Introduction

  1. File (path to *sis)
  2. ImageSet (image batch *czi)
  3. ROI (blank)
  4. Type (Segment)
  5. Tags (Mito Child, Nucleus Child, PV1 child, RBC, RBC area Filter, RBC segmented, Touching Edge Filter RBC)
  6. Image Set
  7. Id (C3, Field P1 - Experiment-161.czi)
  8. Name (Segment #001 (RBC segmented))
  9. Position, Well (well C3, C4)
  10. Field, Well
  11. Area, 2D Oriented Bounds (µm²)
  12. no. Children
  13. Children Ids
  14. Children Names
  15. Parent Ids
  16. Parent Names
  17. Min, Intensities #1
  18. Max, Intensities #1
  19. Mean, Intensities #1
  20. Sum, Intensities #1
  21. SD, Intensities #1
  22. SNR (Mean/SD), Intensities #1
  23. Min, Intensities #2
  24. Max, Intensities #2
  25. Mean, Intensities #2
  26. Sum, Intensities #2
  27. SD, Intensities #2
  28. SNR (Mean/SD), Intensities #2
  29. Min, Intensities #3
  30. Max, Intensities #3
  31. Mean, Intensities #3
  32. Sum, Intensities #3
  33. SD, Intensities #3
  34. SNR (Mean/SD), Intensities #3
  35. Min, Intensities #4
  36. Max, Intensities #4
  37. Mean, Intensities #4
  38. Sum, Intensities #4
  39. SD, Intensities #4
  40. SNR (Mean/SD), Intensities #4

Tags entries (E3):

  • iRBC filter (child only), Mito Child, Nucleus Child, PV1 child, RBC, RBC area Filter, RBC segmented, Touching Edge Filter RBC (512)
  • Mito Child, Nucleus Child, PV1 child, RBC, RBC area Filter, RBC segmented, Touching Edge Filter RBC (1440)
  • Mito, Mito Child, Mito Segmenter, Mito size filter, Mito within RBC (594)
  • Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC (421)
  • PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC inside (521)
  • PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC intersecting (49)

The desired format is:

  1. Treatment group (Well C3,D3)
  2. RBC area um^2 (#1, mean)
  3. RBC count (number of entries with (#1, no. entries)
  4. iRBC area um^2 (#2, mean)
  5. iRBC count (#2, no. entries)
  6. PV1 area um^2 (#2, mean)
  7. PV1 intensity RLU (#2, mean)
  8. PV1 count (#2, no. entries)
  9. Mitochondria area um^2 (#3)
  10. Mitochondria intensity RLU (#3)
  11. Mitochondria count (#3)
  12. Nucleus area um^2 (#4)
  13. Nucleus intensity RLU (#4)
  14. Nucleus count (#4)
library("kableExtra")
library("DT")

Samplesheet

ss <- read.table("samplesheet.tsv",header=TRUE,sep="\t",row.names=1)

ss |> kbl(caption="Description of wells") |> kable_paper("hover", full_width = F)
Description of wells
Drug Description
C3 DMSO 0.01% Control
C4 Piperaquine 270nM Food vacuole and free radical damage
C5 Amodiaquine 156nM Food vacuole and free radical damage
C6 DDSM-1 65nM Inhibit DNA synthesis
C7 Atovaquone 2.7nM Inhibit DNA synthesis
C8 Cipargamin 10nM Cause cell swelling due to Na+ increase
C9 MMV665878 653nM Cause cell swelling due to Na+ increase
C10 W-518 220nM Vesicle trafficking
C11 OGHL250 49uM Vesicle trafficking
D4 Artemisinin 50nM Free radical damage
D5 DHA 50nM Free radical damage
D6 NPPB 626uM Block nutrient uptake
D7 Furozemide 1.34mM Block nutrient uptake
D8 Bepsi 40.7nM Inhibit protein folding
D9 Bepristat 440nM Inhibit protein folding
D10 M5717 5.6nM Block protein synthesis
D11 Cyclohexamide 1.5uM Block protein synthesis

