diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c3eb78a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,17 @@
+# Large test datasets (downloaded locally, not versioned)
+data/
+data2/
+data3/
+
+# Local test caches / logs / generated outputs
+tests_local/*.rds
+tests_local/*.log
+untitled_consensus_cluster/
+
+# R / RStudio
+.Rproj.user/
+.Rhistory
+.RData
+.Ruserdata
+*.o
+*.so
diff --git a/DESCRIPTION b/DESCRIPTION
index 8b40e54..2895b54 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: CellTrek
 Title: Spatial Charting of Single Cell Transcriptomes in Tissues
-Version: 0.0.94
+Version: 0.0.95
 Authors@R: c(
     person("Runmin", 'Wei', email='wander1021@gmail.com', role=c('aut', 'cre')),
     person("Siyuan", 'He', email='SHe2@mdanderson.org', role=c('aut', 'ctb')) )
@@ -10,8 +10,8 @@ License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.2
-Imports: 
-    akima,
+Imports:
+    interp,
     data.table,
     dbscan,
     dplyr,
diff --git a/NAMESPACE b/NAMESPACE
index ba4a4d8..761e1b4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -37,6 +37,6 @@ import(shiny)
 import(tibble)
 import(tidyr)
 import(visNetwork)
-importFrom(akima,interpp)
+importFrom(interp,interpp)
 importFrom(geometry,delaunayn)
 importFrom(packcircles,circleRepelLayout)
diff --git a/NEWS.md b/NEWS.md
new file mode 100644
index 0000000..ffc4ff7
--- /dev/null
+++ b/NEWS.md
@@ -0,0 +1,34 @@
+# CellTrek 0.0.95
+
+## Seurat v5 compatibility
+
+CellTrek now runs on Seurat v5 (tested with Seurat 5.3.0 / SeuratObject 5.2.0 on R 4.3).
+Verified end-to-end on VisiumV1 (brain_st_cortex + brain_sc) and VisiumV2 (stxBrain +
+allen_cortex) data through traint, celltrek, scoloc (KL/DT/KD), scoexp (cc) and run_kdist.
+
+* Replaced direct assay-slot access (`obj[[assay]]@data` / `@counts` / `@scale.data`)
+  with the version-agnostic `GetAssayData()` / `SetAssayData()` accessors throughout
+  `celltrek.R`, `scoexp.R` and `FindCorMarkers.R`. The old v3 idiom of moving counts into
+  the data slot now uses `SetAssayData(..., layer = 'data')`.
+* Made dimensional-reduction keys v5-compliant (`celltrekraw_`, `celltrek_`, `pca_`,
+  `umap_`, `tsne_`).
+* `CreateSeuratObject()` inputs are coerced with `as.matrix()` where needed.
+
+## Spatial coordinates
+
+* `traint()` resolves ST coordinates with the priority: `coord_df` > the image
+  `@coordinates[,coord_xy]` slot (VisiumV1, kept at original full-resolution scale) >
+  `GetTissueCoordinates()` (VisiumV2/FOV `x`/`y`). Preferring `@coordinates` avoids the
+  coordinate down-scaling that `GetTissueCoordinates()` applies to VisiumV1 objects
+  (which shrank spot distances and made the fixed `repel_r` over-disperse the charting).
+  Writes to `image@coordinates` are guarded to VisiumV1 (VisiumV2 has no such slot).
+* New `traint(coord_df = )` argument: supply ST spatial coordinates directly to bypass
+  differences/naming issues across image structures. Resolution priority is
+  `coord_df` > auto-detect > a clear error asking for `coord_df`. Rownames must cover all
+  ST cells/spots; columns named in `coord_xy` are used if present, otherwise the first two
+  columns are taken as (coord_x, coord_y).
+
+## Dependencies
+
+* Interpolation now uses `interp::interpp` instead of `akima::interpp` (akima is not
+  available on all platforms, e.g. macOS arm64). Updated DESCRIPTION and NAMESPACE.
diff --git a/R/FindCorMarkers.R b/R/FindCorMarkers.R
index a8998b4..ff9c074 100644
--- a/R/FindCorMarkers.R
+++ b/R/FindCorMarkers.R
@@ -12,7 +12,7 @@
 #'
 #' @examples CorMarkers <- FindCorMarkers(srt_inp=test_seurat, assay='RNA', features=c('k_dist', 'sig_score'), method='spearman')
 FindCorMarkers <- function(srt_inp, assay='RNA', features, method='spearman') {
-  exp_dat <- as.matrix(srt_inp[[assay]]@data)
+  exp_dat <- as.matrix(GetAssayData(srt_inp, assay=assay, layer='data'))
   output_df <- data.frame(p_val=numeric(), cor=numeric(), p_val_adj=numeric(), feature=character(), gene=character())
   if (all(features %in% colnames(srt_inp@meta.data)))
   for (i in 1:length(features)) {
diff --git a/R/celltrek.R b/R/celltrek.R
index c71c2e1..01885c6 100644
--- a/R/celltrek.R
+++ b/R/celltrek.R
@@ -7,6 +7,10 @@
 #' @param nfeatures number of features for integration
 #' @param cell_names cell cluster/type column name in SC meta data
 #' @param coord_xy coordinates column names in ST images slot
+#' @param coord_df optional data frame/matrix of ST spatial coordinates to use directly,
+#'   bypassing coordinate auto-detection from the image slot. Rownames must cover all ST
+#'   cells/spots. If it contains the columns named in \code{coord_xy} those are used,
+#'   otherwise the first two columns are taken as (coord_x, coord_y).
 #' @param gene_kept selected genes to be kept during integration
 #' @param norm normalization method: LogNormalize/SCTransform
 #'
@@ -20,15 +24,64 @@
 #' @examples
 #' st_sc_traint <- traint(st_data=brain_st, sc_data=brain_sc, st_assay='Spatial', sc_assay='scint', nfeatures=2000, cell_names='cell_names', coord_xy=c('imagerow', 'imagecol'), gene_kept=NULL)
 traint <- function (st_data, sc_data, st_assay='Spatial', sc_assay='scint', norm='LogNormalize', nfeatures=2000,
-                    cell_names='cell_names', coord_xy=c('imagerow', 'imagecol'), gene_kept=NULL, ...) {
+                    cell_names='cell_names', coord_xy=c('imagerow', 'imagecol'), coord_df=NULL, gene_kept=NULL, ...) {
 
