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 37PCA 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.
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.
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 24.04.4 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=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] Canek_0.3.1
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.10 generics_0.1.4 robustbase_0.99-7
#> [4] class_7.3-23 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_6.1.3
#> [16] kernlab_0.9-33 codetools_0.2-20 modeltools_0.2-24
#> [19] textshaping_1.0.5 jquerylib_0.1.4 cli_3.6.6
#> [22] rlang_1.3.0 BiocNeighbors_2.6.0 cachem_1.1.0
#> [25] yaml_2.3.12 otel_0.2.0 fpc_2.2-14
#> [28] FNN_1.1.4.1 tools_4.6.1 flexmix_2.3-20
#> [31] parallel_4.6.1 BiocParallel_1.46.0 BiocGenerics_0.58.1
#> [34] R6_2.6.1 matrixStats_1.5.0 stats4_4.6.1
#> [37] lifecycle_1.0.5 S4Vectors_0.50.1 fs_2.1.0
#> [40] htmlwidgets_1.6.4 bluster_1.22.0 MASS_7.3-65
#> [43] irlba_2.3.7 ragg_1.5.2 cluster_2.1.8.2
#> [46] pkgconfig_2.0.3 desc_1.4.3 pkgdown_2.2.1
#> [49] bslib_0.11.0 Rcpp_1.1.2 systemfonts_1.3.2
#> [52] DEoptimR_1.2-0 xfun_0.60 prabclus_2.3-5
#> [55] knitr_1.51 htmltools_0.5.9 igraph_2.3.3
#> [58] rmarkdown_2.31 compiler_4.6.1 diptest_0.77-2