Run Canek on a toy example

library(Canek)

# Functions
## Function to plot the pca coordinates
plotPCA <- function(pcaData = NULL, label = NULL, legPosition = "topleft"){
  col <- as.integer(label) 
  plot(x = pcaData[,"PC1"], y = pcaData[,"PC2"],
       col = as.integer(label), cex = 0.75, pch = 19,
       xlab = "PC1", ylab = "PC2")
  legend(legPosition,  pch = 19,
         legend = levels(label), 
         col =  unique(as.integer(label)))
}

Load the data

On this toy example we use the two simulated batches included in the SimBatches data from Canek’s package. SimBatches is a list containing:

batches: Simulated scRNA-seq datasets with genes (rows) and cells (columns). Simulations were performed using Splatter.
cell_type: a factor containing the celltype labels of the batches

lsData <- list(B1 = SimBatches$batches[[1]], B2 = SimBatches$batches[[2]])
batch <- factor(c(rep("Batch-1", ncol(lsData[[1]])),
                  rep("Batch-2", ncol(lsData[[2]]))))
celltype <- SimBatches$cell_types
table(batch)
#> batch
#> Batch-1 Batch-2 
#>     631     948
table(celltype)
#> celltype
#> Cell Type 1 Cell Type 2 Cell Type 3 Cell Type 4 
#>        1451          53          38          37

PCA before correction

We perform the Principal Component Analysis (PCA) of the joined datasets and scatter plot the first two PCs. The batch-effect causes cells to group by batch.

data <- Reduce(cbind, lsData)
pcaData <- prcomp(t(data), center = TRUE, scale. = TRUE)$x

plotPCA(pcaData = pcaData, label = batch, legPosition = "bottomleft")

plotPCA(pcaData = pcaData, label = celltype, legPosition = "bottomleft")

Run Canek

We correct the toy batches using the function RunCanek. This function accepts:

List of matrices
Seurat object
List of Seurat objects
SingleCellExperiment object
List of SingleCellExperiment objects

On this example we use the list of matrices created before.

data <- RunCanek(lsData)

PCA after correction

We perform PCA of the corrected datasets and plot the first two PCs. After correction, the cells group by their corresponding cell type.

pcaData <- prcomp(t(data), center = TRUE, scale. = TRUE)$x

plotPCA(pcaData = pcaData, label = batch, legPosition = "topleft")

plotPCA(pcaData = pcaData, label = celltype, legPosition = "topleft")

Session info

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 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-r0.3.32.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=en_US.UTF-8    
#>  [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] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] Canek_0.2.5    rmarkdown_2.31
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.10         generics_0.1.4      class_7.3-23       
#>  [4] robustbase_0.99-7   lattice_0.22-9      numbers_0.9-2      
#>  [7] digest_0.6.39       magrittr_2.0.5      evaluate_1.0.5     
#> [10] grid_4.6.1          fastmap_1.2.0       jsonlite_2.0.0     
#> [13] Matrix_1.7-5        nnet_7.3-20         mclust_6.1.2       
#> [16] kernlab_0.9-33      codetools_0.2-20    modeltools_0.2-24  
#> [19] jquerylib_0.1.4     cli_3.6.6           rlang_1.2.0        
#> [22] BiocNeighbors_2.7.2 cachem_1.1.0        yaml_2.3.12        
#> [25] otel_0.2.0          fpc_2.2-14          FNN_1.1.4.1        
#> [28] tools_4.6.1         flexmix_2.3-20      parallel_4.6.1     
#> [31] BiocParallel_1.47.0 BiocGenerics_0.59.8 buildtools_1.0.0   
#> [34] R6_2.6.1            matrixStats_1.5.0   stats4_4.6.1       
#> [37] lifecycle_1.0.5     S4Vectors_0.51.5    bluster_1.23.0     
#> [40] MASS_7.3-65         irlba_2.3.7         cluster_2.1.8.2    
#> [43] pkgconfig_2.0.3     bslib_0.11.0        Rcpp_1.1.1-1.1     
#> [46] DEoptimR_1.2-0      xfun_0.59           prabclus_2.3-5     
#> [49] sys_3.4.3           knitr_1.51          htmltools_0.5.9    
#> [52] igraph_2.3.3        maketools_1.3.2     compiler_4.6.1     
#> [55] diptest_0.77-2