update to version 0.8.4

SFB-ELAINE · Apr 4, 2024 · ffcf50b · ffcf50b
1 parent 45ba0a0
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diff --git a/CITATION.cff b/CITATION.cff
@@ -9,7 +9,7 @@ type: software
 license: GPL-3.0-or-later
 title: 'detectCilia: Detect and measure the lengths of primary cilia in microscopy
   images'
-version: 0.8.3
+version: 0.8.4
 doi: 10.5281/zenodo.10521546
 abstract: The package loads confocal fluorescence microscopy images and automatically
   determines the positions and lengths of primary cilia of the cells and counts nuclei.
@@ -27,7 +27,7 @@ preferred-citation:
     email: [email protected]
     orcid: https://orcid.org/0000-0001-6836-9865
   year: '2024'
-  notes: R package version 0.8.3
+  notes: R package version 0.8.4
   doi: 10.5281/zenodo.10521546
   url: https://github.com/SFB-ELAINE/detectCilia
 repository-code: https://github.com/SFB-ELAINE/detectCilia
@@ -78,17 +78,13 @@ references:
   title: BiocManager
   abstract: 'BiocManager: Access the Bioconductor Project Package Repository'
   notes: Imports
-  url: https://bioconductor.github.io/BiocManager/
+  url: https://github.com/Bioconductor/BiocManager
   repository: https://CRAN.R-project.org/package=BiocManager
   authors:
   - family-names: Morgan
     given-names: Martin
     email: [email protected]
     orcid: https://orcid.org/0000-0002-5874-8148
-  - family-names: Ramos
-    given-names: Marcel
-    email: [email protected]
-    orcid: https://orcid.org/0000-0002-3242-0582
   year: '2024'
   version: '>= 1.30.19'
 - type: software
@@ -100,7 +96,7 @@ references:
   authors:
   - family-names: Wickham
     given-names: Hadley
-    email: hadley@posit.co
+    email: hadley@rstudio.com
     orcid: https://orcid.org/0000-0003-4757-117X
   - family-names: François
     given-names: Romain
@@ -110,10 +106,6 @@ references:
   - family-names: Müller
     given-names: Kirill
     orcid: https://orcid.org/0000-0002-1416-3412
-  - family-names: Vaughan
-    given-names: Davis
-    email: [email protected]
-    orcid: https://orcid.org/0000-0003-4777-038X
   year: '2024'
   version: '>= 1.0.10'
 - type: software
@@ -123,7 +115,7 @@ references:
   url: https://github.com/aoles/EBImage
   repository: https://bioconductor.org/
   authors:
-  - family-names: Oleś
+  - family-names: Oles
     given-names: Andrzej
   - family-names: Pau
     given-names: Gregoire
@@ -138,7 +130,6 @@ references:
   - family-names: Marais
     given-names: Philip A.
   year: '2024'
-  doi: 10.18129/B9.bioc.EBImage
   version: '>= 4.40.0'
 - type: software
   title: magrittr
@@ -157,7 +148,8 @@ references:
   version: '>= 2.0.3'
 - type: software
   title: readCzi
-  abstract: 'readCzi: Read CZI image files, convert them to tifs, and save metadata'
+  abstract: 'readCzi: readCzi: R package to read CZI image files, convert them to
+    tifs, and save metadata'
   notes: Imports
   url: https://github.com/SFB-ELAINE/readCzi
   authors:
@@ -176,12 +168,12 @@ references:
   authors:
   - family-names: Wickham
     given-names: Hadley
-    email: hadley@posit.co
+    email: hadley@rstudio.com
   - family-names: Hester
     given-names: Jim
   - family-names: Bryan
     given-names: Jennifer
-    email: jenny@posit.co
+    email: jenny@rstudio.com
     orcid: https://orcid.org/0000-0002-6983-2759
   year: '2024'
   version: '>= 2.1.3'
@@ -194,9 +186,9 @@ references:
   authors:
   - family-names: Henry
     given-names: Lionel
-    email: lionel@posit.co
+    email: lionel@rstudio.com
   - family-names: Wickham
     given-names: Hadley
-    email: hadley@posit.co
+    email: hadley@rstudio.com
   year: '2024'
   version: '>= 1.0.6'
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: detectCilia
 Title: Detect and measure the lengths of primary cilia in microscopy images
-Version: 0.8.3
+Version: 0.8.4
 Authors@R: 
     person("Kai", "Budde-Sagert", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-6836-9865"))

diff --git a/R/detectCilia.R b/R/detectCilia.R
@@ -1313,8 +1313,8 @@ detectCilia <- function(
         }
       }
       rm(list = c("jx", "jy", "df_cilium_points_connect_shifted"))
-      
-      
+
+
       df_cilium_points_connect <- df_cilium_points_connect[
         df_cilium_points_connect$possibleCilium,]
 
