# 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 4.1.3 (2022-03-10)
    #> Platform: x86_64-apple-darwin13.4.0 (64-bit)
    #> Running under: macOS Big Sur/Monterey 10.16
    #>
    #> Matrix products: default
    #> BLAS/LAPACK: /Users/martin/miniconda3/envs/R_4.1.3/lib/libopenblasp-r0.3.18.dylib
    #>
    #> locale:
    #> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] Canek_0.2.5
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] Rcpp_1.0.10          highr_0.10           DEoptimR_1.0-11
    #>  [4] bslib_0.4.2          compiler_4.1.3       bluster_1.4.0
    #>  [7] jquerylib_0.1.4      class_7.3-21         prabclus_2.3-2
    #> [10] BiocNeighbors_1.12.0 numbers_0.8-5        tools_4.1.3
    #> [13] digest_0.6.31        mclust_6.0.0         jsonlite_1.8.4
    #> [16] evaluate_0.20        lattice_0.20-45      pkgconfig_2.0.3
    #> [19] rlang_1.0.6          Matrix_1.5-3         igraph_1.3.5
    #> [22] cli_3.6.0            rstudioapi_0.14      yaml_2.3.7
    #> [25] parallel_4.1.3       xfun_0.37            fastmap_1.1.0
    #> [28] knitr_1.42           cluster_2.1.4        sass_0.4.5
    #> [31] S4Vectors_0.32.4     fpc_2.2-10           diptest_0.76-0
    #> [34] nnet_7.3-18          stats4_4.1.3         grid_4.1.3
    #> [37] robustbase_0.95-0    R6_2.5.1             flexmix_2.3-18
    #> [40] BiocParallel_1.28.3  rmarkdown_2.20       irlba_2.3.5.1
    #> [43] kernlab_0.9-32       magrittr_2.0.3       matrixStats_0.63.0
    #> [46] modeltools_0.2-23    htmltools_0.5.4      BiocGenerics_0.40.0
    #> [49] MASS_7.3-58.3        cachem_1.0.6         FNN_1.1.3.1