Introduction

scReClassify is a post hoc cell type classification of single-cell RNA-sequencing data to fine-tune cell type annotations generated by any cell type classification procedure. Typically, cell type identification relies on human inspection using combination of prior biological knowledge and computational techniques. Due to incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Using semi-supervised learning algorithm, adaSampling, we are able to correct cell type annotations from various degree of noise.

Overview of scReClassify methods

Installation

Install the latest development version from GitHub using the devtools package:

if (!("devtools" %in% rownames(installed.packages())))
    install.packages("devtools")

library(devtools)
devtools::install_github("SydneyBioX/scReClassify")

To install the Bioconductor version of scReClassify, enter the following to your R console.

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

Loading packages and data

suppressPackageStartupMessages({
    library(scReClassify)
    library(DT)
    library(mclust)
    library(dplyr)
    library(SummarizedExperiment)
    library(SingleCellExperiment)
})

## Warning: multiple methods tables found for 'union'

## Warning: multiple methods tables found for 'intersect'

## Warning: multiple methods tables found for 'setdiff'

data("gse87795_subset_sce")

dat <- gse87795_subset_sce
cellTypes <- gse87795_subset_sce$cellTypes

gse87795_subset_sce is a SingleCellExperiment object of a mouse fetal liver development data deposited at Gene Expression Omnibus respository with accession ID GSE87795. The cell type information can be found on the colData of the SingleCellExperiment object.

# Cell types
table(cellTypes)

## cellTypes
## Endothelial Cell      Erythrocyte      Hepatoblast       Macrophage 
##               49              130               42               51 
##    Megakaryocyte Mesenchymal Cell 
##               17               78

# We set the number of clusters
nCs <- length(table(cellTypes))
nCs

## [1] 6

# This demo dataset is already pre-processed
dim(dat)

## [1] 1000  367

There are 6 cell types, 367 cells and 1000 number of genes.

Part A. scReClassify (Demonstration with synthetic mislabels)

Dimension reduction

Prior to running scReClassify, we perform dimension reduction. matPCs is a tool in scReClassify to simplify this process. In this function, a dimension reduced matrix is returned with n principal components (PCs), where n is the number of principal components (PCs) that by sum explains at least 70% variance.

The function accepts either a matrix or a SingleCellExperiment object. If the data parameter is a SingleCellExperiment object, an assay variable must be specified to perform dimension reduction on the correct assay. If the SingleCellExperiment object data already has a ‘PCA’ in reducedDimNames(), the ‘PCA’ matrix of n columns are returned.

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 (10-50%) in sample labels. The purpose here is to simulate different level of mislabeling in the data. Given a cell type label cls.truth, noisyCls function will randomly select a rho percentage of cells from a given cell type and relabel to other cell types.

Here, we create different degree of noise from 10% to 50%.

lab <- cellTypes

set.seed(1)
# Function to create noise in the cell type label
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)

## [1] 34

cls.noisy02 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.2, lab)

## [1] 71

cls.noisy03 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.3, lab)

## [1] 108

cls.noisy04 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.4, lab)

## [1] 144

cls.noisy05 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.5, lab)

