Last updated: 2025-08-05

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Rmd 6531274 reneeisnowhere 2025-07-22 first commit 
library(tidyverse)

Custom imageJ macros to identify cell nuclei and ROIs:

// === STEP 1: Open first .tif image from the folder ===
folder = "C:\\userfolders\\Images_EVOS\\If_cardiotox\\87-1 images\\C_DAPI\\";
saveRoiFolder = "C:\\userfolders\\Images_EVOS\\If_cardiotox\\87-1 images\\outline\\";

list = getFileList(folder);

for (i = 0; i < list.length; i++) {
    if (endsWith(list[i], ".tif")) {
        // Open image
        open(folder + list[i]);
        originalTitle = getTitle();

        // === STEP 2: Duplicate and close original ===
        baseName = replace(getTitle(), ".tif", "");  // removes .tif extension
        newTitle = baseName;  // or whatever matches your naming
        run("Duplicate...", "title=" + newTitle);
        dupTitle = getTitle(); // duplicated image becomes active
        selectImage(originalTitle);
        close();

        // === STEP 3: Process duplicated image ===
        selectImage(dupTitle);
        run("8-bit");
        setAutoThreshold("Otsu dark");
        setOption("BlackBackground", true);
        run("Convert to Mask");
        run("Watershed");

        // === STEP 4: Analyze particles ===
        run("Analyze Particles...", "size=21-Infinity circularity=0.30-1.00 show=[Count Masks] display exclude summarize add");

        // === STEP 5: Save ROIs ===
        roiManager("Select", 0); // Avoid saving empty ROI manager
        saveName = replace(dupTitle, ".tif", "") + ".zip";
        roiManager("Save", saveRoiFolder + saveName);

        // Cleanup (optional)
        close();
        maskTitle = "Count Masks of " + dupTitle;
        if (isOpen(maskTitle)) {
            selectImage(maskTitle);
             close();
            }

        roiManager("Deselect");
        roiManager("Reset");

       // break; // Remove this line if you want to process all images in the folder
    }
}

Custom imageJ macros to use nuclei ROIs and detect any RED channel intesity for positive gammaH2AX staining

// === USER SETTINGS ===
roiFolder = "C:\\userfolders\\Images_EVOS\\If_cardiotox\\87-1 images\\outline\\";
txredFolder = "C:\\userfolders\\Images_EVOS\\If_cardiotox\\87-1 images\\C_TxRed\\";
outputFolder = "C:\\userfolders\\Images_EVOS\\If_cardiotox\\87-1 images\\DAPI_counts\\";
signalThreshold = 5;  // define what counts as a "positive signal"

// === GET ROI FILES ===
roiFiles = getFileList(roiFolder);
// Before the loop, open/create summary file
masterFile = outputFolder + "Master_summary.csv";
// Write header (run once, before loop)
if (File.exists(masterFile)) File.delete(masterFile);
File.append("Sample,Total_ROIs,Positive_ROIs\n", masterFile);

// === LOOP OVER EACH ROI FILE ===
for (i = 0; i < roiFiles.length; i++) {
    roiFile = roiFiles[i];
    
    // Only process .zip ROI files
    if (!endsWith(roiFile, ".zip")) continue;

    // Remove suffixes more precisely
    baseName = replace(roiFile, ".zip", "");  // e.g. "3h_87_DOX_B_DAPI_0004"

    // === Build full filenames ===
    roiPath = roiFolder + roiFile;
    txredImage = replace(baseName, "_DAPI_", "_TxRed_") + ".tif";
    txredPath = txredFolder + txredImage;
    outputCSV = outputFolder + "Cell_intensity_" + baseName + ".csv";

    // === Check if TxRed image exists ===
    if (!File.exists(txredPath)) {
        print("Skipping: TxRed image not found for", baseName);
        continue;
    }

    // === Open image ===
    open(txredPath);
    imageTitle = getTitle();

    // === Load ROI file ===
    roiManager("Reset");
    roiManager("Open", roiPath);
    roiManager("Show None");
    roiManager("Show All");

    // === Prepare image for analysis ===
    run("8-bit");
    setAutoThreshold("Otsu dark");
    getThreshold(lower, upper);

// Set a minimum acceptable threshold value (e.g., 25)
minThreshold = 10;
if (lower < minThreshold) {
    lower = minThreshold;
}

// Apply threshold with adjusted floor
setThreshold(lower, 255);
run("Convert to Mask");

    // === Measure signal in ROIs ===
    run("Set Measurements...", "area mean min redirect=None decimal=3");
    roiCount = roiManager("Count");

    if (roiCount == 0) {
        print("No ROIs in", roiFile);
        close(); // Close image
        continue;
    }

    roiIndexes = newArray(roiCount);
    for (j = 0; j < roiCount; j++) {
        roiIndexes[j] = j;
    }

    roiManager("Select", roiIndexes);
    roiManager("Measure");

