chore: naming change and document

- `performCellposeSegmentation()` -> `doCellposeSegmentation()`

NAMESPACE

@@ -167,6 +167,7 @@ export(dimPlot2D)
 export(dimPlot3D)
 export(distGiottoImage)
 export(doCellSegmentation)
+export(doCellposeSegmentation)
 export(doClusterProjection)
 export(doFeatureSetEnrichment)
 export(doGiottoClustree)
@@ -241,6 +242,7 @@ export(getGiottoImage)
 export(getMultiomics)
 export(getNearestNetwork)
 export(getONTraCv1Input)
+export(getONTraCv2Input)
 export(getPolygonInfo)
 export(getRainbowColors)
 export(getSpatialEnrichment)
@@ -294,12 +296,12 @@ export(overlapToMatrix)
 export(overlapToMatrixMultiPoly)
 export(overlaps)
 export(pDataDT)
-export(performCellposeSegmentation)
 export(pieCellTypesFromEnrichment)
 export(plotCCcomDotplot)
 export(plotCCcomHeatmap)
 export(plotCPF)
 export(plotCTCompositionInNicheCluster)
+export(plotCTCompositionInProbCluster)
 export(plotCellProximityFeatSpot)
 export(plotCellProximityFeats)
 export(plotCellTypeNTScore)

R/cell_segmentation.R

@@ -92,10 +92,10 @@ doCellSegmentation <- function(
 #'
 #' @title perform cellpose segmentation
 #' @description
-#'  
+#'
 #' perform the Giotto Wrapper of cellpose segmentation. This is for a model inference to generate segmentation mask file from input image.
 #' main parameters needed
-#' @name performCellposeSegmentation
+#' @name doCellposeSegmentation
 #' @param image_dir character, required. Provide a path to a gray scale or a three channel image.
 #' @param python_path python environment with cellpose installed. default = "giotto_cellpose".
 #' @param mask_output required. Provide a path to the output mask file.
@@ -127,88 +127,88 @@ doCellSegmentation <- function(
 #' @returns No return variable, as this will write directly to output path provided.
 #' @examples
 #' # example code
-#' performCellposeSegmentation(image_dir = input_image, mask_output = output, channel_1 = 2, channel_2 = 1, model_name = 'cyto3',batch_size=4)
+#' doCellposeSegmentation(image_dir = input_image, mask_output = output, channel_1 = 2, channel_2 = 1, model_name = 'cyto3',batch_size=4)
 #' @export
-performCellposeSegmentation <- function(python_env = 'giotto_cellpose',
+doCellposeSegmentation <- function(python_env = 'giotto_cellpose',
                                         image_dir,
                                         mask_output,
                                         channel_1 = 0,
                                         channel_2 = 0,
                                         model_name = 'cyto3',
-                                        batch_size=8, 
-                                        resample=TRUE, 
+                                        batch_size=8,
+                                        resample=TRUE,
                                         channel_axis=NULL,
-                                        z_axis=NULL, 
-                                        normalize=TRUE, 
-                                        invert=FALSE, 
-                                        rescale=NULL, 
+                                        z_axis=NULL,
+                                        normalize=TRUE,
+                                        invert=FALSE,
+                                        rescale=NULL,
                                         diameter=NULL,
-                                        flow_threshold=0.4, 
-                                        cellprob_threshold=0.0, 
-                                        do_3D=FALSE, 
+                                        flow_threshold=0.4,
+                                        cellprob_threshold=0.0,
+                                        do_3D=FALSE,
                                         anisotropy=NULL,
-                                        stitch_threshold=0.0, 
-                                        min_size=15, 
-                                        niter=NULL, 
-                                        augment=FALSE, 
+                                        stitch_threshold=0.0,
+                                        min_size=15,
+                                        niter=NULL,
+                                        augment=FALSE,
                                         tile=TRUE,
-                                        tile_overlap=0.1, 
-                                        bsize=224, 
-                                        interp=TRUE, 
+                                        tile_overlap=0.1,
+                                        bsize=224,
+                                        interp=TRUE,
                                         compute_masks=TRUE,
                                         progress=NULL,
                                         verbose = TRUE,...){
-    
-    
+
+
     #Check Input arguments
     model_name <- match.arg(model_name, unique(c('cyto3', 'cyto2', 'cyto','nuclei', model_name)))
     ## Load required python libraries
     GiottoClass::set_giotto_python_path(python_env)
     GiottoUtils::package_check('cellpose',repository = 'pip')
-    
+
     cellpose <- reticulate::import("cellpose")
     np <- reticulate::import("numpy")
     cv2 <- reticulate::import("cv2")
     torch <- reticulate::import("torch")
     message('successfully loaded giotto environment with cellpose.')
-    
+
     if (!(torch$cuda$is_available())){
         warning('GPU is not available for this session, inference may be slow.\n ')
     }
-    
+
     GiottoUtils::vmsg(.v = verbose, .is_debug = F,'Loading Image from ',image_dir)
-    
+
     img <- cellpose$io$imread(image_dir)
     GiottoUtils::vmsg(.v = verbose, .is_debug = F,'Loading Model...')
-    
+
     model_to_seg <- cellpose$models$Cellpose(model_type=model_name,gpu = torch$cuda$is_available())
     channel_to_seg <- as.integer(c(channel_1,channel_2))
-    
+
     GiottoUtils::vmsg(.v = verbose, .is_debug = F,'Segmenting Image...')
     segmentation <- model_to_seg$eval
-    
-    result <- segmentation(img, 
-                           diameter=diameter, 
+
+    result <- segmentation(img,
+                           diameter=diameter,
                            channels=channel_to_seg,
                            batch_size = batch_size,
-                           resample=resample, 
+                           resample=resample,
                            channel_axis=channel_axis,
-                           z_axis=z_axis, 
-                           normalize=normalize, 
-                           invert=invert, 
-                           rescale=rescale, 
-                           flow_threshold=flow_threshold, 
-                           cellprob_threshold=cellprob_threshold, 
-                           do_3D=do_3D, 
+                           z_axis=z_axis,
+                           normalize=normalize,
+                           invert=invert,
+                           rescale=rescale,
+                           flow_threshold=flow_threshold,
+                           cellprob_threshold=cellprob_threshold,
+                           do_3D=do_3D,
                            anisotropy=anisotropy,
-                           stitch_threshold=stitch_threshold, 
-                           min_size=min_size, 
-                           niter=niter, 
-                           augment=augment, 
+                           stitch_threshold=stitch_threshold,
+                           min_size=min_size,
+                           niter=niter,
+                           augment=augment,
                            tile=tile,
-                           tile_overlap=tile_overlap, 
-                           bsize=bsize, 
-                           interp=interp, 
+                           tile_overlap=tile_overlap,
+                           bsize=bsize,
+                           interp=interp,
                            compute_masks=compute_masks,
                            progress=progress)
     masks <- result[[1]]