   st_data$id <- names(st_data$orig.ident)
   sc_data$id <- names(sc_data$orig.ident)
   sc_data$cell_names <- make.names(sc_data@meta.data[, cell_names])
   st_data$type <- 'st'
   sc_data$type <- 'sc'
-  st_data$coord_x <- st_data@images[[1]]@coordinates[, coord_xy[1]]
-  st_data$coord_y <- st_data@images[[1]]@coordinates[, coord_xy[2]]
+  ## Resolve ST spatial coordinates. Priority:
+  ##  1) user-supplied coord_df (bypasses all image structures);
+  ##  2) the image @coordinates slot columns named in coord_xy (VisiumV1) -- used
+  ##     directly to preserve the original full-resolution coordinate scale;
+  ##  3) GetTissueCoordinates() for image classes without @coordinates
+  ##     (VisiumV2/FOV, which expose x/y). If none work, stop and ask for coord_df.
+  ## NOTE: GetTissueCoordinates() rescales VisiumV1 coordinates by the image scale
+  ## factor, which would shrink spot distances; hence we prefer @coordinates here.
+  st_cells <- Cells(st_data)
+  if (!is.null(coord_df)) {
+    coord_df <- as.data.frame(coord_df)
+    if (all(coord_xy %in% colnames(coord_df))) {
+      xy <- coord_df[, coord_xy, drop=FALSE]
+    } else {
+      if (ncol(coord_df) < 2) stop("coord_df must have at least two columns (coord_x, coord_y)")
+      xy <- coord_df[, 1:2, drop=FALSE]
+    }
+    if (!all(st_cells %in% rownames(xy))) {
+      stop("coord_df rownames must cover all ST cells/spots (Cells(st_data)); ",
+           sum(!st_cells %in% rownames(xy)), " missing")
+    }
+    st_data$coord_x <- xy[st_cells, 1]
+    st_data$coord_y <- xy[st_cells, 2]
+  } else if (length(st_data@images) > 0 &&
+             'coordinates' %in% slotNames(st_data@images[[1]]) &&
+             all(coord_xy %in% colnames(st_data@images[[1]]@coordinates))) {
+    ## VisiumV1: raw imagerow/imagecol (original scale)
+    st_coord <- st_data@images[[1]]@coordinates
+    st_data$coord_x <- st_coord[st_cells, coord_xy[1]]
+    st_data$coord_y <- st_coord[st_cells, coord_xy[2]]
+  } else {
+    st_coord <- tryCatch(Seurat::GetTissueCoordinates(st_data), error=function(e) NULL)
+    if (is.null(st_coord)) {
+      stop("Could not read spatial coordinates from st_data (unsupported image structure). ",
+           "Please supply them via the 'coord_df' argument.")
+    }
+    if (all(coord_xy %in% colnames(st_coord))) {
+      st_data$coord_x <- st_coord[st_cells, coord_xy[1]]
+      st_data$coord_y <- st_coord[st_cells, coord_xy[2]]
+    } else if (all(c('x', 'y') %in% colnames(st_coord))) {
+      ## VisiumV2/FOV: x is the column (imagecol), y is the row (imagerow)
+      st_data$coord_x <- st_coord[st_cells, 'y']
+      st_data$coord_y <- st_coord[st_cells, 'x']
+    } else {
+      stop("Spatial coordinate columns not found (looked for c('", coord_xy[1], "','", coord_xy[2],
+           "') or c('x','y')). Please supply them via the 'coord_df' argument.")
+    }
+  }
+  if (anyNA(st_data$coord_x) || anyNA(st_data$coord_y)) {
+    stop("Some ST cells/spots have missing coordinates after resolution; check coord_df / image coordinates.")
+  }
   DefaultAssay(st_data) <- st_assay
   DefaultAssay(sc_data) <- sc_assay
 
@@ -43,8 +96,8 @@ traint <- function (st_data, sc_data, st_assay='Spatial', sc_assay='scint', norm
     sc_st_features <- union(sc_st_features, gene_kept)
   }
 
-  sc_st_features <- sc_st_features[(sc_st_features %in% rownames(st_data[[st_assay]]@data)) &
-                                     (sc_st_features %in% rownames(sc_data[[sc_assay]]@data))]
+  sc_st_features <- sc_st_features[(sc_st_features %in% rownames(GetAssayData(st_data, assay=st_assay, layer='data'))) &
+                                     (sc_st_features %in% rownames(GetAssayData(sc_data, assay=sc_assay, layer='data')))]
   cat('Using', length(sc_st_features), 'features for integration... \n')
   ###
 
@@ -54,26 +107,29 @@ traint <- function (st_data, sc_data, st_assay='Spatial', sc_assay='scint', norm
 
   cat('Data transfering... \n')
   st_data_trans <- Seurat::TransferData(anchorset = sc_st_anchors,
-                                        refdata = GetAssayData(sc_data, assay = sc_assay, slot='data')[sc_st_features, ], weight.reduction = 'cca')
+                                        refdata = GetAssayData(sc_data, assay = sc_assay, layer='data')[sc_st_features, ], weight.reduction = 'cca')
   st_data@assays$transfer <- st_data_trans
 
   cat('Creating new Seurat object... \n')
   sc_st_meta <- dplyr::bind_rows(st_data@meta.data, sc_data@meta.data)
-  counts_temp <- cbind(data.frame(st_data[['transfer']]@data), data.frame(sc_data[[sc_assay]]@data[sc_st_features, ] %>% data.frame))
+  counts_temp <- cbind(data.frame(GetAssayData(st_data, assay='transfer', layer='data')),
+                       data.frame(GetAssayData(sc_data, assay=sc_assay, layer='data')[sc_st_features, ] %>% data.frame))
   rownames(sc_st_meta) <- make.names(sc_st_meta$id)
   colnames(counts_temp) <- make.names(sc_st_meta$id)
-  sc_st_int <- CreateSeuratObject(counts = counts_temp, assay = 'traint', meta.data = sc_st_meta)
-  sc_st_int[['traint']]@data <- sc_st_int[['traint']]@counts
-  sc_st_int[['traint']]@counts <- matrix(NA, nrow = 0, ncol = 0)
+  sc_st_int <- CreateSeuratObject(counts = as.matrix(counts_temp), assay = 'traint', meta.data = sc_st_meta)
+  sc_st_int <- SetAssayData(sc_st_int, assay='traint', layer='data',
+                            new.data = GetAssayData(sc_st_int, assay='traint', layer='counts'))
 
   cat('Scaling -> PCA -> UMAP... \n')
   sc_st_int <- ScaleData(sc_st_int, features = sc_st_features) %>%
     RunPCA(features = sc_st_features)
   sc_st_int <- RunUMAP(sc_st_int, dims = 1:30)
   sc_st_int@images <- st_data@images
-  sc_st_int@images[[1]]@coordinates <- data.frame(imagerow=sc_st_int@meta.data$coord_x,
-                                                  imagecol=sc_st_int@meta.data$coord_y) %>%
-    set_rownames(rownames(sc_st_int@meta.data))
+  if ('coordinates' %in% slotNames(sc_st_int@images[[1]])) {
+    sc_st_int@images[[1]]@coordinates <- data.frame(imagerow=sc_st_int@meta.data$coord_x,
+                                                    imagecol=sc_st_int@meta.data$coord_y) %>%
+      set_rownames(rownames(sc_st_int@meta.data))
+  }
 
   return (sc_st_int)
 }
@@ -127,7 +183,7 @@ celltrek_repel <- function(celltrek_inp, repel_r=5, repel_iter=10) {
 #' @return A list of 1. celltrek_distance matrix; 2. trained random forest model (optional)
 #'
 #' @import dbscan
-#' @importFrom akima interpp
+#' @importFrom interp interpp
 #' @import magrittr
 #' @import dplyr
 #' @import randomForestSRC
@@ -171,7 +227,7 @@ celltrek_dist <- function (st_sc_int, int_assay='traint', reduction='pca', intp
     }) %>% Reduce(rbind, .)
 