    // === Count ROIs with mean > threshold ===
    positiveCount = 0;
    for (j = 0; j < roiCount; j++) {
        value = getResult("Mean", j);
        if (value > signalThreshold) {
            positiveCount++;
        }
    }
// === Optional quality control: flag unexpectedly low or high counts
minPos = 25;   // adjust to your expected lower limit
maxPos = 450;  // adjust to your expected upper limit

if (positiveCount < minPos || positiveCount > maxPos) {
    print("⚠️ WARNING: Sample", baseName, "has", positiveCount, "positive ROIs (expected between", minPos, "and", maxPos, ")");

    // Display the image and ROIs for inspection
    selectImage(imageTitle);
    roiManager("Show All");

    // Pause and allow manual threshold/ROI review
    waitForUser("Inspect and adjust this sample manually if needed.\nWhen done, click OK to re-measure and continue.");

    // Re-measure based on updated ROIs
    run("Clear Results");
    roiCount = roiManager("Count");

    roiIndexes = newArray(roiCount);
    for (j = 0; j < roiCount; j++) {
        roiIndexes[j] = j;
    }
    roiManager("Select", roiIndexes);
    roiManager("Measure");

    // Re-count positives after manual fix
    positiveCount = 0;
    for (j = 0; j < roiCount; j++) {
        value = getResult("Mean", j);
        if (value > signalThreshold) {
            positiveCount++;
        }
    }

    print("✅ After manual adjustment:", positiveCount, "positive ROIs");
}

    // === Save Results table ===
    saveAs("Results", outputCSV);
    // Inside your processing loop, after counting positives:
line = baseName + "," + roiCount + "," + positiveCount + "\n";
File.append(line, masterFile);

    // === Optional: Print summary to log ===
    print(baseName, ": ", positiveCount, "/", roiCount, " ROIs positive (>", signalThreshold, ")");

    // === Cleanup ===
    close(); // Close TxRed image
    roiManager("Reset");
    run("Clear Results");
}
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
Nuclei_gamma_count_87<- read_delim("data/Final_four_data/re_analysis/IF_data/Nuclei_gamma_count_87.txt",delim="\t")
Nuclei_gamma_count_77<- read_delim("data/Final_four_data/re_analysis/IF_data/Nuclei_gamma_count_77.txt",delim="\t")
Nuclei_gamma_count_71<- read_delim("data/Final_four_data/re_analysis/IF_data/Nuclei_gamma_count_71.txt",delim="\t")

Nuclei_gamma_count_75<- read_delim("data/Final_four_data/re_analysis/IF_data/Nuclei_gamma_count_75.txt",delim="\t")
Ind_A_table <- Nuclei_gamma_count_87 %>% 
  dplyr::select(Sample,percentage) %>% 
  separate_wider_delim(Sample,delim = "_",names = c("time","ind","trt","focus"),too_many = "merge") %>% 
  group_by(trt,time) %>%
  mutate(group_number = rep(c("A","B"), length.out = n())) %>%
  mutate(trt=if_else(trt=="MTZ","MTX",trt)) %>% 
  ungroup() %>% 
  pivot_wider(., id_cols=c(time, ind,trt), names_from = group_number, values_from = percentage) %>% 
  rowwise() %>% 
  mutate(total = round(mean(c_across(c(A, B)), na.rm = TRUE), digits = 1)) %>% 
  ungroup() %>% 
  mutate(ind="A")

Ind_B_table <- Nuclei_gamma_count_77 %>% 
  dplyr::select(Sample,percentage) %>% 
  separate_wider_delim(Sample,delim = "_",names = c("time","ind","trt","focus"),too_many = "merge") %>% 
  group_by(trt,time) %>%
  mutate(group_number = rep(c("A","B"), length.out = n())) %>%
   mutate(trt=if_else(trt=="MTZ","MTX",trt)) %>% 
  ungroup() %>% 
  pivot_wider(., id_cols=c(time, ind,trt), names_from = group_number, values_from = percentage) %>% 
  rowwise() %>% 
  mutate(total = round(mean(c_across(c(A, B)), na.rm = TRUE), digits = 1)) %>% 
  ungroup() %>% 
  mutate(ind="B")

Ind_C_table <- Nuclei_gamma_count_71 %>% 
  dplyr::select(Sample,percentage) %>% 
  separate_wider_delim(Sample,delim = "_",names = c("time","ind","trt","focus"),too_many = "merge") %>% 
  group_by(trt,time) %>%
  mutate(group_number = rep(c("A","B"), length.out = n())) %>%
   mutate(trt=if_else(trt=="MTZ","MTX",trt)) %>% 
  ungroup() %>% 
  pivot_wider(., id_cols=c(time, ind,trt), names_from = group_number, values_from = percentage) %>% 
  rowwise() %>% 
  mutate(total = round(mean(c_across(c(A, B)), na.rm = TRUE), digits = 1)) %>% 
  ungroup() %>% 
  mutate(ind="C")



