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.
For more detailed instuction, you can find the vignette at our website: https://sydneybiox.github.io/scdney/.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("scReClassify")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)
})
Current version of this package is implemented to run with svm and randomForest classifiers.
data(gse87795_subset_sce)
dat <- gse87795_subset_sce
cellTypes <- gse87795_subset_sce$cellTypes
# number of clusters
nCs <- length(table(cellTypes))
# This demo dataset is already pre-processed
dim(dat)reducedDim(dat, "matPCs") = matPCs(dat, assay = "logNorm", 0.7)Here in this example, we will synthetically generate varying degree of noise in sample labels.
lab <- cellTypes
set.seed(1)
noisyCls <- function(dat, rho, cls.truth){
cls.noisy <- cls.truth
names(cls.noisy) <- colnames(dat)
for(i in seq_len(length(table(cls.noisy)))) {
# class label starts from 0
if (i != length(table(cls.noisy))) {
cls.noisy[sample(which(cls.truth == names(table(cls.noisy))[i]), floor(sum(cls.truth == names(table(cls.noisy))[i]) * rho))] <- names(table(cls.noisy))[i+1]
} else {
cls.noisy[sample(which(cls.truth == names(table(cls.noisy))[i]), floor(sum(cls.truth == names(table(cls.noisy))[i]) * rho))] <- names(table(cls.noisy))[1]
}
}
print(sum(cls.truth != cls.noisy))
return(cls.noisy)
}
cls.noisy01 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.1, lab)
cls.noisy02 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.2, lab)
cls.noisy03 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.3, lab)
cls.noisy04 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.4, lab)
cls.noisy05 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.5, lab)Here in this example, we will only use Support Vector machine (svm) as base classifier.
###################################
# SVM
###################################
base <- "svm"
set.seed(1)
result = lapply(seq_len(10), function(j) {
final <- multiAdaSampling(dat, cls.noisy01, reducedDimName = "matPCs",
classifier=base, percent=1, L=10)$final
ari01 <- mclust::adjustedRandIndex(lab, final)
acc01 <- bAccuracy(lab, final)
final <- multiAdaSampling(dat, cls.noisy02, reducedDimName = "matPCs",
classifier=base, percent=1, L=10)$final
ari02 <- mclust::adjustedRandIndex(lab, final)
acc02 <- bAccuracy(lab, final)
final <- multiAdaSampling(dat, cls.noisy03, reducedDimName = "matPCs",
classifier=base, percent=1, L=10)$final
ari03 <- mclust::adjustedRandIndex(lab, final)
acc03 <- bAccuracy(lab, final)
final <- multiAdaSampling(dat, cls.noisy04, reducedDimName = "matPCs",
classifier=base, percent=1, L=10)$final
ari04 <- mclust::adjustedRandIndex(lab, final)
acc04 <- bAccuracy(lab, final)
final <- multiAdaSampling(dat, cls.noisy05, reducedDimName = "matPCs",
classifier=base, percent=1, L=10)$final
ari05 <- 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)# 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 genes
End <- c("ITGA2B", "ITGB3")
# check examples
idx <- 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)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