     st_intp_df <- apply(st_pca[, 1:nPCs], 2, function(col_x) {
-      akima::interpp(x=st_pca$coord_x, y=st_pca$coord_y, z=col_x,
+      interp::interpp(x=st_pca$coord_x, y=st_pca$coord_y, z=col_x,
                      linear=intp_lin, xo=st_intp_df$coord_x, yo=st_intp_df$coord_y) %>%
         magrittr::extract2('z')
     }) %>% data.frame(., id='X', type='st_intp', st_intp_df) %>% na.omit
@@ -284,27 +340,26 @@ celltrek_from_dist <- function (dist_mat, coord_df, dist_cut, top_spot=10, spot_
   sc_coord_list <- celltrek_chart(dist_mat=dist_mat, coord_df=coord_df, dist_cut=dist_cut, top_spot=top_spot, spot_n=spot_n, repel_r=repel_r, repel_iter=repel_iter)
   sc_coord_raw <- sc_coord_list[[1]]
   sc_coord <- sc_coord_list[[2]]
-  sc_out <- CreateSeuratObject(counts=sc_data[[sc_assay]]@data[, sc_coord$id_raw] %>% set_colnames(sc_coord$id_new),
+  sc_out <- CreateSeuratObject(counts=as.matrix(GetAssayData(sc_data, assay=sc_assay, layer='data')[, sc_coord$id_raw]) %>% set_colnames(sc_coord$id_new),
                                project='celltrek', assay=sc_assay,
                                meta.data=sc_data@meta.data[sc_coord$id_raw, ] %>%
                                  dplyr::rename(id_raw=id) %>%
                                  mutate(id_new=sc_coord$id_new) %>%
                                  set_rownames(sc_coord$id_new))
   sc_out@meta.data <- dplyr::left_join(sc_out@meta.data, sc_coord) %>% data.frame %>% set_rownames(sc_out$id_new)
-  sc_out[[sc_assay]]@data <- sc_out[[sc_assay]]@counts
-  sc_out[[sc_assay]]@counts <- matrix(nrow = 0, ncol = 0)
+  sc_out <- SetAssayData(sc_out, assay=sc_assay, layer='data', new.data=GetAssayData(sc_out, assay=sc_assay, layer='counts'))
   sc_coord_raw_df <- CreateDimReducObject(embeddings=sc_coord_raw %>%
                                             dplyr::mutate(coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
                                             dplyr::select(c(coord1, coord2)) %>% set_rownames(sc_coord_raw$id_new) %>% as.matrix,
-                                          assay=sc_assay, key='celltrek_raw')
+                                          assay=sc_assay, key='celltrekraw_')
   sc_coord_dr <- CreateDimReducObject(embeddings=sc_coord %>%
                                         dplyr::mutate(coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
                                         dplyr::select(c(coord1, coord2)) %>% set_rownames(sc_coord$id_new) %>% as.matrix,
-                                      assay=sc_assay, key='celltrek')
+                                      assay=sc_assay, key='celltrek_')
   sc_pca_dr <- CreateDimReducObject(embeddings=sc_data@reductions$pca@cell.embeddings[sc_coord$id_raw, ] %>%
-                                      set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='pca')
+                                      set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='pca_')
   sc_umap_dr <- CreateDimReducObject(embeddings=sc_data@reductions$umap@cell.embeddings[sc_coord$id_raw, ] %>%
-                                       set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='umap')
+                                       set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='umap_')
   sc_out@reductions$celltrek <- sc_coord_dr
   sc_out@reductions$celltrek_raw <- sc_coord_raw_df
   sc_out@reductions$pca <- sc_pca_dr
@@ -312,7 +367,9 @@ celltrek_from_dist <- function (dist_mat, coord_df, dist_cut, top_spot=10, spot_
   if (!is.null(st_data)) {
     sc_out@images <- st_data@images
     sc_out@images[[1]]@assay <- DefaultAssay(sc_out)
-    sc_out@images[[1]]@coordinates <- data.frame(imagerow=sc_coord$coord_x, imagecol=sc_coord$coord_y) %>% set_rownames(sc_coord$id_new)
+    if ('coordinates' %in% slotNames(sc_out@images[[1]])) {
+      sc_out@images[[1]]@coordinates <- data.frame(imagerow=sc_coord$coord_x, imagecol=sc_coord$coord_y) %>% set_rownames(sc_coord$id_new)
+    }
     sc_out@images[[1]]@scale.factors$spot_dis <- spot_dis
   }
   output <- list(celltrek=sc_out)
@@ -361,7 +418,7 @@ celltrek <- function (st_sc_int, int_assay='traint', sc_data=NULL, sc_assay='RNA
   if (!is.null(sc_data)) {
     cat('sc data...')
     sc_data$id <- Seurat::Cells(sc_data)
-    sc_out <- CreateSeuratObject(counts=sc_data[[sc_assay]]@data[, sc_coord$id_raw] %>% set_colnames(sc_coord$id_new),
+    sc_out <- CreateSeuratObject(counts=as.matrix(GetAssayData(sc_data, assay=sc_assay, layer='data')[, sc_coord$id_raw]) %>% set_colnames(sc_coord$id_new),
                                  project='celltrek', assay=sc_assay,
                                  meta.data=sc_data@meta.data[sc_coord$id_raw, ] %>%
                                    dplyr::rename(id_raw=id) %>%
@@ -369,36 +426,35 @@ celltrek <- function (st_sc_int, int_assay='traint', sc_data=NULL, sc_assay='RNA
                                    set_rownames(sc_coord$id_new))
     sc_out@meta.data <- dplyr::left_join(sc_out@meta.data, sc_coord) %>% data.frame %>% set_rownames(sc_out$id_new)
 