Example with well C3

x <- read.csv("20250701_COMBINED_Drug_pair_2_measurements.tsv",header=TRUE,sep="\t")

myt <- as.data.frame(table(x$Tags) )

myt |> kbl(caption="Frequency of features across all wells") |> kable_paper("hover", full_width = F)
Frequency of features across all wells
Var1 Freq
iRBC filter (child only), Mito Child, Nucleus Child, PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC 3303
Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC inside, Mitochondria 3295
Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC intersecting, Mitochondria 97
Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC inside 3032
Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC intersecting 73
PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC 14281
PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC inside 3122
PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC intersecting 318 
WELL="C3"
y <- subset(x,Position..Well==WELL)

myt <- table(y$Tags)

myt |> kbl(caption="Frequency of features in well C3") |> kable_paper("hover", full_width = F)
Frequency of features in well C3
Var1 Freq
iRBC filter (child only), Mito Child, Nucleus Child, PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC 239
Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC inside, Mitochondria 245
Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC intersecting, Mitochondria 6
Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC inside 240
Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC intersecting 7
PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC 1028
PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC inside 227
PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC intersecting 24 
# here is the important code for one well (C3)
rbc <- y[which(y$Parent.Names==""),]
RBC_count <- nrow(rbc)
RBC_area <- mean(rbc$Area..2D.Oriented.Bounds...µm...)

irbc <- subset(y,Tags=="iRBC filter (child only), Mito Child, Nucleus Child, PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC")
iRBC_count <- nrow(irbc)
iRBC_area <- mean(irbc$Area..2D.Oriented.Bounds...µm...)

pv1 <- subset(y, Tags == "PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC" | Tags =="PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC inside" | Tags == "PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC intersecting" )
PV1_area <- mean(pv1$Area..2D.Oriented.Bounds...µm...)
PV1_intensity <- mean(pv1$Mean..Intensities..2)
PV1_count <- nrow(pv1)

mito <- subset(y, Tags == "Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC inside, Mitochondria" | Tags == "Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC intersecting, Mitochondria")
Mito_area <- mean(mito$Area..2D.Oriented.Bounds...µm...)
Mito_intensity <- mean(mito$Mean..Intensities..1)
Mito_count <- nrow(mito)

nucleus <- subset(y, Tags == "Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC inside" | Tags == "Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC intersecting" )
Nucleus_area <- mean(nucleus$Area..2D.Oriented.Bounds...µm...)
Nucleus_intensity <- mean(nucleus$Mean..Intensities..3)
Nucleus_count <- nrow(nucleus)

res <- c("RBC count"=RBC_count,
  "RBC area"=RBC_area,
  "iRBC count"=iRBC_count,
  "iRBC area"=iRBC_area,
  "PV1_area"=PV1_area,
  "PV1_intensity"=PV1_intensity,
  "PV1_count"=PV1_count,
  "Mito_area"=Mito_area,
  "Mito_intensity"=Mito_intensity,
  "Mito_count"=Mito_count,
  "Nucleus_area"=Nucleus_area,
  "Nucleus_intensity"=Nucleus_intensity,
  "Nucleus_count"=Nucleus_count)

res
##         RBC count          RBC area        iRBC count         iRBC area 
##       1267.000000         59.952071        239.000000         59.762435 
##          PV1_area     PV1_intensity         PV1_count         Mito_area 
##         50.565121       3479.049900       1279.000000          8.837521 
##    Mito_intensity        Mito_count      Nucleus_area Nucleus_intensity 
##       4644.203291        251.000000          3.721479       5905.622685 
##     Nucleus_count 
##        247.000000
WELLS <- unique(x$Position..Well)

Make a function and run for all wells

Now put it in a function so we can iterate over all wells.

