust::adjustedRandIndex(lab, cls.noisy03), mclust::adjustedRandIndex(lab, cls.noisy04),
mclust::adjustedRandIndex(lab, cls.noisy05)), col="red3", pch=c(2,3,4,5,6), cex=1)

Part B. scReClassify (mislabeled cell type correction)

# PCA procedure
reducedDim(dat, "matPCs") = matPCs(dat, assay="logNorm", 0.7)
# run scReClassify
set.seed(1)
cellTypes.reclassify<- multiAdaSampling(dat, cellTypes, reducedDimName="matPCs",
classifier="svm", percent=1, L=10)
# Verification by marker genesEnd<- c("ITGA2B", "ITGB3")
# check examplesidx<- which(cellTypes.reclassify$final!=cellTypes)
cbind(original=cellTypes[idx], reclassify=cellTypes.reclassify$final[idx]) %>%
DT::datatable()
mat<- assay(dat, "logNorm")
c1<-mat[, which(cellTypes=="Endothelial Cell")]
c2<-mat[, which(cellTypes=="Erythrocyte")]
c3<-mat[, which(cellTypes=="Hepatoblast")]
c4<-mat[, which(cellTypes=="Macrophage")]
c5<-mat[, which(cellTypes=="Megakaryocyte")]
c6<-mat[, which(cellTypes=="Mesenchymal Cell")]
cs<- rainbow(length(table(cellTypes)))
# (example 1 E13.5_C14)#####
par(mfrow=c(1,2))
marker<-End[1]
boxplot(c1[marker,], c2[marker,], c3[marker,], c4[marker,], c5[marker,], c6[marker,], col=cs, main=marker, names=c("Others", "Others", "Others", "Orignal", "Reclassified", "Others"), las=2, xlab="Labels",
ylab="log2FPKM")
points(5, mat[marker, which(colnames(mat) %in%"E13.5_C14")], pch=16, col="red", cex=2)
marker<-End[2]
boxplot(c1[marker,], c2[marker,], c3[marker,], c4[marker,], c5[marker,], c6[marker,], col=cs, main=marker, names=c("Others", "Others", "Others", "Orignal", "Reclassified", "Others"), las=2, xlab="Labels", ylab="log2FPKM")
points(5, mat[marker, which(colnames(mat) %in%"E13.5_C14")], pch=16, col="red", cex=2)

Reference

scReClassify is published in BMC Genomics. Please refer to the following article for more details on method implementation and evaluation.

Taiyun Kim, Kitty Lo, Thomas A. Geddes, Hani Jieun Kim, Jean Yee Hwa Yang & Pengyi Yang (2019) scReClassify: post hoc cell type classification of single-cell RNA-seq data. BMC Genomics, 20:913. https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6305-x