-    sc_out[[sc_assay]]@data <- sc_out[[sc_assay]]@counts
-    sc_out[[sc_assay]]@counts <- matrix(nrow = 0, ncol = 0)
+    sc_out <- SetAssayData(sc_out, assay=sc_assay, layer='data', new.data=GetAssayData(sc_out, assay=sc_assay, layer='counts'))
     sc_coord_raw_df <- CreateDimReducObject(embeddings=sc_coord_raw %>%
                                               dplyr::mutate(coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
                                               dplyr::select(c(coord1, coord2)) %>% set_rownames(sc_coord_raw$id_new) %>% as.matrix,
-                                            assay=sc_assay, key='celltrek_raw')
+                                            assay=sc_assay, key='celltrekraw_')
     sc_coord_dr <- CreateDimReducObject(embeddings=sc_coord %>%
                                           dplyr::mutate(coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
                                           dplyr::select(c(coord1, coord2)) %>% set_rownames(sc_coord$id_new) %>% as.matrix,
-                                        assay=sc_assay, key='celltrek')
+                                        assay=sc_assay, key='celltrek_')
     sc_out@reductions$celltrek <- sc_coord_dr
     sc_out@reductions$celltrek_raw <- sc_coord_raw_df
     if ('pca' %in% names(sc_data@reductions)) {
       sc_pca_dr <- CreateDimReducObject(embeddings=sc_data@reductions$pca@cell.embeddings[sc_coord$id_raw, ] %>%
-                                          set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='pca')
+                                          set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='pca_')
       sc_out@reductions$pca <- sc_pca_dr
     }
     if ('umap' %in% names(sc_data@reductions)) {
       sc_umap_dr <- CreateDimReducObject(embeddings=sc_data@reductions$umap@cell.embeddings[sc_coord$id_raw, ] %>%
-                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='umap')
+                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='umap_')
       sc_out@reductions$umap <- sc_umap_dr
     }
     if ('tsne' %in% names(sc_data@reductions)) {
       sc_tsne_dr <- CreateDimReducObject(embeddings=sc_data@reductions$tsne@cell.embeddings[sc_coord$id_raw, ] %>%
-                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='tsne')
+                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='tsne_')
       sc_out@reductions$tsne <- sc_tsne_dr
     }
   } else {
     cat('no sc data...')
-    sc_out <- CreateSeuratObject(counts=st_sc_int[[int_assay]]@data[, sc_coord$id_raw] %>%
+    sc_out <- CreateSeuratObject(counts=as.matrix(GetAssayData(st_sc_int, assay=int_assay, layer='data')[, sc_coord$id_raw]) %>%
                                    set_colnames(sc_coord$id_new),
                                  project='celltrek', assay=int_assay,
                                  meta.data=st_sc_int@meta.data[sc_coord$id_raw, ] %>%
@@ -408,39 +464,42 @@ celltrek <- function (st_sc_int, int_assay='traint', sc_data=NULL, sc_assay='RNA
     sc_out$coord_x <- sc_coord$coord_x[match(sc_coord$id_new, sc_out$id_new)]
     sc_out$coord_y <- sc_coord$coord_y[match(sc_coord$id_new, sc_out$id_new)]
 
-    sc_out[[int_assay]]@counts <- matrix(nrow = 0, ncol = 0)
-    sc_out[[int_assay]]@scale.data <- st_sc_int[[int_assay]]@scale.data[, sc_coord$id_raw] %>% set_colnames(sc_coord$id_new)
+    sc_out <- SetAssayData(sc_out, assay=int_assay, layer='data', new.data=GetAssayData(sc_out, assay=int_assay, layer='counts'))
+    sc_out <- SetAssayData(sc_out, assay=int_assay, layer='scale.data',
+                           new.data=GetAssayData(st_sc_int, assay=int_assay, layer='scale.data')[, sc_coord$id_raw] %>% set_colnames(sc_coord$id_new))
     sc_coord_raw_df <- CreateDimReducObject(embeddings=sc_coord_raw %>%
                                               dplyr::mutate(coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
                                               dplyr::select(c(coord1, coord2)) %>% set_rownames(sc_coord_raw$id_new) %>% as.matrix,
-                                            assay=sc_assay, key='celltrek_raw')
+                                            assay=sc_assay, key='celltrekraw_')
     sc_coord_dr <- CreateDimReducObject(embeddings = sc_coord %>%
                                           dplyr::mutate(coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
                                           dplyr::select(c(coord1, coord2)) %>%
                                           set_rownames(sc_coord$id_new) %>%
                                           as.matrix,
-                                        assay=int_assay, key='celltrek')
+                                        assay=int_assay, key='celltrek_')
     sc_out@reductions$celltrek <- sc_coord_dr
     sc_out@reductions$celltrek_raw <- sc_coord_raw_df
     if ('pca' %in% names(st_sc_int@reductions)) {
       sc_pca_dr <- CreateDimReducObject(embeddings=st_sc_int@reductions$pca@cell.embeddings[sc_coord$id_raw, ] %>%
-                                          set_rownames(sc_coord$id_new) %>% as.matrix, assay=int_assay, key='pca')
+                                          set_rownames(sc_coord$id_new) %>% as.matrix, assay=int_assay, key='pca_')
       sc_out@reductions$pca <- sc_pca_dr
     }
     if ('umap' %in% names(st_sc_int@reductions)) {
       sc_umap_dr <- CreateDimReducObject(embeddings=st_sc_int@reductions$umap@cell.embeddings[sc_coord$id_raw, ] %>%
-                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=int_assay, key='umap')
+                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=int_assay, key='umap_')
       sc_out@reductions$umap <- sc_umap_dr
     }
     if ('tsne' %in% names(st_sc_int@reductions)) {
       sc_tsne_dr <- CreateDimReducObject(embeddings=st_sc_int@reductions$tsne@cell.embeddings[sc_coord$id_raw, ] %>%
-                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=int_assay, key='tsne')
+                                           set_rownames(sc_coord$id_new) %>% as.matrix, assay=int_assay, key='tsne_')
       sc_out@reductions$tsne <- sc_tsne_dr
     }
   }
   sc_out@images <- st_sc_int@images
   sc_out@images[[1]]@assay <- DefaultAssay(sc_out)
-  sc_out@images[[1]]@coordinates <- data.frame(imagerow=sc_coord$coord_x, imagecol=sc_coord$coord_y) %>% set_rownames(sc_coord$id_new)
+  if ('coordinates' %in% slotNames(sc_out@images[[1]])) {
+    sc_out@images[[1]]@coordinates <- data.frame(imagerow=sc_coord$coord_x, imagecol=sc_coord$coord_y) %>% set_rownames(sc_coord$id_new)
+  }
   sc_out@images[[1]]@scale.factors$spot_dis <- dist_res$spot_d
   sc_out@images[[1]]@scale.factors$spot_dis_intp <- spot_dis_intp
 
