# BLASE for annotating scRNA-seq

    library(scater)
    library(ggplot2)
    library(BiocParallel)
    library(blase)
    library(patchwork)

    RNGversion("3.5.0")
    #> Warning in RNGkind("Mersenne-Twister", "Inversion", "Rounding"): non-uniform
    #> 'Rounding' sampler used
    SEED <- 7
    set.seed(SEED)

    N_CORES <- 2
    bpparam <- MulticoreParam(N_CORES)

This article will show how BLASE can be used for annotating scRNA-seq data using existing bulk or microarray data. We make use of scRNA-seq Dogga et al. 2024 and microarray data from Painter et al. 2018. Code for generating the objects used here is available in the BLASE reproducibility repository.

## Load Data

First we’ll load in the data we’re using, pre-prepared from the BLASE reproducibility repository.

    data(painter_microarray, package = "blase")
    data(MCA_PF_SCE, package = "blase")

We can examine the true lifecycle stages, and also the calculated pseudotime trajectory (Slingshot).

    plotUMAP(MCA_PF_SCE, colour_by = "STAGE_HR")

    #| fig.alt: >
    #|   UMAP of Dogga et al. single-cell RNA-seq reference coloured by pseudotime,
    #|   starting with Rings, and ending with Schizonts.
    plotUMAP(MCA_PF_SCE, colour_by = "slingPseudotime_1")

## Prepare BLASE

Now we’ll prepare BLASE for use.

### Create BLASE data object

First, we create the object, giving it the number of bins we want to use, and how to calculate them.

    blase_data <- as.BlaseData(
        MCA_PF_SCE,
        pseudotime_slot = "slingPseudotime_1",
        n_bins = 48,
        split_by = "cells"
    )

    # Add these bins to the sc metadata
    MCA_PF_SCE <- assign_pseudotime_bins(MCA_PF_SCE,
        pseudotime_slot = "slingPseudotime_1",
        n_bins = 48,
        split_by = "cells"
    )

### Select Genes

Now we will select the genes we want to use, using BLASE’s gene peakedness metric.

    gene_peakedness_info <- calculate_gene_peakedness(
        MCA_PF_SCE,
        window_pct = 5,
        knots = 18,
        BPPARAM = bpparam
    )

    genes_to_use <- gene_peakedness_spread_selection(
        MCA_PF_SCE,
        gene_peakedness_info,
        genes_per_bin = 30,
        n_gene_bins = 30
    )

    head(gene_peakedness_info)
    #>             gene peak_pseudotime mean_in_window mean_out_window     ratio
    #> 28 PF3D7-1401100       3.8311312       5.793085        1.528934  3.788969
    #> 44 PF3D7-1401200       6.0203490       4.791942        1.921439  2.493934
    #> 29 PF3D7-1401400       3.9679573     696.174509       39.689901 17.540344
    #> 84 PF3D7-1401600      11.4933936      93.076303        7.631160 12.196875
    #> 1  PF3D7-1401900       0.1368261      43.270750        3.321464 13.027613
    #> 80 PF3D7-1402200      10.9460892       3.754015        1.554102  2.415553
    #>    window_start window_end deviance_explained
    #> 28    3.4890659  4.1731965          0.1881312
    #> 44    5.6782838  6.3624143          0.2035355
    #> 29    3.6258920  4.3100226          0.2513795
    #> 84   11.1513283 11.8354589          0.6891177
    #> 1    -0.2052392  0.4788914          0.3846938
    #> 80   10.6040239 11.2881544          0.1768597

By using the `gene_peakedness_spread_selection` function, we can ensure that genes with high ratios are selected from throughout the trajectory.

    ggplot(gene_peakedness_info, aes(x = peak_pseudotime, y = ratio)) +
        geom_point() +
        ggtitle("All genes")

    gene_peakedness_selected_genes <- gene_peakedness_info[
        gene_peakedness_info$gene %in% genes_to_use,
    ]

    ggplot(gene_peakedness_selected_genes, aes(x = peak_pseudotime, y = ratio)) +
        geom_point() +
        ggtitle("Selected genes")

Here, we add them to the BLASE object for mapping with.

    genes(blase_data) <- genes_to_use

## Calculate Mappings

Now we can perform the actual mapping step, and review the results.

    mapping_results <- map_all_best_bins(
        blase_data,
        painter_microarray,
        BPPARAM = bpparam
    )

    plot_mapping_result_heatmap(mapping_results)
    #> Inferred correlation metric.

