Last updated: 2020-07-20
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Knit directory: cytomapper_publication/
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This script offers an example on how to run the cytomapperShiny function to gate and visualize cells on images. For more information, please refer to ?cytomapperShiny and the Help section within the Shiny app.
Here, we will read in the previously generated data objects and directly pass them into the cytomapperShiny function.
library(cytomapper)
sce <- readRDS("data/PancreasData/pancreas_sce.rds")
masks <- readRDS("data/PancreasData/pancreas_masks.rds")
images <- readRDS("data/PancreasData/pancreas_images.rds")When not supplying a segmentation mask or multi-channel image object, the cytomapperShiny function can be used to perform classic hierarchical gating similar to FlowJo on cell-specific expression values:
if (interactive()) {
cytomapperShiny(object = sce, img_id = "ImageName", cell_id = "CellNumber")
}Next, we will only supply the SingleCellExperiment object and the CytoImageList segmentation mask object to the Shiny function. In this setting, segmentation masks are coloured (i) based on selected marker expression and (ii) based on the gated cells.
if (interactive()) {
cytomapperShiny(object = sce, mask = masks,
cell_id = "CellNumber", img_id = "ImageName")
}In the third setting, we will gate cells based on their mean expression counts but outline the selected cells on composite images. For this, we will need to supply the SingleCellExperiment object, a CytoImageList object containing segmentation masks and a CytoImageList object containing multi-channel images.
if (interactive()) {
cytomapperShiny(object = sce, mask = masks, image = images,
cell_id = "CellNumber", img_id = "ImageName")
}
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] cytomapper_1.1.2 SingleCellExperiment_1.11.6
[3] SummarizedExperiment_1.19.6 DelayedArray_0.15.7
[5] matrixStats_0.56.0 Matrix_1.2-18
[7] Biobase_2.49.0 GenomicRanges_1.41.5
[9] GenomeInfoDb_1.25.8 IRanges_2.23.10
[11] S4Vectors_0.27.12 BiocGenerics_0.35.4
[13] EBImage_4.31.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] viridis_0.5.1 viridisLite_0.3.0 svgPanZoom_0.3.4
[4] shiny_1.5.0 sp_1.4-2 GenomeInfoDbData_1.2.3
[7] vipor_0.4.5 tiff_0.1-5 yaml_2.2.1
[10] gdtools_0.2.2 pillar_1.4.6 backports_1.1.8
[13] lattice_0.20-41 glue_1.4.1 digest_0.6.25
[16] RColorBrewer_1.1-2 promises_1.1.1 XVector_0.29.3
[19] colorspace_1.4-1 htmltools_0.5.0 httpuv_1.5.4
[22] pkgconfig_2.0.3 raster_3.3-7 zlibbioc_1.35.0
[25] purrr_0.3.4 xtable_1.8-4 fftwtools_0.9-8
[28] scales_1.1.1 svglite_1.2.3.2 jpeg_0.1-8.1
[31] later_1.1.0.1 git2r_0.27.1 tibble_3.0.3
[34] generics_0.0.2 ggplot2_3.3.2 ellipsis_0.3.1
[37] magrittr_1.5 crayon_1.3.4 mime_0.9
[40] evaluate_0.14 fs_1.4.2 beeswarm_0.2.3
[43] shinydashboard_0.7.1 tools_4.0.2 lifecycle_0.2.0
[46] stringr_1.4.0 munsell_0.5.0 locfit_1.5-9.4
[49] compiler_4.0.2 systemfonts_0.2.3 rlang_0.4.7
[52] grid_4.0.2 RCurl_1.98-1.2 rstudioapi_0.11
[55] htmlwidgets_1.5.1 bitops_1.0-6 rmarkdown_2.3
[58] gtable_0.3.0 codetools_0.2-16 abind_1.4-5
[61] R6_2.4.1 gridExtra_2.3 knitr_1.29
[64] dplyr_1.0.0 fastmap_1.0.1 rprojroot_1.3-2
[67] stringi_1.4.6 ggbeeswarm_0.6.0 Rcpp_1.0.5
[70] vctrs_0.3.2 png_0.1-7 tidyselect_1.1.0
[73] xfun_0.15