diff --git a/R/scoexp.R b/R/scoexp.R
index 91fb05b..8c82c30 100644
--- a/R/scoexp.R
+++ b/R/scoexp.R
@@ -225,18 +225,18 @@ scoexp <- function(celltrek_inp, sigm=NULL, assay='RNA', gene_select=NULL, zero_
   if (is.null(sigm)) sigm <- celltrek_inp@images[[1]]@scale.factors$spot_dis
   if (is.null(gene_select)) {
     cat('gene filtering...\n')
-    feature_nz <- apply(celltrek_inp[[assay]]@data, 1, function(x) mean(x!=0)*100)
+    feature_nz <- apply(GetAssayData(celltrek_inp, assay=assay, layer='data'), 1, function(x) mean(x!=0)*100)
     features <- names(feature_nz)[feature_nz > zero_cutoff]
     cat(length(features), 'features after filtering...\n')
   } else if (length(gene_select) > 1) {
-    features <- intersect(gene_select, rownames(celltrek_inp[[assay]]@data))
+    features <- intersect(gene_select, rownames(GetAssayData(celltrek_inp, assay=assay, layer='data')))
     if (length(features)==0) stop('No genes in gene_select detected')
   }
   celltrek_inp <- Seurat::ScaleData(celltrek_inp, features=features)
   res <- list(gs=c(), cc=c(), rbfk=c(), wcor=c())
   dist_mat <- dist(celltrek_inp@meta.data[, c('coord_x', 'coord_y')]) %>% as.matrix
   kern_mat <- rbfk(dist_mat, sigm=sigm, zero_diag=F)
-  expr_mat <- t(as.matrix(celltrek_inp[[assay]]@scale.data))
+  expr_mat <- t(as.matrix(GetAssayData(celltrek_inp, assay=assay, layer='scale.data')))
   cat('Calculating spatial-weighted cross-correlation...\n')
   wcor_mat <- wcor(X=expr_mat, W=kern_mat, method=cor_method)
 
diff --git a/README.md b/README.md
index 2f1087b..27cddba 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,8 @@ In this tutorial, we will demonstrate the cell charting workflow based on the mo
 library(devtools)
 install_github("navinlabcode/CellTrek")
 ```
+
+> **Seurat v5 compatibility note.** This version has been updated to run on Seurat v5 (tested with Seurat 5.3.0 / SeuratObject 5.2.0 on R 4.3). Internally, direct assay-slot access (`obj[[assay]]@data`/`@counts`/`@scale.data`) was replaced with the version-agnostic `GetAssayData()`/`SetAssayData()` accessors, and dimensional-reduction keys were made v5-compliant. The interpolation step now depends on the `interp` package (a drop-in replacement for `akima`, which is not available on all platforms). The tutorial code below has likewise been updated for v5 (e.g. `HVFInfo()` instead of `@meta.features`).
 ## 2. Loading the packages and datasets (scRNA-seq and ST data)
 We start by loading the packages needed for the analyses. Please install them if you haven't.
 ``` r
@@ -124,7 +126,6 @@ Based on the CellTrek result, we can further investigate the co-expression patte
 L5 IT cells first are extracted from the charting result.
 ``` r
 brain_celltrek_l5 <- subset(brain_celltrek, subset=cell_type=='L5 IT')
-brain_celltrek_l5@assays$RNA@scale.data <- matrix(NA, 1, 1)
 brain_celltrek_l5$cluster <- gsub('L5 IT VISp ', '', brain_celltrek_l5$cluster)
 DimPlot(brain_celltrek_l5, group.by = 'cluster')
 ```
@@ -132,12 +133,12 @@ DimPlot(brain_celltrek_l5, group.by = 'cluster')
 We select top 2000 variable genes (exclude mitochondrial, ribosomal and high-zero genes)
 ``` r
 brain_celltrek_l5 <- FindVariableFeatures(brain_celltrek_l5)
-vst_df <- brain_celltrek_l5@assays$RNA@meta.features %>% data.frame %>% mutate(id=rownames(.))
-nz_test <- apply(as.matrix(brain_celltrek_l5[['RNA']]@data), 1, function(x) mean(x!=0)*100)
+vst_df <- HVFInfo(brain_celltrek_l5) %>% data.frame %>% mutate(id=rownames(.))
+nz_test <- apply(as.matrix(GetAssayData(brain_celltrek_l5, assay='RNA', layer='data')), 1, function(x) mean(x!=0)*100)
 hz_gene <- names(nz_test)[nz_test<20]
 mt_gene <- grep('^Mt-', rownames(brain_celltrek_l5), value=T)
 rp_gene <- grep('^Rpl|^Rps', rownames(brain_celltrek_l5), value=T)
-vst_df <- vst_df %>% dplyr::filter(!(id %in% c(mt_gene, rp_gene, hz_gene))) %>% arrange(., -vst.variance.standardized)
+vst_df <- vst_df %>% dplyr::filter(!(id %in% c(mt_gene, rp_gene, hz_gene))) %>% arrange(., -variance.standardized)
 feature_temp <- vst_df$id[1:2000]
 ```
 We use scoexp to do the spatial-weighted gene co-expression analysis.
diff --git a/man/traint.Rd b/man/traint.Rd
index 15132e2..f00a2c9 100644
--- a/man/traint.Rd
+++ b/man/traint.Rd
@@ -13,6 +13,7 @@ traint(
   nfeatures = 2000,
   cell_names = "cell_names",
   coord_xy = c("imagerow", "imagecol"),
+  coord_df = NULL,
   gene_kept = NULL,
   ...
 )
@@ -34,6 +35,11 @@ traint(
 