@@ -1386,7 +1386,7 @@ detectCilia <- function(
 
           # Mark all structures that are too small and therefore may not be a cilium
           for(k in unique(df_cilium_points_connect$ciliumNumber)){
-            if(sum(df_cilium_points_connect$ciliumNumber == k) < min_cilium_area_in_pixels){
+            if(sum(df_cilium_points_connect$ciliumNumber == k) < floor(min_cilium_area_in_pixels/2)){
               df_cilium_points_connect$possibleCilium[
                 df_cilium_points_connect$ciliumNumber == k] <- FALSE
             }
@@ -1609,72 +1609,30 @@ detectCilia <- function(
       !df_cilium_information$ciliumNumber %in% cilium_numbers_to_be_removed,]
   }
 
-  ###new from here
+
   # Delete all cilia that are disconnected
   # In every found cilium (cluster), delete separated cilium parts that
   # are not directly connected (being vicinity_connect apart) to the
   # brightest part of that cilium
-  
+
   df_cilium_all$disconnectedCilia <- FALSE
   df_cilium_all$ClusterNumber <- 0
-  
+
   for(i in unique(df_cilium_all$ciliumNumber)){
     # print(i)
     df_dummy <- df_cilium_all[df_cilium_all$ciliumNumber == i,]
-
-    # # Create a data frame with (x, y) coordinates
-    # coordinates <- data.frame(x = df_dummy$pos_x, y = df_dummy$pos_y)
-    # 
-    # # Calculate pairwise distances
-    # distances <- as.matrix(dist(coordinates, method = "manhattan"))
-    # distances[!lower.tri(distances)] <- NA
-    # 
-    # # Set the threshold distance (e.g., 2 units)
-    # threshold_distance <- 1
-    # 
-    # # Initialize labels
-    # labels <- rep(NA, length(df_dummy$pos_x))
-    # current_label <- 1
-    # 
-    # # Assign labels based on connectivity
-    # for (k in 1:(length(x_coords) - 1)) {
-    #   if(is.na(labels[k])) {
-    #     neighbors <- which(distances[,k] <= threshold_distance)
-    #     labels[neighbors] <- current_label
-    #     current_label <- current_label + 1
-    #   }
-    # }
-    # 
-    # 
-    # 
-    # 
-    # 
-    # 
-    # distances <- as.matrix(dist(cbind(df_dummy$pos_x, df_dummy$pos_y), upper = FALSE, method = "manhattan"))
-    # distances[!lower.tri(distances)] <- NA
-    # 
-    # threshold_distance <- 1
-    # 
-    # connected_pairs <- which(distances <= threshold_distance, arr.ind = TRUE)
-    # 
-    # for (k in 1:nrow(connected_pairs)) {
-    #   point1 <- connected_pairs[k, "row"]
-    #   point2 <- connected_pairs[k, "col"]
-    #   
-    #   df_dummy
-    # }
-
+
     j <- 1
-    
+
     rows_gone_through <- j
     rows_to_go_through <- 1:length(df_dummy$pos_x)
     rows_to_go_through <- rows_to_go_through[rows_to_go_through != j]
-    
+
     threshold_distance <- 1
     df_dummy$ClusterNumber[j] <- 1
-    
+
     if(length(df_dummy$ClusterNumber) > 1){
-      
+
       next_point <- rows_to_go_through[
         rows_to_go_through %in%
           which(df_dummy$pos_x %in% (df_dummy$pos_x[j] - threshold_distance) : (df_dummy$pos_x[j] + threshold_distance) &
@@ -1684,112 +1642,96 @@ detectCilia <- function(
       }else{
         j <- next_point
       }
-      
+
       while(0 %in% df_dummy$ClusterNumber){
-        
+
         .pos_x_distance <- df_dummy$pos_x[j] -
           df_dummy$pos_x
-        
+
         .pos_y_distance <- df_dummy$pos_y[j] -
           df_dummy$pos_y
-        
+
         .pos_x_distance[abs(.pos_x_distance) <= threshold_distance] <- 0
         .pos_y_distance[abs(.pos_y_distance) <= threshold_distance] <- 0
-        
+
         .distance <- abs(.pos_x_distance) + abs(.pos_y_distance)
-        
+
         # Get the cluster number (close cilium that has been detected)
         ClusterNumber_dummy <-
           unique(df_dummy$ClusterNumber[.distance == 0])
-        
+
         if(length(ClusterNumber_dummy) == 1 && ClusterNumber_dummy == 0){
           # Advance cluster number number because there is no cluster close by
           ClusterNumber <- max(df_dummy$ClusterNumber) + 1
         }else{
           # Points belong to already existing cluster
           ClusterNumber <- ClusterNumber_dummy[!(ClusterNumber_dummy == 0)]
         }
-
-        # if(length(ClusterNumber) > 1){
-        #   print(paste0("The following clusters of cilium ", i,
-        #                " are now one: ", paste0(ClusterNumber, collapse = ", ")))
-        #   for(k in 2:length(ClusterNumber)){
-        #     df_dummy$ClusterNumber[
-        #       df_dummy$ClusterNumber == ClusterNumber[k]] <-