# define the function
smry <- function(y) {
  rbc <- y[which(y$Parent.Names==""),]
  RBC_count <- nrow(rbc)
  RBC_area <- mean(rbc$Area..2D.Oriented.Bounds...µm...)

  irbc <- subset(y,Tags=="iRBC filter (child only), Mito Child, Nucleus Child, PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC")
  iRBC_count <- nrow(irbc)
  iRBC_area <- mean(irbc$Area..2D.Oriented.Bounds...µm...)

  pv1 <- subset(y, Tags == "PV1 child, RBC, RBC area Filter, RBC segmenter, Touching Edge Filter RBC" | Tags =="PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC inside" | Tags == "PV1, PV1 child, PV1 Segmenter, PV1 size filter, PV1 within RBC, PV1 within RBC intersecting" )
  PV1_area <- mean(pv1$Area..2D.Oriented.Bounds...µm...)
  PV1_intensity <- mean(pv1$Mean..Intensities..2)
  PV1_count <- nrow(pv1)

  mito <- subset(y, Tags == "Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC inside, Mitochondria" | Tags == "Mito Child, Mito Segmenter, Mito size filter, Mito within RBC, Mito within RBC intersecting, Mitochondria")
  Mito_area <- mean(mito$Area..2D.Oriented.Bounds...µm...)
  Mito_intensity <- mean(mito$Mean..Intensities..1)
  Mito_count <- nrow(mito)

  nucleus <- subset(y, Tags == "Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC inside" | Tags == "Nuclei, Nuclei Segmenter, Nucleus Child, Nucleus size filter, Nucleus within RBC, Nucleus within RBC intersecting" )
  Nucleus_area <- mean(nucleus$Area..2D.Oriented.Bounds...µm...)
  Nucleus_intensity <- mean(nucleus$Mean..Intensities..3)
  Nucleus_count <- nrow(nucleus)

  res <- c("RBC count"=RBC_count,
    "RBC area"=RBC_area,
    "iRBC count"=iRBC_count,
    "iRBC area"=iRBC_area,
    "PV1_area"=PV1_area,
    "PV1_intensity"=PV1_intensity,
    "PV1_count"=PV1_count,
    "Mito_area"=Mito_area,
    "Mito_intensity"=Mito_intensity,
    "Mito_count"=Mito_count,
    "Nucleus_area"=Nucleus_area,
    "Nucleus_intensity"=Nucleus_intensity,
    "Nucleus_count"=Nucleus_count)

  res
}

# get a vector of all wells
WELLS <- unique(x$Position..Well)

# now run the function for all wells
z <- lapply(WELLS, function(w) {
  y <- subset(x,Position..Well==w)
  smry(y)
} )

# z is a list of vectors, which we can convert to a matrix
z <- do.call(rbind,z)
rownames(z) <- WELLS
# peek at the ddata format
head(z,2)
##    RBC count RBC area iRBC count iRBC area PV1_area PV1_intensity PV1_count
## C3      1267 59.95207        239  59.76244 50.56512      3479.050      1279
## C4      1108 59.68130        227  58.51197 49.47871      4268.908      1122
##    Mito_area Mito_intensity Mito_count Nucleus_area Nucleus_intensity
## C3  8.837521       4644.203        251     3.721479          5905.623
## C4  8.326510       5050.113        234     3.486644          5498.474
##    Nucleus_count
## C3           247
## C4           218

Show the data as a kableExtra table.

zz <- merge(ss,z,by=0)

