# Using mascarade package

This is a vignette describing usage of `mascarade` to generate masks for clusters on 2D dimensional reduction plots like UMAP or t-SNE.

### Package installation

The package stable version can be installed from CRAN:

    install.packages("mascarade")

The most recent development version of the package can be installed from GitHub:

    remotes::install_github("alserglab/mascarade")

### Loading necessary libraries

    library(mascarade)
    library(data.table)
    library(ggplot2)
    library(ggforce)

### Example run

Loading example data from PBMC 3K processed with Seurat (see below for more details).

    data("exampleMascarade")

UMAP coordinates:

    head(exampleMascarade$dims)

    ##                   UMAP_1    UMAP_2
    ## AAACATACAACCAC -4.232792 -4.152139
    ## AAACATTGAGCTAC -4.892886 10.985685
    ## AAACATTGATCAGC -5.508639 -7.211088
    ## AAACCGTGCTTCCG 11.332233  3.161727
    ## AAACCGTGTATGCG -7.450703  1.092022
    ## AAACGCACTGGTAC -3.509504 -6.087042

Cluster annotations:

    head(exampleMascarade$clusters)

    ## AAACATACAACCAC AAACATTGAGCTAC AAACATTGATCAGC AAACCGTGCTTCCG AAACCGTGTATGCG
    ##   Memory CD4 T              B   Memory CD4 T     CD14+ Mono             NK
    ## AAACGCACTGGTAC
    ##   Memory CD4 T
    ## 9 Levels: Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK ... Platelet

Expression table for several genes:

    head(exampleMascarade$features)

    ##                     MS4A1       GNLY       CD3E      CD14     FCER1A     FCGR3A
    ## AAACATACAACCAC -0.4110536 -0.4081782  1.0157094 -0.393789 -0.1373491 -0.4507969
    ## AAACATTGAGCTAC  2.5965712 -0.4081782 -0.9189074 -0.393789 -0.1373491 -0.4507969
    ## AAACATTGATCAGC -0.4110536  0.7526607  0.8148764 -0.393789 -0.1373491 -0.4507969
    ## AAACCGTGCTTCCG -0.4110536 -0.4081782 -0.9189074 -0.393789 -0.1373491  1.1300704
    ## AAACCGTGTATGCG -0.4110536  2.3958265 -0.9189074 -0.393789 -0.1373491 -0.4507969
    ## AAACGCACTGGTAC -0.4110536 -0.4081782  1.1029222 -0.393789 -0.1373491 -0.4507969
    ##                        LYZ       PPBP       CD8A
    ## AAACATACAACCAC -0.11104505 -0.1416271  2.1039769
    ## AAACATTGAGCTAC  0.06112027 -0.1416271 -0.3537211
    ## AAACATTGATCAGC  0.07833934 -0.1416271 -0.3537211
    ## AAACCGTGCTTCCG  1.40875149  2.9255239 -0.3537211
    ## AAACCGTGTATGCG -0.97272094 -0.1416271 -0.3537211
    ## AAACGCACTGGTAC -0.06309661 -0.1416271 -0.3537211

Let’s plot these data:

    data <- data.table(exampleMascarade$dims,
                       cluster=exampleMascarade$clusters,
                       exampleMascarade$features)
    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(aes(color=cluster)) +
        coord_fixed() +
        theme_classic()

Now let’s generate cluster masks:

    maskTable <- generateMask(dims=exampleMascarade$dims,
                              clusters=exampleMascarade$clusters)

The `maskTable` is actually a table of cluster borders. A single cluster can have multiple connected parts, and one a single part can contain multiple border lines (groups).

    head(maskTable)

    ##       UMAP_1    UMAP_2           part            group      cluster
    ##        <num>     <num>         <char>           <char>       <fctr>
    ## 1: -2.581865 -7.829109 Memory CD4 T#1 Memory CD4 T#1#1 Memory CD4 T
    ## 2: -2.570402 -7.806183 Memory CD4 T#1 Memory CD4 T#1#1 Memory CD4 T
    ## 3: -2.581865 -7.783258 Memory CD4 T#1 Memory CD4 T#1#1 Memory CD4 T
    ## 4: -2.593327 -7.771795 Memory CD4 T#1 Memory CD4 T#1#1 Memory CD4 T
    ## 5: -2.616253 -7.760332 Memory CD4 T#1 Memory CD4 T#1#1 Memory CD4 T
    ## 6: -2.639179 -7.748869 Memory CD4 T#1 Memory CD4 T#1#1 Memory CD4 T

Now we can use this table to draw the borders with `geom_path` (`group` column should be used as the group aesthetics):