## [1] 182

With noisyCls function, we have relabeled Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Hepatoblast, Erythrocyte, Mesenchymal Cell, Hepatoblast, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Macrophage, Erythrocyte, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Megakaryocyte, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Erythrocyte, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Endothelial Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Hepatoblast, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Erythrocyte, Erythrocyte, Hepatoblast, Megakaryocyte, Erythrocyte, Erythrocyte, Erythrocyte, Megakaryocyte, Endothelial Cell, Hepatoblast, Erythrocyte, Mesenchymal Cell, Endothelial Cell, Erythrocyte, Macrophage, Mesenchymal Cell, Endothelial Cell, Mesenchymal Cell, Mesenchymal Cell, Macrophage, Megakaryocyte, Erythrocyte, Endothelial Cell, Erythrocyte, Macrophage, Hepatoblast, Mesenchymal Cell, Erythrocyte, Macrophage, Endothelial Cell, Hepatoblast, Mesenchymal Cell, Mesenchymal Cell, Erythrocyte, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Endothelial Cell, Hepatoblast, Mesenchymal Cell, Erythrocyte, Erythrocyte, Hepatoblast, Erythrocyte, Hepatoblast, Erythrocyte, 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Endothelial Cell, Erythrocyte, Macrophage, Macrophage, Hepatoblast, Erythrocyte, Macrophage, Megakaryocyte, Erythrocyte, Hepatoblast, Macrophage, Macrophage, Endothelial Cell, Megakaryocyte, Endothelial Cell, Hepatoblast, Erythrocyte, Endothelial Cell, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Macrophage, Erythrocyte, Erythrocyte, Endothelial Cell, Hepatoblast, Endothelial Cell, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Endothelial Cell, Macrophage, Erythrocyte, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Hepatoblast, Macrophage, Erythrocyte, Hepatoblast, Macrophage, Hepatoblast, Mesenchymal Cell, Erythrocyte, Erythrocyte, Megakaryocyte, Hepatoblast, Macrophage, Hepatoblast, Endothelial Cell, Mesenchymal Cell, Hepatoblast, Erythrocyte, Mesenchymal Cell, Macrophage, Macrophage, Hepatoblast, Erythrocyte, Erythrocyte, Mesenchymal Cell, Macrophage, Macrophage, Erythrocyte, Erythrocyte, Macrophage, Hepatoblast, Hepatoblast, Macrophage, Macrophage, Megakaryocyte, Macrophage, Erythrocyte, Endothelial Cell, Macrophage, Hepatoblast, Erythrocyte, Macrophage, Megakaryocyte, Macrophage, Endothelial Cell, Erythrocyte, Macrophage, Endothelial Cell, Macrophage, Hepatoblast, Endothelial Cell, Endothelial Cell, Hepatoblast, Erythrocyte, Erythrocyte, Macrophage, Erythrocyte, Erythrocyte, Hepatoblast, Megakaryocyte, Macrophage, Megakaryocyte, Megakaryocyte, Erythrocyte, Macrophage, Erythrocyte, Erythrocyte, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Endothelial Cell, Erythrocyte, Erythrocyte, Macrophage, Macrophage, Erythrocyte and Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Hepatoblast, Erythrocyte, Mesenchymal Cell, Hepatoblast, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Macrophage, Erythrocyte, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Megakaryocyte, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Erythrocyte, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Endothelial Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Mesenchymal Cell, Endothelial Cell, Endothelial Cell, Hepatoblast, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Macrophage, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Mesenchymal Cell, Erythrocyte, Erythrocyte, Hepatoblast, Megakaryocyte, Erythrocyte, Erythrocyte, Erythrocyte, Megakaryocyte, Endothelial Cell, Hepatoblast, Erythrocyte, Mesenchymal Cell, Endothelial Cell, Erythrocyte, Macrophage, Mesenchymal Cell, Endothelial Cell, Mesenchymal Cell, Mesenchymal Cell, Macrophage, Megakaryocyte, Erythrocyte, Endothelial