 \item{coord_xy}{coordinates column names in ST images slot}
 
+\item{coord_df}{optional data frame/matrix of ST spatial coordinates to use directly,
+bypassing coordinate auto-detection from the image slot. Rownames must cover all ST
+cells/spots. If it contains the columns named in \code{coord_xy} those are used,
+otherwise the first two columns are taken as (coord_x, coord_y).}
+
 \item{gene_kept}{selected genes to be kept during integration}
 }
 \value{
diff --git a/tests_local/figs/1_celltrek_spatial.png b/tests_local/figs/1_celltrek_spatial.png
new file mode 100644
index 0000000..6ba4d8f
Binary files /dev/null and b/tests_local/figs/1_celltrek_spatial.png differ
diff --git a/tests_local/figs/2_scoloc_KL_mst.png b/tests_local/figs/2_scoloc_KL_mst.png
new file mode 100644
index 0000000..f760a36
Binary files /dev/null and b/tests_local/figs/2_scoloc_KL_mst.png differ
diff --git a/tests_local/figs/3_scoexp_heatmap.png b/tests_local/figs/3_scoexp_heatmap.png
new file mode 100644
index 0000000..af92649
Binary files /dev/null and b/tests_local/figs/3_scoexp_heatmap.png differ
diff --git a/tests_local/figs/4_dataset2_spatial.png b/tests_local/figs/4_dataset2_spatial.png
new file mode 100644
index 0000000..9ff7f0b
Binary files /dev/null and b/tests_local/figs/4_dataset2_spatial.png differ
diff --git a/tests_local/make_figs.R b/tests_local/make_figs.R
new file mode 100644
index 0000000..e5c1309
--- /dev/null
+++ b/tests_local/make_figs.R
@@ -0,0 +1,60 @@
+suppressPackageStartupMessages({
+  library(CellTrek); library(Seurat); library(dplyr)
+  library(ggplot2); library(pheatmap); library(viridis)
+})
+set.seed(1)
+dir.create('tests_local/figs', showWarnings = FALSE)
+ct <- readRDS('tests_local/brain_celltrek_cache.rds')
+
+## ---- Fig 1: spatial charting (charted single cells on tissue coords) ----
+df <- ct@meta.data
+df$cell_type <- factor(df$cell_type, levels = sort(unique(df$cell_type)))
+p1 <- ggplot(df, aes(coord_y, max(coord_x)+min(coord_x)-coord_x, color = cell_type)) +
+  geom_point(size = 0.7, alpha = 0.85) +
+  coord_fixed() + theme_void() +
+  guides(color = guide_legend(override.aes = list(size = 3))) +
+  labs(title = paste0('CellTrek charting: ', nrow(df), ' single cells mapped to tissue'),
+       color = 'cell type')
+ggsave('tests_local/figs/1_celltrek_spatial.png', p1, width = 9, height = 7, dpi = 130)
+cat('fig1 done\n')
+
+## ---- Fig 2: SColoc (KL) consensus co-localization matrix ----
+glut <- c('L2/3 IT','L4','L5 IT','L5 PT','NP','L6 IT','L6 CT','L6b')
+g <- subset(ct, subset = cell_type %in% glut)
+sg <- CellTrek::scoloc(g, col_cell = 'cell_type', use_method = 'KL', eps = 1e-50, boot_n = 20)
+mst <- as.matrix(sg$mst_cons)
+labs <- glut; names(labs) <- make.names(glut)
+rownames(mst) <- colnames(mst) <- labs[rownames(mst)]
+mst_sym <- mst + t(mst)
+png('tests_local/figs/2_scoloc_KL_mst.png', width = 900, height = 800, res = 130)
+pheatmap(mst_sym, cluster_rows = TRUE, cluster_cols = TRUE,
+         color = viridis(100), display_numbers = TRUE, number_format = '%.2f',
+         main = 'SColoc (KL): MST consensus co-localization\n(glutamatergic neurons, 20 bootstraps)')
+dev.off()
+cat('fig2 done\n')
+
+## ---- Fig 3: SCoexp (cc) spatial co-expression heatmap on L5 IT ----
+l5 <- subset(ct, subset = cell_type == 'L5 IT')
+l5 <- FindVariableFeatures(l5, verbose = FALSE)
+vst_df <- HVFInfo(l5) %>% data.frame %>% mutate(id = rownames(.))
+nz <- apply(as.matrix(GetAssayData(l5, assay='RNA', layer='data')), 1, function(x) mean(x!=0)*100)
+hz <- names(nz)[nz < 20]
+mt <- grep('^Mt-', rownames(l5), value = TRUE)
+rp <- grep('^Rpl|^Rps', rownames(l5), value = TRUE)
+vst_df <- vst_df %>% filter(!(id %in% c(mt, rp, hz))) %>% arrange(-variance.standardized)
+feats <- head(na.omit(vst_df$id), 300)
+res <- CellTrek::scoexp(celltrek_inp = l5, assay = 'RNA', approach = 'cc',
+                        gene_select = feats, sigm = 140, avg_cor_min = .3,
+                        zero_cutoff = 3, min_gen = 15, max_gen = 200, maxK = 6, k = 6, reps = 20)
+k_df <- do.call(rbind, lapply(seq_along(res$gs), function(i)
+  data.frame(gene = res$gs[[i]], Module = paste0('K', i))))
+rownames(k_df) <- k_df$gene; k_df$gene <- NULL
+gord <- rownames(k_df)
+png('tests_local/figs/3_scoexp_heatmap.png', width = 850, height = 800, res = 130)
+pheatmap(res$wcor[gord, gord], clustering_method = 'ward.D2',
+         annotation_row = k_df, show_rownames = FALSE, show_colnames = FALSE,
+         treeheight_row = 12, treeheight_col = 12, fontsize = 8,
+         color = viridis(10), main = 'SCoexp: L5 IT spatial co-expression modules')
+dev.off()
+cat('fig3 done | modules:', length(res$gs), '| sizes:', paste(sapply(res$gs, length), collapse=','), '\n')
+cat('ALL FIGS DONE\n')
diff --git a/tests_local/regress_coords.R b/tests_local/regress_coords.R
new file mode 100644
index 0000000..4b3796e
--- /dev/null
+++ b/tests_local/regress_coords.R
@@ -0,0 +1,28 @@
+## Regression check after the coordinate-scale fix: traint must resolve coords for
+## both VisiumV1 (brain_st_cortex, via @coordinates) and VisiumV2 (stxBrain, via GTC x/y).
+suppressPackageStartupMessages({library(CellTrek); library(Seurat); library(dplyr)})
+set.seed(1)
+mini_sc <- function(sc, per=25){ md<-sc@meta.data; k<-unlist(lapply(split(rownames(md), md$cell_type), function(x) sample(x, min(length(x),per)))); sc[,k] }
+
+## ---- VisiumV1 ----
+st1 <- readRDS('data/brain_st_cortex.rds'); sc1 <- readRDS('data/brain_sc.rds')