-        #       ClusterNumber[1]
-        #   }
-        #   rm(k)
-        #   
-        # }else{
+
           df_dummy$ClusterNumber[
             df_dummy$ClusterNumber == 0 & .distance == 0] <- ClusterNumber
-        # }
-        # 
+
           rows_to_go_through <- rows_to_go_through[rows_to_go_through != j]
           next_point <- rows_to_go_through[
             rows_to_go_through %in%
               which(df_dummy$pos_x %in% (df_dummy$pos_x[j] - threshold_distance) : (df_dummy$pos_x[j] + threshold_distance) &
                       df_dummy$pos_y %in% (df_dummy$pos_y[j] - threshold_distance) : (df_dummy$pos_y[j] + threshold_distance))][1]
           if(is.na(next_point)){
-            j <- rows_to_go_through[1]
+            point_connected_to_others <- FALSE
+            l <- 1
+            while(!point_connected_to_others && l <= length(rows_to_go_through)){
+              .pos_x_distance <- df_dummy$pos_x[rows_to_go_through[l]] -
+                df_dummy$pos_x
+
+              .pos_y_distance <- df_dummy$pos_y[rows_to_go_through[l]] -
+                df_dummy$pos_y
+
+              .pos_x_distance[abs(.pos_x_distance) <= threshold_distance] <- 0
+              .pos_y_distance[abs(.pos_y_distance) <= threshold_distance] <- 0
+
+              .distance <- abs(.pos_x_distance) + abs(.pos_y_distance)
+
+              if(0 %in% .distance[rows_gone_through]){
+                j <- rows_to_go_through[l]
+                point_connected_to_others <- TRUE
+              }else{
+                l <- l+1
+              }
+            }
+            if(!point_connected_to_others){
+              j <- rows_to_go_through[1]
+            }
+            rows_gone_through <- c(rows_gone_through, j)
           }else{
             j <- next_point
+            rows_gone_through <- c(rows_gone_through, j)
           }
-          
+
       }
-      rm(list = c("j", "ClusterNumber"))
-
-      # df_test <- df_dummy %>% 
-      #   dplyr::group_by(ClusterNumber) %>%
-      #   dplyr::summarise(meanIntensity = mean(.data$fluorescence_intensity),
-      #                    clusterSize = length(.data$fluorescence_intensity))
-      # 
-      # # Remove all clusters that are too small
-      # df_test <- df_test[!(df_test$clusterSize < min_cilium_area_in_pixels),]
-      # 
-      # cluster_number_with_heighest_mean_intensity <- df_test$ClusterNumber[
-      #   df_test$meanIntensity == max(df_test$meanIntensity)]
-      # 
-      # if(length(cluster_number_with_heighest_mean_intensity)>1){
-      #   print("Please check the determination of the cluster number.")
-      # }
-      # 
-      # df_dummy$disconnectedPart[
-      #   df_dummy$ClusterNumber != cluster_number_with_heighest_mean_intensity] <- TRUE
-      # 
-      # df_dummy <- df_dummy[!df_dummy$disconnectedPart,]
-      # df_dummy <- df_dummy[,!names(df_dummy)=="ClusterNumber"]
-
-      # # Keep only rows that have been found
-      # if(i == unique(df_cilium_points$ciliumNumber)[1]){
-      #   df_cilium_points2 <- df_dummy
-      # }else{
-      #   df_cilium_points2 <- rbind(df_cilium_points2, df_dummy)
-      # }
-
+      rm(list = c("j", "l", "ClusterNumber"))
+
       if(max(df_dummy$ClusterNumber) > 1){
         df_cilium_all$disconnectedCilia[df_cilium_all$ciliumNumber == i] <- TRUE
       }
-      
+
     }
-    
+
   }
   rm(list = c("i", "df_dummy"))
-  
+
   # Remove cilia with disconnected cilia
   disconnectedCilia <- unique(df_cilium_all$ciliumNumber[df_cilium_all$disconnectedCilia])
-  
+
   df_cilium_information <- df_cilium_information[!df_cilium_information$ciliumNumber %in% disconnectedCilia, ]
   df_cilium_all <- df_cilium_all[!df_cilium_all$ciliumNumber %in% disconnectedCilia,]
-  
+
   # Drop extra columns
   df_cilium_all <-
     df_cilium_all[,!names(df_cilium_all)=="disconnectedCilia"]
   df_cilium_all <-
     df_cilium_all[,!names(df_cilium_all)=="ClusterNumber"]
 
-  ###new up to here
-
+
   # Renumber cilia
   .number <- 1
   df_cilium_information$ciliumNumber_NEW <- NA

diff --git a/inst/CITATION b/inst/CITATION
@@ -4,7 +4,7 @@ bibentry(bibtype = "Manual",
   title = "{detectCilia}: R package for detecting the lengths of primary cilia",
   author = person("Kai", "Budde-Sagert"),
   year = 2024,
-  note = "R package version 0.8.3",
+  note = "R package version 0.8.4",
   doi = "10.5281/zenodo.10521546",
   url = "https://github.com/SFB-ELAINE/detectCilia"
   )