    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(aes(color=cluster)) +
        geom_path(data=maskTable, aes(group=group)) +
        coord_fixed() +
        theme_classic()

Or we can color the borders instead of points:

    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(color="grey") +
        geom_path(data=maskTable, aes(group=group, color=cluster), linewidth=1) +
        coord_fixed() +
        theme_classic()

We can use `ggforce` package to make the borders touch instead of overlap:

    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(color="grey") +
        ggforce::geom_shape(data=maskTable, aes(group=group, color=cluster),
                            linewidth=1, fill=NA, expand=unit(-1, "pt")) +
        coord_fixed() +
        theme_classic()

In the presence of small clusters it can help to expand the borders a bit further away from the points.

    maskTable <- generateMask(dims=exampleMascarade$dims,
                              clusters=exampleMascarade$clusters,
                              expand=0.02)
    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(color="grey") +
        ggforce::geom_shape(data=maskTable, aes(group=group, color=cluster),
                            linewidth=1, fill=NA, expand=unit(-1, "pt")) +
        coord_fixed() +
        theme_classic()

With the help of `ggforce`-based function `geom_mark_shape` we can also put the labels within the plot itself.

    myMask <- list(
        geom_mark_shape(data=maskTable, aes(group=cluster, color=cluster, label = cluster),
                        fill = NA,
                       linewidth=1, expand=unit(-1, "pt"),
                       con.cap=0, con.type = "straight",
                       label.fontsize = 10, label.buffer = unit(0, "cm"),
                       label.fontface = "plain",
                       label.minwidth = 0,
                       label.margin = margin(2, 2, 2, 2, "pt"),
                       label.lineheight = 0,
                       con.colour = "inherit",
                       show.legend = FALSE),
        # expanding to give a bit more space for labels
        scale_x_continuous(expand = expansion(mult = 0.1)),
        scale_y_continuous(expand = expansion(mult = 0.1))
    )

    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(color="grey") +
        myMask +
        coord_fixed() +
        theme_classic()

The same can be achived with the `fancyMask()` helper function:

    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(color="grey") +
        fancyMask(maskTable, ratio=1, cols = scales::hue_pal()) +
        theme_classic()

Now we can easily show association between cell types and expression of particular genes, such as GNLY being a good marker for NK cells in this dataset.

    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(aes(color=GNLY), size=0.5) +
        scale_color_gradient2(low = "#404040", high="red") +
        fancyMask(maskTable, ratio=1, cols = scales::hue_pal()) +
        theme_classic()

We can focus on a single cluster too:

    ggplot(data, aes(x=UMAP_1, y=UMAP_2)) +
        geom_point(aes(color=GNLY), size=0.5) +
        scale_color_gradient2(low = "#404040", high="red") +
        geom_path(data=maskTable[cluster=="NK"], aes(group=group)) +
        coord_fixed() +
        theme_classic()

### Working with Seurat

For this part of the vignette you need `Seurat`package.

    library(Seurat)

Let’s get the example PBMC3K dataset:

    pbmc3k <- readRDS(url("https://alserglab.wustl.edu/files/mascarade/examples/pbmc3k_seurat5.rds"))
    pbmc3k <- NormalizeData(pbmc3k)
    pbmc3k

    ## An object of class Seurat
    ## 13714 features across 2638 samples within 1 assay
    ## Active assay: RNA (13714 features, 2000 variable features)
    ##  2 layers present: counts, data
    ##  2 dimensional reductions calculated: pca, umap

The same object can be obtained using `SeuratData` package (can be installed with `remotes::install_github('satijalab/seurat-data')`):

    if (requireNamespace("SeuratData")) {
        if (!AvailableData()["pbmc3k", "Installed"]) {
            InstallData("pbmc3k")
        }
        LoadData("pbmc3k")

        pbmc3k <- UpdateSeuratObject(pbmc3k.final)
        pbmc3k
    }

Generate masks using a helper function:

    maskTable <- generateMaskSeurat(pbmc3k)

We can use `fancyMask()` now, here it reuses colors from `DimPlot` automatically:

    DimPlot(pbmc3k) + NoLegend() +
        fancyMask(maskTable, ratio=1)

For the `DimPlot`, the borders can be viewed as redundant and removed:

    DimPlot(pbmc3k) + NoLegend() +
        fancyMask(maskTable, linewidth = 0, ratio=1)

Let’s plot an NK cell marker:

    FeaturePlot(pbmc3k, "GNLY", cols=c("grey90", "red")) +
        fancyMask(maskTable, ratio=1)

