Figure 4
Nils Eling and Nicolas Damond
2020-07-08
Last updated: 2020-07-08
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Knit directory: cytomapper_publication/
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This script reproduces the analysis performed in Figure 4. Here, we will load the libraries and data for this figure:
library(cytomapper)
library(dplyr)
sce <- readRDS("data/PancreasData/pancreas_sce.rds")
images <- readRDS("data/PancreasData/pancreas_images.rds")Similar to Figure 3, we will now order the images based on the mean intensity of all PIN positive pixels.
PIN_mean <- unlist(lapply(images,
function(x){
cur_x <- x[,,"PIN"]
mean(cur_x[cur_x > 0])
}))Here, we will visualize the marker pro-insulin (PIN) across all images. Due to the progressive decline in beta cell function, we expect PIN expression to be reduced in Long-Duration patients. However, a similar analysis can be performed to visually detect image-to-image irregularities in background stain.
We will normalize across all images to keep the differences in staining intensity between images intact.
# Specify the image order
cur_order <- order(PIN_mean, decreasing = TRUE)
# Collect metadata for images
cur_meta <- as_tibble(colData(sce)) %>%
group_by(ImageNumber) %>%
summarise_at(vars("ImageName", "ImageFullName", "stage"), unique) %>%
mutate(ImageFullName = gsub(".tiff", "", ImageFullName)) %>%
as.data.frame()
rownames(cur_meta) <- cur_meta$ImageFullName
cur_meta <- cur_meta[names(PIN_mean)[cur_order],]
images <- normalize(images)
images <- normalize(images, inputRange = c(0, 0.2))
plotPixels(image = images[cur_order],
colour_by = "PIN",
scale_bar = list(length = 100,
label = "",
colour = "white"),
colour = list(PIN = c("black", "yellow")),
legend = list(margin = 100,
colour_by.title.cex = 8,
colour_by.labels.cex = 4),
image_title = list(text = cur_meta$stage,
colour = "white",
cex = 3))
# Save plot
plotPixels(image = images[cur_order],
colour_by = "PIN",
scale_bar = list(length = 100,
label = "",
colour = "white"),
colour = list(PIN = c("black", "yellow")),
legend = list(margin = 100,
colour_by.title.cex = 8,
colour_by.labels.cex = 4),
image_title = list(text = cur_meta$stage,
colour = "white",
cex = 3),
save_plot = list(filename = "docs/final_figures/main/Fig_4.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