+st1 <- RenameCells(st1, new.names=make.names(Cells(st1))); sc1 <- RenameCells(sc1, new.names=make.names(Cells(sc1)))
+st1 <- st1[, sample(Cells(st1), 300)]; sc1 <- mini_sc(sc1, 25)
+t1 <- CellTrek::traint(st1, sc1, st_assay='Spatial', sc_assay='RNA', cell_names='cell_type')
+c1 <- na.omit(t1@meta.data[t1$type=='st', c('coord_x','coord_y')])
+cat('VisiumV1: image', class(st1@images[[1]]), '| coord range', paste(round(range(c1$coord_x)),collapse='-'),
+    '| spot_dis', round(median(dbscan::kNN(c1,k=6)$dist)), '| st coords set', all(!is.na(t1$coord_x[t1$type=='st'])), '\n')
+
+## ---- VisiumV2 ----
+e <- new.env(); load('data2/anterior1.rda', envir=e); st2 <- UpdateSeuratObject(get('anterior1', envir=e))
+st2 <- NormalizeData(st2, verbose=FALSE) %>% FindVariableFeatures(nfeatures=2000, verbose=FALSE)
+st2 <- RenameCells(st2, new.names=make.names(Cells(st2)))
+sc2 <- readRDS('data2/allen_cortex.rds'); sc2$cell_type <- as.character(sc2$subclass)
+sc2 <- mini_sc(sc2, 25)
+sc2 <- NormalizeData(sc2,verbose=FALSE) %>% FindVariableFeatures(nfeatures=2000,verbose=FALSE) %>% ScaleData(verbose=FALSE) %>% RunPCA(npcs=30,verbose=FALSE) %>% RunUMAP(dims=1:30,verbose=FALSE)
+sc2 <- RenameCells(sc2, new.names=make.names(Cells(sc2)))
+t2 <- CellTrek::traint(st2, sc2, st_assay='Spatial', sc_assay='RNA', cell_names='cell_type')
+c2 <- na.omit(t2@meta.data[t2$type=='st', c('coord_x','coord_y')])
+cat('VisiumV2: image', class(st2@images[[1]]), '| coord range', paste(round(range(c2$coord_x)),collapse='-'),
+    '| spot_dis', round(median(dbscan::kNN(c2,k=6)$dist)), '| st coords set', all(!is.na(t2$coord_x[t2$type=='st'])), '\n')
+cat('== REGRESS OK ==\n')
diff --git a/tests_local/run_dataset2.R b/tests_local/run_dataset2.R
new file mode 100644
index 0000000..8c96e5f
--- /dev/null
+++ b/tests_local/run_dataset2.R
@@ -0,0 +1,69 @@
+## Second paired dataset test on Seurat v5:
+##   ST  = stxBrain anterior1 (10x Visium, updates to Assay5 + VisiumV2 image)
+##   scRNA = allen_cortex reference (subclass labels)
+## This exercises the VisiumV2 (v5 image class) code path, which the first
+## dataset (brain_st_cortex, VisiumV1) did not.
+suppressPackageStartupMessages({
+  library(CellTrek); library(Seurat); library(dplyr); library(magrittr)
+  library(ggplot2)
+})
+set.seed(1)
+cat('Seurat', as.character(packageVersion('Seurat')), '\n')
+dir.create('tests_local/figs', showWarnings = FALSE)
+
+## ---- ST: stxBrain anterior1 ----
+e <- new.env(); load('data2/anterior1.rda', envir = e)
+st <- UpdateSeuratObject(get('anterior1', envir = e))
+cat('ST anterior1:', ncol(st), 'spots | image class:', class(st@images[[1]]), '\n')
+st <- NormalizeData(st, verbose = FALSE) %>% FindVariableFeatures(nfeatures = 2000, verbose = FALSE)
+st <- RenameCells(st, new.names = make.names(Cells(st)))
+
+## ---- scRNA: allen_cortex, subsample + preprocess ----
+sc <- readRDS('data2/allen_cortex.rds')
+sc$cell_type <- as.character(sc$subclass)
+set.seed(42)
+md <- sc@meta.data
+keep <- unlist(lapply(split(rownames(md), md$cell_type), function(x) sample(x, min(length(x), 70))))
+sc <- subset(sc, cells = keep)
+sc <- NormalizeData(sc, verbose = FALSE) %>%
+  FindVariableFeatures(nfeatures = 2000, verbose = FALSE) %>%
+  ScaleData(verbose = FALSE) %>% RunPCA(npcs = 30, verbose = FALSE) %>%
+  RunUMAP(dims = 1:30, verbose = FALSE)
+sc <- RenameCells(sc, new.names = make.names(Cells(sc)))
+cat('scRNA allen:', ncol(sc), 'cells |', length(unique(sc$cell_type)), 'cell types\n')
+
+## ---- Step 1: traint ----
+cat('\n==== traint (VisiumV2 ST) ====\n')
+tr <- CellTrek::traint(st_data = st, sc_data = sc, st_assay = 'Spatial',
+                       sc_assay = 'RNA', cell_names = 'cell_type')
+cat('traint:', ncol(tr), 'cells | coord_x set:',
+    all(!is.na(tr$coord_x[tr$type == 'st'])), '\n')
+
+## ---- Step 2: celltrek ----
+cat('\n==== celltrek ====\n')
+res <- CellTrek::celltrek(st_sc_int = tr, int_assay = 'traint', sc_data = sc,
+                          sc_assay = 'RNA', reduction = 'pca', intp = TRUE,
+                          intp_pnt = 5000, intp_lin = FALSE, nPCs = 30, ntree = 1000,
+                          dist_thresh = 0.55, top_spot = 5, spot_n = 5,
+                          repel_r = 20, repel_iter = 20, keep_model = FALSE)$celltrek
+cat('celltrek:', ncol(res), 'charted cells | coords in meta:',
+    all(c('coord_x','coord_y') %in% colnames(res@meta.data)), '\n')
+saveRDS(res, 'tests_local/dataset2_celltrek_cache.rds')
+
+## ---- Fig: spatial charting ----
+df <- res@meta.data
+df$cell_type <- factor(df$cell_type, levels = sort(unique(df$cell_type)))
+p <- ggplot(df, aes(coord_y, max(coord_x)+min(coord_x)-coord_x, color = cell_type)) +
+  geom_point(size = 0.6, alpha = 0.85) + coord_fixed() + theme_void() +
+  guides(color = guide_legend(override.aes = list(size = 3))) +
+  labs(title = paste0('Dataset 2 (stxBrain anterior + allen_cortex): ',
+                      nrow(df), ' cells charted'), color = 'subclass')
+ggsave('tests_local/figs/4_dataset2_spatial.png', p, width = 9, height = 7, dpi = 130)
+cat('spatial fig saved\n')
+
+## ---- Step 3: scoloc (DT, no external dep) ----
+cat('\n==== scoloc (DT) ====\n')
+sg <- CellTrek::scoloc(res, col_cell = 'cell_type', use_method = 'DT', boot_n = 10)
+cat('scoloc mst_cons:', paste(dim(sg$mst_cons), collapse = 'x'), '\n')
+
+cat('\n==== DATASET 2 ALL STAGES COMPLETED ====\n')
diff --git a/tests_local/run_pipeline.R b/tests_local/run_pipeline.R