Or multiple markers (skipping the labels to save space, but adding colors):

    featureList <- c("MS4A1", "GNLY", "CD3E", "CD14")
    FeaturePlot(pbmc3k, features=featureList, cols=c("grey90", "red")) *
        fancyMask(maskTable, ratio=1, linewidth=0.5, label=FALSE, cols = scales::hue_pal())

Works with t-SNE too:

    pbmc3k <- RunTSNE(pbmc3k)

    maskTable <- generateMaskSeurat(pbmc3k, reduction = "tsne")

    FeaturePlot(pbmc3k, features=featureList, reduction = "tsne", cols=c("grey90", "red")) *
        fancyMask(maskTable, ratio=1, linewidth=0.5, label=FALSE, cols = scales::hue_pal())

### 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           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] Seurat_5.5.1       SeuratObject_5.4.0 sp_2.2-1           ggforce_0.5.0
    ## [5] ggsci_5.1.0        ggplot2_4.0.3      data.table_1.18.4  mascarade_0.3.5
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] deldir_2.0-4           pbapply_1.7-4          gridExtra_2.3.1
    ##   [4] rlang_1.3.0            magrittr_2.0.5         RcppAnnoy_0.0.23
    ##   [7] otel_0.2.0             matrixStats_1.5.0      ggridges_0.5.7
    ##  [10] compiler_4.6.1         spatstat.geom_3.8-1    reshape2_1.4.5
    ##  [13] png_0.1-9              systemfonts_1.3.2      vctrs_0.7.3
    ##  [16] stringr_1.6.0          pkgconfig_2.0.3        fastmap_1.2.0
    ##  [19] labeling_0.4.3         promises_1.5.0         rmarkdown_2.31
    ##  [22] purrr_1.2.2            xfun_0.59              cachem_1.1.0
    ##  [25] jsonlite_2.0.0         goftest_1.2-3          later_1.4.8
    ##  [28] spatstat.utils_3.2-3   tweenr_2.0.3           irlba_2.3.7
    ##  [31] parallel_4.6.1         cluster_2.1.8.2        R6_2.6.1
    ##  [34] ica_1.0-3              stringi_1.8.7          bslib_0.11.0
    ##  [37] RColorBrewer_1.1-3     spatstat.data_3.1-9    reticulate_1.46.0
    ##  [40] parallelly_1.48.0      spatstat.univar_3.2-0  scattermore_1.2
    ##  [43] lmtest_0.9-40          jquerylib_0.1.4        Rcpp_1.1.2
    ##  [46] knitr_1.51             tensor_1.5.1           future.apply_1.20.2
    ##  [49] zoo_1.8-15             sctransform_0.4.3      httpuv_1.6.17
    ##  [52] Matrix_1.7-5           splines_4.6.1          igraph_2.3.3
    ##  [55] tidyselect_1.2.1       abind_1.4-8            yaml_2.3.12
    ##  [58] codetools_0.2-20       spatstat.random_3.5-0  miniUI_0.1.2
    ##  [61] spatstat.explore_3.8-1 listenv_1.0.0          plyr_1.8.9
    ##  [64] lattice_0.22-9         tibble_3.3.1           shiny_1.14.0
    ##  [67] withr_3.0.3            S7_0.2.2               ROCR_1.0-12
    ##  [70] evaluate_1.0.5         Rtsne_0.17             future_1.70.0
    ##  [73] fastDummies_1.7.6      survival_3.8-6         polyclip_1.10-7
    ##  [76] fitdistrplus_1.2-6     pillar_1.11.1          KernSmooth_2.23-26
    ##  [79] plotly_4.12.0          generics_0.1.4         RcppHNSW_0.7.0
    ##  [82] scales_1.4.0           globals_0.19.1         xtable_1.8-8
    ##  [85] glue_1.8.1             lazyeval_0.2.3         tools_4.6.1
    ##  [88] RSpectra_0.16-2        RANN_2.6.2             dotCall64_1.2
    ##  [91] cowplot_1.2.0          grid_4.6.1             tidyr_1.3.2
    ##  [94] nlme_3.1-169           patchwork_1.3.2        cli_3.6.6
    ##  [97] spatstat.sparse_3.2-0  spam_2.11-4            viridisLite_0.4.3
    ## [100] dplyr_1.2.1            uwot_0.2.4             gtable_0.3.6
    ## [103] sass_0.4.10            digest_0.6.39          progressr_1.0.0
    ## [106] ggrepel_0.9.8          htmlwidgets_1.6.4      farver_2.1.2
    ## [109] htmltools_0.5.9        lifecycle_1.0.5        httr_1.4.8
    ## [112] mime_0.13              MASS_7.3-65