zz |> kbl(caption="Summarised data by well") |> kable_paper("hover", full_width = F)
Summarised data by well
Row.names Drug Description RBC count RBC area iRBC count iRBC area PV1_area PV1_intensity PV1_count Mito_area Mito_intensity Mito_count Nucleus_area Nucleus_intensity Nucleus_count
C3 DMSO 0.01% Control 1267 59.95207 239 59.76244 50.56512 3479.050 1279 8.837521 4644.2033 251 3.721479 5905.623 247
C4 Piperaquine 270nM Food vacuole and free radical damage 1108 59.68130 227 58.51197 49.47871 4268.908 1122 8.326510 5050.1132 234 3.486644 5498.474 218
C5 Amodiaquine 156nM Food vacuole and free radical damage 1386 60.43123 277 57.54707 50.59340 3940.025 1395 6.574243 1977.6610 287 2.532401 5102.204 244
C6 DDSM-1 65nM Inhibit DNA synthesis 1412 58.77855 283 57.96876 49.23086 4178.926 1418 8.203615 4783.9154 283 3.307747 5504.826 271
C7 Atovaquone 2.7nM Inhibit DNA synthesis 1347 58.98824 268 58.30964 49.13302 4251.045 1364 8.150090 5079.2931 275 3.475012 6388.834 266
C8 Cipargamin 10nM Cause cell swelling due to Na+ increase 1430 58.59986 251 56.36729 49.71329 4172.217 1443 6.318472 2950.6643 259 2.769500 5064.717 193
C9 MMV665878 653nM Cause cell swelling due to Na+ increase 1320 58.34666 221 55.12919 50.03600 3600.101 1332 5.033644 873.6914 228 2.808488 4582.392 166
D10 M5717 5.6nM Block protein synthesis 1511 57.00724 286 56.61045 47.94023 4072.360 1520 7.729455 4574.3273 300 3.084335 6886.685 286
D11 Cyclohexamide 1.5uM Block protein synthesis 1429 55.36446 253 55.11494 47.15147 3945.445 1437 7.407381 4186.0737 264 3.201629 6610.812 240
D4 Artemisinin 50nM Free radical damage 1206 57.57922 234 55.63792 48.76311 4066.048 1216 8.799574 4150.5616 242 3.794716 8460.831 236
D5 DHA 50nM Free radical damage 1261 58.67011 223 56.80388 49.86090 3864.880 1271 6.213268 2209.1254 226 3.016739 7140.583 211
D8 Bepsi 40.7nM Inhibit protein folding 1454 57.19384 272 55.55021 49.00180 4076.862 1457 8.780328 4545.8542 272 3.541456 8486.803 273
D9 Bepristat 440nM Inhibit protein folding 1453 57.00203 269 55.10699 48.24839 4101.749 1467 7.540342 4385.4794 271 3.188267 7282.694 254

Show as data.table. This function allows you to download as an Excel file.

DT::datatable(
  { head(zz,1000) },

  extensions = 'Buttons',
  filter = list(position = 'top', clear = FALSE),

  options = list(
    paging = TRUE,
    searching = TRUE,
    fixedColumns = TRUE,
    autoWidth = TRUE,
    ordering = TRUE,
    dom = 'tB',
    buttons = c('copy', 'csv', 'excel'),
    pageLength = 50,
    search = list(regex = TRUE, caseInsensitive = TRUE)),

  rownames= TRUE)

Principal component analysis

First quantify CVs, then do PCA.

cv <- function(x) { sd(x)/mean(x) }

par(mar=c(5.1,9.1,4.1,2.1))

barplot(apply(z,2,cv),horiz=T,las=1,xlab="CV")

par(mar=c(5.1,4.1,4.1,2.1))

pca <- prcomp(z, scale=TRUE)

scree <- pca$sdev
names(scree) <- 1:length(scree)
barplot(scree, main="Screeplot")

biplot(pca, main="PCA")

More variations on biplot clusstering here: https://www.geeksforgeeks.org/how-to-create-a-biplot-in-r/

Session information

For reproucibility

sessionInfo()
## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0  LAPACK version 3.10.0
## 
## 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=en_US.UTF-8   
##  [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       
## 
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] DT_0.33          kableExtra_1.4.0
## 
## loaded via a namespace (and not attached):
##  [1] vctrs_0.6.5        svglite_2.2.1      cli_3.6.5          knitr_1.50        
##  [5] rlang_1.1.6        xfun_0.52          stringi_1.8.7      textshaping_1.0.1 
##  [9] jsonlite_2.0.0     glue_1.8.0         htmltools_0.5.8.1  sass_0.4.10       
## [13] scales_1.4.0       rmarkdown_2.29     crosstalk_1.2.1    evaluate_1.0.3    
## [17] jquerylib_0.1.4    fastmap_1.2.0      yaml_2.3.10        lifecycle_1.0.4   
## [21] stringr_1.5.1      compiler_4.5.1     RColorBrewer_1.1-3 htmlwidgets_1.6.4 
## [25] rstudioapi_0.17.1  systemfonts_1.2.3  farver_2.1.2       digest_0.6.37     
## [29] viridisLite_0.4.2  R6_2.6.1           dichromat_2.0-0.1  pillar_1.10.2     
## [33] magrittr_2.0.3     bslib_0.9.0        tools_4.5.1        xml2_1.3.8        
## [37] cachem_1.1.0