GitHub - SydneyBioX/scReClassify: Post hoc cell type classification of single-cell RNA-sequencing data. (original) (raw)

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_sce cellTypes <- gse87795_subset_sce$cellTypes

number of clusters

nCs <- 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(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)

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)$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)

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 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)

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