Last updated: 2020-11-18
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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. We have taken screenshots from this shiny app to generate Supplementary Figure 4.
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 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[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
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] cytomapper_1.2.0 SingleCellExperiment_1.12.0
[3] SummarizedExperiment_1.20.0 Biobase_2.50.0
[5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
[7] IRanges_2.24.0 S4Vectors_0.28.0
[9] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[11] matrixStats_0.57.0 EBImage_4.32.0
[13] 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_1.4-4 GenomeInfoDbData_1.2.4
[7] vipor_0.4.5 tiff_0.1-6 yaml_2.2.1
[10] gdtools_0.2.2 pillar_1.4.6 lattice_0.20-41
[13] glue_1.4.2 digest_0.6.27 RColorBrewer_1.1-2
[16] promises_1.1.1 XVector_0.30.0 colorspace_2.0-0
[19] htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18
[22] pkgconfig_2.0.3 raster_3.4-5 zlibbioc_1.36.0
[25] purrr_0.3.4 xtable_1.8-4 fftwtools_0.9-9
[28] scales_1.1.1 svglite_1.2.3.2 whisker_0.4
[31] jpeg_0.1-8.1 later_1.1.0.1 git2r_0.27.1
[34] tibble_3.0.4 generics_0.1.0 ggplot2_3.3.2
[37] ellipsis_0.3.1 magrittr_2.0.1 crayon_1.3.4
[40] mime_0.9 evaluate_0.14 fs_1.5.0
[43] beeswarm_0.2.3 shinydashboard_0.7.1 tools_4.0.3
[46] lifecycle_0.2.0 stringr_1.4.0 munsell_0.5.0
[49] locfit_1.5-9.4 DelayedArray_0.16.0 compiler_4.0.3
[52] systemfonts_0.3.2 rlang_0.4.8 grid_4.0.3
[55] RCurl_1.98-1.2 rstudioapi_0.13 htmlwidgets_1.5.2
[58] bitops_1.0-6 rmarkdown_2.5 gtable_0.3.0
[61] codetools_0.2-18 abind_1.4-5 R6_2.5.0
[64] gridExtra_2.3 knitr_1.30 dplyr_1.0.2
[67] fastmap_1.0.1 rprojroot_2.0.2 stringi_1.5.3
[70] ggbeeswarm_0.6.0 Rcpp_1.0.5 vctrs_0.3.5
[73] png_0.1-7 tidyselect_1.1.0 xfun_0.19