Figure 3
Nils Eling and Nicolas Damond
2020-07-10
Last updated: 2020-07-10
Checks: 6 1
Knit directory: cytomapper_publication/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | a100544 | nilseling | 2020-07-10 | Recompiled htmls |
| html | 2e3df83 | nilseling | 2020-07-09 | Added Github icon and recompiled |
| html | 69f1501 | nilseling | 2020-07-09 | Small fixes and recompilation |
| Rmd | 86ddc2d | nilseling | 2020-07-09 | Increased figure size for reporting |
| html | 86ddc2d | nilseling | 2020-07-09 | Increased figure size for reporting |
| html | 5476a61 | nilseling | 2020-07-08 | Recompiled files |
| Rmd | cb92024 | nilseling | 2020-07-03 | Finalized Figure 4 |
| html | cb92024 | nilseling | 2020-07-03 | Finalized Figure 4 |
| Rmd | 9b40183 | nilseling | 2020-07-03 | Finalized Figure 3 |
This script reproduces the analysis performed in Figure 3. Here, we will load the libraries and data for this figure:
library(cytomapper)
library(dplyr)
sce <- readRDS("data/PancreasData/pancreas_sce.rds")
masks <- readRDS("data/PancreasData/pancreas_masks.rds")We will now order the images based on the percentage of beta cells out of all islet cells. In that way, we can visualize the decline of beta cells across all sample images.
cur_summary <- as_tibble(colData(sce)) %>%
# Calculate for each image the area and beta cell density
group_by(ImageNumber) %>%
summarise(ImageName = unique(ImageName),
Stage = unique(stage),
betaCellCount = sum(CellType == "beta"),
isletCellCount = sum(CellCat == "islet")) %>%
mutate(betaCellFraction = betaCellCount / isletCellCount) %>%
arrange(desc(betaCellFraction))`summarise()` ungrouping output (override with `.groups` argument)Next, we will plot the cell-types on images after reordering them by beta cell fraction. We will further subset the SingleCellExperiment object to only contain iselt cells.
cur_sce <- sce[,sce$CellCat == "islet"]
cur_order <- match(cur_summary$ImageName, mcols(masks)$ImageName)
# Define the colors for the different cell types
ct_colours <- vector(mode = "character", length = length(unique(cur_sce$CellType)))
names(ct_colours) <- unique(cur_sce$CellType)
ct_colours["beta"] <- "yellow"
ct_colours["alpha"] <- "firebrick1"
ct_colours["delta"] <- "firebrick3"
ct_colours["gamma"] <- "deeppink4"
plotCells(mask = masks[cur_order],
object = cur_sce,
img_id = "ImageName",
cell_id = "CellNumber",
colour_by = "CellType",
scale_bar = list(length = 100,
label = "",
colour = "black"),
legend = list(margin = 100),
colour = list(CellType = ct_colours),
missing_colour = "white",
background_colour = "gray",
image_title = list(text = paste(cur_summary$ImageName, "-",
cur_summary$Stage),
colour = "black"))
# Save plot
plotCells(mask = masks[cur_order],
object = cur_sce,
img_id = "ImageName",
cell_id = "CellNumber",
colour_by = "CellType",
scale_bar = list(length = 100,
label = "",
colour = "black"),
legend = list(colour_by.legend.cex = 9,
margin = 100),
colour = list(CellType = ct_colours),
missing_colour = "white",
background_colour = "gray",
image_title = list(text = paste(cur_summary$ImageName, "-",
cur_summary$Stage),
colour = "black"),
save_plot = list(filename = "docs/final_figures/main/Fig_3.png"))Here, we see the progressive decline in beta cell fractions. The images are ordered based on T1D stage: Non-diabetic, Onset and Long-Duration, as expected. We further see an increased morphological irregularity in Long-Duration islets.
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.5
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] dplyr_1.0.0 cytomapper_1.1.1
[3] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[5] DelayedArray_0.14.0 matrixStats_0.56.0
[7] Biobase_2.48.0 GenomicRanges_1.40.0
[9] GenomeInfoDb_1.24.2 IRanges_2.22.2
[11] S4Vectors_0.26.1 BiocGenerics_0.34.0
[13] EBImage_4.30.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 locfit_1.5-9.4 lattice_0.20-41
[4] fftwtools_0.9-8 png_0.1-7 rprojroot_1.3-2
[7] digest_0.6.25 R6_2.4.1 tiff_0.1-5
[10] backports_1.1.7 evaluate_0.14 ggplot2_3.3.1
[13] pillar_1.4.4 zlibbioc_1.34.0 rlang_0.4.6
[16] whisker_0.4 raster_3.1-5 Matrix_1.2-18
[19] rmarkdown_2.2 stringr_1.4.0 htmlwidgets_1.5.1
[22] RCurl_1.98-1.2 munsell_0.5.0 compiler_4.0.0
[25] httpuv_1.5.4 xfun_0.14 pkgconfig_2.0.3
[28] htmltools_0.4.0 tidyselect_1.1.0 gridExtra_2.3
[31] tibble_3.0.1 GenomeInfoDbData_1.2.3 codetools_0.2-16
[34] viridisLite_0.3.0 crayon_1.3.4 later_1.1.0.1
[37] bitops_1.0-6 grid_4.0.0 gtable_0.3.0
[40] lifecycle_0.2.0 git2r_0.27.1 magrittr_1.5
[43] scales_1.1.1 stringi_1.4.6 XVector_0.28.0
[46] viridis_0.5.1 fs_1.4.1 promises_1.1.1
[49] sp_1.4-2 generics_0.0.2 ellipsis_0.3.1
[52] vctrs_0.3.1 RColorBrewer_1.1-2 tools_4.0.0
[55] glue_1.4.1 purrr_0.3.4 jpeg_0.1-8.1
[58] abind_1.4-5 yaml_2.2.1 colorspace_1.4-1
[61] knitr_1.28