@@ -84,40 +84,21 @@ lisa <-

       BPcellType <- BPPARAM

     }

-    if (!fast) {

-      message("Generating local L-curves. ")

-      if (identical(BPimage, BPcellType)) {

-        message("You might like to consider setting BPPARAM to run the calculations in parallel.")

-      }

-      curveList <-

-        BiocParallel::bplapply(

-          cellSummary,

-          generateCurves,

-          Rs = Rs,

-          window = window,

-          window.length = window.length,

-          BPcellType = BPcellType,

-          BPPARAM = BPimage,

-          sigma = sigma

-        )

-    }

-    if (fast) {

-      message("Generating local L-curves. If you run out of memory, try 'fast = FALSE'.")

-

-      curveList <-

-        BiocParallel::bplapply(

-          cellSummary,

-          inhomLocalK,

-          Rs = Rs,

-          sigma = sigma,

-          window = window,

-          window.length = window.length,

-          minLambda = minLambda,

-          lisaFunc = lisaFunc,

-          BPPARAM = BPimage

-        )

-    }

+    message("Generating local L-curves.")

+

+    curveList <-

+      BiocParallel::bplapply(

+        cellSummary,

+        inhomLocalK,

+        Rs = Rs,

+        sigma = sigma,

+        window = window,

+        window.length = window.length,

+        minLambda = minLambda,

+        lisaFunc = lisaFunc,

+        BPPARAM = BPimage

+      )

     curvelist <- lapply(curveList, as.data.frame)

     curves <- as.matrix(dplyr::bind_rows(curvelist))

@@ -17,8 +17,6 @@

 #' @param sigma A numeric variable used for scaling when filting inhomogeneous L-curves.

 #' @param lisaFunc Either "K" or "L" curve.

 #' @param minLambda  Minimum value for density for scaling when fitting inhomogeneous L-curves.

-#' @param fast A logical describing whether to use a fast approximation of the

-#' inhomogeneous local L-curves.

 #'

 #' @return A matrix of LISA curves

 #'

@@ -66,8 +64,7 @@ lisaClust <-

            whichParallel = "imageID",

            sigma = NULL,

            lisaFunc = "K",

-           minLambda = 0.05,

-           fast = TRUE) {

+           minLambda = 0.05) {

     if (methods::is(cells, "SummarizedExperiment")) {

       cd <- spicyR:::.format_data(

         cells, imageID, cellType, spatialCoords, FALSE

@@ -81,8 +78,7 @@ lisaClust <-

                          whichParallel = whichParallel,

                          sigma = sigma,

                          lisaFunc = lisaFunc,

-                         minLambda = minLambda,

-                         fast = fast

+                         minLambda = minLambda

       )

       kM <- kmeans(lisaCurves, k)

       regions <- paste("region", kM$cluster, sep = "_")

@@ -103,8 +99,7 @@ lisaClust <-

                 whichParallel = whichParallel,

                 sigma = sigma,

                 lisaFunc = lisaFunc,

-                minLambda = minLambda,

-                fast = fast

+                minLambda = minLambda

             )

             kM <- kmeans(lisaCurves, k)