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 26.04.1 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-r0.3.32.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_0.3.1    rmarkdown_2.32
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.10          generics_0.1.4       class_7.3-24        
#>  [4] robustbase_0.99-7    lattice_0.23-1       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_1.7-6         nnet_7.3-21          mclust_6.1.3        
#> [16] kernlab_0.9-33       codetools_0.2-20     modeltools_0.2-25   
#> [19] jquerylib_0.1.4      cli_3.6.6            rlang_1.3.0         
#> [22] BiocNeighbors_2.7.3  cachem_1.1.0         yaml_2.3.12         
#> [25] otel_0.2.0           fpc_2.2-15           FNN_1.1.4.1         
#> [28] tools_4.6.1          flexmix_2.3-21       parallel_4.6.1      
#> [31] BiocParallel_1.47.0  BiocGenerics_0.59.12 buildtools_1.0.0    
#> [34] R6_2.6.1             matrixStats_1.5.0    stats4_4.6.1        
#> [37] lifecycle_1.0.5      S4Vectors_0.51.10    bluster_1.23.1      
#> [40] MASS_7.3-66          irlba_2.4.1          cluster_2.1.8.3     
#> [43] pkgconfig_2.0.3      bslib_0.12.0         Rcpp_1.1.2          
#> [46] DEoptimR_1.2-2       xfun_0.61            prabclus_2.3-5      
#> [49] sys_3.4.3            knitr_1.52           htmltools_0.5.9     
#> [52] igraph_2.3.4         maketools_1.3.2      compiler_4.6.1      
#> [55] diptest_0.77-2