Cell, Erythrocyte, Macrophage, Hepatoblast, Mesenchymal Cell, Erythrocyte, Macrophage, Endothelial Cell, Hepatoblast, Mesenchymal Cell, Mesenchymal Cell, Erythrocyte, Mesenchymal Cell, Mesenchymal Cell, Endothelial Cell, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Endothelial Cell, Hepatoblast, Mesenchymal Cell, Erythrocyte, Erythrocyte, Hepatoblast, Erythrocyte, Hepatoblast, Erythrocyte, Endothelial Cell, Erythrocyte, Mesenchymal Cell, Erythrocyte, Megakaryocyte, Endothelial Cell, Mesenchymal Cell, Mesenchymal Cell, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Megakaryocyte, Megakaryocyte, Erythrocyte, Endothelial Cell, Erythrocyte, Erythrocyte, Mesenchymal Cell, Erythrocyte, Endothelial Cell, Erythrocyte, Mesenchymal Cell, Erythrocyte, Macrophage, Mesenchymal Cell, Erythrocyte, Mesenchymal Cell, Endothelial Cell, Hepatoblast, Erythrocyte, Hepatoblast, Endothelial Cell, Endothelial Cell, Erythrocyte, Endothelial Cell, Macrophage, Erythrocyte, Endothelial Cell, Macrophage, Erythrocyte, Hepatoblast, Hepatoblast, Erythrocyte, Megakaryocyte, Megakaryocyte, Macrophage, Erythrocyte, Macrophage, Erythrocyte, Megakaryocyte, Hepatoblast, Macrophage, Erythrocyte, Erythrocyte, Macrophage, Erythrocyte, Endothelial Cell, Macrophage, Mesenchymal Cell, Endothelial Cell, Erythrocyte, Hepatoblast, Erythrocyte, Endothelial Cell, Erythrocyte, Megakaryocyte, Endothelial Cell, Megakaryocyte, Erythrocyte, Mesenchymal Cell, Hepatoblast, Endothelial Cell, Endothelial Cell, Macrophage, Erythrocyte, Hepatoblast, Mesenchymal Cell, Erythrocyte, Hepatoblast, Erythrocyte, Endothelial Cell, Macrophage, Hepatoblast, Erythrocyte, Hepatoblast, Endothelial Cell, Erythrocyte, Macrophage, Erythrocyte, Hepatoblast, Erythrocyte, Erythrocyte, Hepatoblast, Erythrocyte, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Macrophage, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Hepatoblast, Erythrocyte, Erythrocyte, Mesenchymal Cell, Hepatoblast, Hepatoblast, Endothelial Cell, Endothelial Cell, Megakaryocyte, Hepatoblast, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Hepatoblast, Macrophage, Erythrocyte, Erythrocyte, Endothelial Cell, Erythrocyte, Macrophage, Macrophage, Hepatoblast, Erythrocyte, Macrophage, Megakaryocyte, Erythrocyte, Hepatoblast, Macrophage, Macrophage, Endothelial Cell, Megakaryocyte, Endothelial Cell, Hepatoblast, Erythrocyte, Endothelial Cell, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Macrophage, Erythrocyte, Erythrocyte, Endothelial Cell, Hepatoblast, Endothelial Cell, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Endothelial Cell, Macrophage, Erythrocyte, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Erythrocyte, Hepatoblast, Macrophage, Erythrocyte, Hepatoblast, Macrophage, Hepatoblast, Mesenchymal Cell, Erythrocyte, Erythrocyte, Megakaryocyte, Hepatoblast, Macrophage, Hepatoblast, Endothelial Cell, Mesenchymal Cell, Hepatoblast, Erythrocyte, Mesenchymal Cell, Macrophage, Macrophage, Hepatoblast, Erythrocyte, Erythrocyte, Mesenchymal Cell, Macrophage, Macrophage, Erythrocyte, Erythrocyte, Macrophage, Hepatoblast, Hepatoblast, Macrophage, Macrophage, Megakaryocyte, Macrophage, Erythrocyte, Endothelial Cell, Macrophage, Hepatoblast, Erythrocyte, Macrophage, Megakaryocyte, Macrophage, Endothelial Cell, Erythrocyte, Macrophage, Endothelial Cell, Macrophage, Hepatoblast, Endothelial Cell, Endothelial Cell, Hepatoblast, Erythrocyte, Erythrocyte, Macrophage, Erythrocyte, Erythrocyte, Hepatoblast, Megakaryocyte, Macrophage, Megakaryocyte, Megakaryocyte, Erythrocyte, Macrophage, Erythrocyte, Erythrocyte, Hepatoblast, Erythrocyte, Erythrocyte, Erythrocyte, Endothelial Cell, Erythrocyte, Erythrocyte, Macrophage, Macrophage, Erythrocyte number of cells for rho equal to 0.1, 0.2, 0.3, 0.4 and 0.5 respectively.