## Transfer Mappings

In the Painter et al. paper, the expected lifecycle stages are as follows:

Lifecycle Stage | Painter et al. HPI
--- | ---
Ring | 0-21
Trophozoite | 16-32
Schizont | 33-48

We can use this to transfer back to the scRNA-seq as follows:

    plotUMAP(MCA_PF_SCE, colour_by = "pseudotime_bin")

    annotated_sce <- annotate_sce(MCA_PF_SCE, mapping_results, include_stats = TRUE)
    annotated_sce$BLASE_Annotation <- gsub(
      " hpi", "", annotated_sce$BLASE_Annotation)
    annotated_sce$BLASE_Annotation <- as.numeric(annotated_sce$BLASE_Annotation)

    annotation_plot <- plotUMAP(annotated_sce, colour_by = "BLASE_Annotation")
    corr_plot <- plotUMAP(
      annotated_sce, colour_by = "BLASE_Annotation_Correlation") +
        labs(color = "Correlation")

    annotation_plot + corr_plot

    annotated_sce$BLASE_Annotation_transfer <- ""
    for (best_bulk in unique(annotated_sce$BLASE_Annotation)) {
        mask <- colData(annotated_sce)[, "BLASE_Annotation"] == best_bulk

        if (best_bulk %in% 0:15) {
            annotated_sce[, mask]$BLASE_Annotation_transfer <- "Ring"
        } else if (best_bulk %in% 16:21) {
            annotated_sce[, mask]$BLASE_Annotation_transfer <- "Ring/Trophozoite"
        } else if (best_bulk %in% 22:32) {
            annotated_sce[, mask]$BLASE_Annotation_transfer <- "Trophozoite"
        } else if (best_bulk %in% 33:48) {
            annotated_sce[, mask]$BLASE_Annotation_transfer <- "Schizont"
        }
    }

    plotUMAP(annotated_sce, colour_by = "BLASE_Annotation_transfer") +
        plotUMAP(annotated_sce, colour_by = "STAGE_LR")

## Session Info

    sessionInfo()
    #> R version 4.5.2 (2025-10-31)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.3 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
    #>
    #> Random number generation:
    #>  RNG:     Mersenne-Twister
    #>  Normal:  Inversion
    #>  Sample:  Rounding
    #>
    #> 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] patchwork_1.3.2             blase_1.1.2
    #>  [3] BiocParallel_1.45.0         scater_1.39.2
    #>  [5] ggplot2_4.0.2               scuttle_1.21.0
    #>  [7] SingleCellExperiment_1.33.0 SummarizedExperiment_1.41.1
    #>  [9] Biobase_2.71.0              GenomicRanges_1.63.1
    #> [11] Seqinfo_1.1.0               IRanges_2.45.0
    #> [13] S4Vectors_0.49.0            BiocGenerics_0.57.0
    #> [15] generics_0.1.4              MatrixGenerics_1.23.0
    #> [17] matrixStats_1.5.0           BiocStyle_2.39.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3       sys_3.4.3                jsonlite_2.0.0
    #>   [4] magrittr_2.0.4           spatstat.utils_3.2-1     ggbeeswarm_0.7.3
    #>   [7] farver_2.1.2             rmarkdown_2.30           vctrs_0.7.1
    #>  [10] ROCR_1.0-12              spatstat.explore_3.7-0   htmltools_0.5.9
    #>  [13] S4Arrays_1.11.1          BiocNeighbors_2.5.3      SparseArray_1.11.10
    #>  [16] sass_0.4.10              sctransform_0.4.3        parallelly_1.46.1
    #>  [19] KernSmooth_2.23-26       bslib_0.10.0             htmlwidgets_1.6.4
    #>  [22] ica_1.0-3                plyr_1.8.9               plotly_4.12.0
    #>  [25] zoo_1.8-15               cachem_1.1.0             buildtools_1.0.0
    #>  [28] igraph_2.2.2             mime_0.13                lifecycle_1.0.5
    #>  [31] pkgconfig_2.0.3          rsvd_1.0.5               Matrix_1.7-4
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