Ind_D_table <- Nuclei_gamma_count_75 %>% 
  dplyr::select(Sample,percentage) %>% 
  separate_wider_delim(Sample,delim = "_",names = c("time","ind","trt","focus"),too_many = "merge") %>% 
  group_by(trt,time) %>%
  mutate(group_number = rep(c("A","B"), length.out = n())) %>%
   mutate(trt=if_else(trt=="MTZ","MTX",trt)) %>% 
  ungroup() %>% 
  pivot_wider(., id_cols=c(time, ind,trt), names_from = group_number, values_from = percentage) %>% 
  rowwise() %>% 
  mutate(total = round(mean(c_across(c(A, B)), na.rm = TRUE), digits = 1)) %>% 
  ungroup() %>% 
  mutate(ind="D")
bind_rows(Ind_A_table,Ind_B_table) %>%
  bind_rows(., Ind_C_table) %>% 
  bind_rows(., Ind_D_table) %>% 
  mutate(time=factor(time, levels =c("3h","24h")),
         trt=factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>% 
  ggplot(.,aes(x=trt, y=total))+
  geom_boxplot(aes(fill=trt))+
  geom_point(aes(colour = ind))+
  ggsignif:: geom_signif(comparisons = list(
      c("VEH", "DOX"),
      c("VEH", "EPI"),
      c("VEH", "DNR"),
      c("VEH", "MTX"),
      c("VEH", "TRZ")),
      step_increase = 0.1,
      map_signif_level = FALSE, 
      test = "t.test")+
      facet_wrap(~time)+
  theme_bw()+
  scale_fill_manual(values=drug_pal)

Version Author Date
c79f4be reneeisnowhere 2025-07-29 
bind_rows(Ind_A_table,Ind_B_table) %>%
  bind_rows(., Ind_C_table) %>% 
  bind_rows(., Ind_D_table) %>% 
  mutate(time=factor(time, levels =c("3h","24h")),
         trt=factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>% 
  ggplot(.,aes(x=trt, y=total))+
  geom_boxplot(aes(fill=trt))+
  geom_point(aes(colour = ind))+
  # ggsignif:: geom_signif(comparisons = list(
  #     c("VEH", "DOX"),
  #     c("VEH", "EPI"),
  #     c("VEH", "DNR"),
  #     c("VEH", "MTX"),
  #     c("VEH", "TRZ")),
  #     step_increase = 0.1,
  #     map_signif_level = FALSE, 
  #     test = "t.test")+
      facet_wrap(~time)+
  theme_bw()+
  scale_fill_manual(values=drug_pal)

Version Author Date
c79f4be reneeisnowhere 2025-07-29

making table for future publishing

bind_rows(Ind_A_table,Ind_B_table) %>%
  bind_rows(., Ind_C_table) %>% 
  bind_rows(., Ind_D_table) %>% 
  write_delim(., "data/Final_four_data/re_analysis/ATAC_excel_outputs/All_nuclei_gamma_counts.txt",delim="\t")

sessionInfo()
R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.4 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
 [5] purrr_1.0.4     readr_2.1.5     tidyr_1.3.1     tibble_3.3.0   
 [9] ggplot2_3.5.2   tidyverse_2.0.0 workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] sass_0.4.10        generics_0.1.4     stringi_1.8.7      hms_1.1.3         
 [5] digest_0.6.37      magrittr_2.0.3     timechange_0.3.0   evaluate_1.0.4    
 [9] grid_4.4.2         RColorBrewer_1.1-3 fastmap_1.2.0      rprojroot_2.0.4   
[13] jsonlite_2.0.0     processx_3.8.6     whisker_0.4.1      ps_1.9.1          
[17] promises_1.3.3     httr_1.4.7         scales_1.4.0       jquerylib_0.1.4   
[21] cli_3.6.5          crayon_1.5.3       rlang_1.1.6        bit64_4.6.0-1     
[25] withr_3.0.2        cachem_1.1.0       yaml_2.3.10        parallel_4.4.2    
[29] tools_4.4.2        tzdb_0.5.0         ggsignif_0.6.4     httpuv_1.6.16     
[33] vctrs_0.6.5        R6_2.6.1           lifecycle_1.0.4    git2r_0.36.2      
[37] bit_4.6.0          fs_1.6.6           vroom_1.6.5        pkgconfig_2.0.3   
[41] callr_3.7.6        pillar_1.11.0      bslib_0.9.0        later_1.4.2       
[45] gtable_0.3.6       glue_1.8.0         Rcpp_1.1.0         xfun_0.52         
[49] tidyselect_1.2.1   rstudioapi_0.17.1  knitr_1.50         dichromat_2.0-0.1 
[53] farver_2.1.2       htmltools_0.5.8.1  labeling_0.4.3     rmarkdown_2.29    
[57] compiler_4.4.2     getPass_0.2-4