Use scReClassify to correct mislabeled cell types.

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

Benchmark evaluation

To benchmark scReClassify, we perform scReclassify to all degree of noise with 10 repeats. We measure the accuracy of scReClassify and the Adjusted Rand Index (ARI) to measure the concordance of the reclassified cell type to the true cell type label.

###################################
# 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)

We can visualise the performance of the scReClassify. The boxes represent the accuracy and the ARI after scReClassify. The red markers indicate the baseline (prior to scReClassify).

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)

The plot shows that with scReClassify, cell type information have been refined (boxes are higher than the red markers). The scReClassified results show higher accuracy across noise levels 0.1 - 0.4 (i.e. closer to the true label). With the noise level 0.5, it is showing similar accuracy which is as expected because the initial label contains equal amount of true and false information and thus making it difficult for the algorithm to learn the true label. This shows that scReClassify is also robust to noisy cell type labels.

Part B. scReClassify (mislabeled cell type correction)

scReClassify has shown promising result with the synthetic noise we have created. Here we will use scReClassify on the actual cell type label from public repository. The data we will use is a mouse fetal liver dataset from GEO with an accession ID GSE87795.

# 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")

Below is a table of cell type labels classified to a different cell types after scReClassify.

# check examples
idx <- which(cellTypes.reclassify$final != cellTypes)

cbind(original=cellTypes[idx], reclassify=cellTypes.reclassify$final[idx]) %>%
    DT::datatable()

Here, we visualise the expression level of the a cells that is reclassified for demontration purpose. The box plots are the marker gene expression levels grouped by cells types. The expression level of the reclassified cell (Cell ID: E13.5_C14) are highlighted as red marker.

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)

As shown in the boxplots above, the expression level of the reclassified cell (red dot) is similar to the expression levels of the reclassified cell types in a marker gene. This highlights that the E13.5_C14 cell has a similar expression profiles to the reclassified cell types rather than its originally labeled cell type. Thus, we were able to identify that E13.5_C14 potentially belongs to the reclassified cell type with scReClassify.

SessionInfo

sessionInfo()

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
##  [3] Biobase_2.67.0              GenomicRanges_1.59.0       
##  [5] GenomeInfoDb_1.43.1         IRanges_2.41.1             
##  [7] S4Vectors_0.45.2            BiocGenerics_0.53.3        
##  [9] generics_0.1.3              MatrixGenerics_1.19.0      
## [11] matrixStats_1.4.1           dplyr_1.1.4                
## [13] mclust_6.1.1                DT_0.33                    
## [15] scReClassify_1.13.0         BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##  [1] utf8_1.2.4              sass_0.4.9              class_7.3-22           
##  [4] SparseArray_1.7.2       lattice_0.22-6          magrittr_2.0.3         
##  [7] digest_0.6.37           evaluate_1.0.1          grid_4.4.2             
## [10] fastmap_1.2.0           jsonlite_1.8.9          Matrix_1.7-1           
## [13] e1071_1.7-16            BiocManager_1.30.25     httr_1.4.7             
## [16] fansi_1.0.6             crosstalk_1.2.1         UCSC.utils_1.3.0       
## [19] jquerylib_0.1.4         abind_1.4-8             cli_3.6.3              
## [22] rlang_1.1.4             crayon_1.5.3            XVector_0.47.0         
## [25] cachem_1.1.0            DelayedArray_0.33.2     yaml_2.3.10            
## [28] S4Arrays_1.7.1          tools_4.4.2             GenomeInfoDbData_1.2.13
## [31] vctrs_0.6.5             buildtools_1.0.0        R6_2.5.1               
## [34] proxy_0.4-27            lifecycle_1.0.4         zlibbioc_1.52.0        
## [37] randomForest_4.7-1.2    htmlwidgets_1.6.4       pkgconfig_2.0.3        
## [40] pillar_1.9.0            bslib_0.8.0             glue_1.8.0             
## [43] tidyselect_1.2.1        tibble_3.2.1            xfun_0.49              
## [46] sys_3.4.3               knitr_1.49              htmltools_0.5.8.1      
## [49] rmarkdown_2.29          maketools_1.3.1         compiler_4.4.2

An introduction to scReClassify package