new file mode 100644
index 0000000..d514d37
--- /dev/null
+++ b/tests_local/run_pipeline.R
@@ -0,0 +1,84 @@
+## Local functional test of CellTrek on Seurat v5
+## Runs the README workflow on the mouse brain data (subsampled for speed).
+suppressPackageStartupMessages({
+  library(CellTrek)
+  library(Seurat)
+  library(dplyr)
+  library(magrittr)
+})
+set.seed(1)
+cat('Seurat', as.character(packageVersion('Seurat')), '\n')
+
+data_dir <- 'data'
+brain_st <- readRDS(file.path(data_dir, 'brain_st_cortex.rds'))
+brain_sc <- readRDS(file.path(data_dir, 'brain_sc.rds'))
+
+## syntactically valid names
+brain_st <- RenameCells(brain_st, new.names = make.names(Cells(brain_st)))
+brain_sc <- RenameCells(brain_sc, new.names = make.names(Cells(brain_sc)))
+
+## subsample SC for speed, keeping every cell type
+set.seed(42)
+md <- brain_sc@meta.data
+keep <- unlist(lapply(split(rownames(md), md$cell_type), function(x) sample(x, min(length(x), 60))))
+brain_sc <- subset(brain_sc, cells = keep)
+cat('SC cells after subsample:', ncol(brain_sc), '| cell types:', length(unique(brain_sc$cell_type)), '\n')
+cat('ST spots:', ncol(brain_st), '\n')
+
+cache <- 'tests_local/brain_celltrek_cache.rds'
+if (file.exists(cache)) {
+  cat('\n==== loading cached celltrek object ====\n')
+  brain_celltrek <- readRDS(cache)
+} else {
+## ---- Step 1: traint (co-embedding) ----
+cat('\n==== traint ====\n')
+brain_traint <- CellTrek::traint(st_data = brain_st, sc_data = brain_sc,
+                                 sc_assay = 'RNA', cell_names = 'cell_type')
+cat('traint object:', ncol(brain_traint), 'cells |',
+    'reductions:', paste(names(brain_traint@reductions), collapse=','), '\n')
+stopifnot('pca' %in% names(brain_traint@reductions))
+
+## ---- Step 2: celltrek (charting) ----
+cat('\n==== celltrek ====\n')
+brain_celltrek <- CellTrek::celltrek(st_sc_int = brain_traint, int_assay = 'traint',
+                                     sc_data = brain_sc, sc_assay = 'RNA',
+                                     reduction = 'pca', intp = TRUE, intp_pnt = 5000,
+                                     intp_lin = FALSE, nPCs = 30, ntree = 1000,
+                                     dist_thresh = 0.55, top_spot = 5, spot_n = 5,
+                                     repel_r = 20, repel_iter = 20, keep_model = TRUE)$celltrek
+saveRDS(brain_celltrek, cache)
+}
+cat('celltrek object:', ncol(brain_celltrek), 'charted cells\n')
+cat('coord range x:', paste(round(range(brain_celltrek$coord_x)), collapse='-'),
+    '| y:', paste(round(range(brain_celltrek$coord_y)), collapse='-'), '\n')
+stopifnot(all(c('coord_x','coord_y') %in% colnames(brain_celltrek@meta.data)))
+stopifnot('celltrek' %in% names(brain_celltrek@reductions))
+
+## ---- Step 3: SColoc (KL) ----
+cat('\n==== scoloc (KL) ====\n')
+glut_cell <- c('L2/3 IT','L4','L5 IT','L5 PT','NP','L6 IT','L6 CT','L6b')
+glut_cell <- glut_cell[glut_cell %in% brain_celltrek$cell_type]
+brain_glut <- subset(brain_celltrek, subset = cell_type %in% glut_cell)
+cat('glut cells:', ncol(brain_glut), '\n')
+brain_sgraph_KL <- CellTrek::scoloc(brain_glut, col_cell = 'cell_type',
+                                    use_method = 'KL', eps = 1e-50, boot_n = 10)
+cat('\nscoloc mst_cons dim:', paste(dim(brain_sgraph_KL$mst_cons), collapse='x'), '\n')
+
+## ---- Step 4: SCoexp (cc) on L5 IT ----
+cat('\n==== scoexp (cc) ====\n')
+brain_l5 <- subset(brain_celltrek, subset = cell_type == 'L5 IT')
+cat('L5 IT cells:', ncol(brain_l5), '\n')
+if (ncol(brain_l5) >= 30) {
+  brain_l5 <- FindVariableFeatures(brain_l5, verbose = FALSE)
+  feats <- VariableFeatures(brain_l5)
+  feats <- head(feats, 300)
+  res_cc <- CellTrek::scoexp(celltrek_inp = brain_l5, assay = 'RNA', approach = 'cc',
+                             gene_select = feats, sigm = 140, avg_cor_min = .3,
+                             zero_cutoff = 3, min_gen = 10, max_gen = 200, maxK = 6, k = 6, reps = 10)
+  cat('scoexp modules found:', length(res_cc$gs), '\n')
+  cat('module sizes:', paste(sapply(res_cc$gs, length), collapse=','), '\n')
+} else {
+  cat('skip scoexp: too few L5 IT cells in subsample\n')
+}
+
+cat('\n==== ALL STAGES COMPLETED ====\n')
diff --git a/tests_local/test_coord_df_param.R b/tests_local/test_coord_df_param.R
new file mode 100644
index 0000000..0f6dec0
--- /dev/null
+++ b/tests_local/test_coord_df_param.R
@@ -0,0 +1,39 @@
+## Lightweight validation of the new traint(coord_df=) parameter on a normal,
+## fully-operable object (brain_st_cortex, VisiumV1). Small subsample for speed.
+suppressPackageStartupMessages({library(CellTrek); library(Seurat); library(dplyr)})
+set.seed(1)
+st <- readRDS('data/brain_st_cortex.rds')
+sc <- readRDS('data/brain_sc.rds')
+st <- RenameCells(st, new.names = make.names(Cells(st)))
+sc <- RenameCells(sc, new.names = make.names(Cells(sc)))
+## subsample for speed
+st <- st[, sample(Cells(st), 250)]
+set.seed(2); md <- sc@meta.data
+keep <- unlist(lapply(split(rownames(md), md$cell_type), function(x) sample(x, min(length(x), 25))))
+sc <- sc[, keep]
+
+## coord_df with NON-standard column names (col1 -> coord_x, col2 -> coord_y)
+tc <- GetTissueCoordinates(st)
+my_coord <- data.frame(rowpos = tc[, 'imagerow'], colpos = tc[, 'imagecol'])
+rownames(my_coord) <- Cells(st)
+
+cat('== Test 1: traint WITH coord_df (non-standard col names) ==\n')
+tr <- CellTrek::traint(st, sc, st_assay = 'Spatial', sc_assay = 'RNA',
+                       cell_names = 'cell_type', coord_df = my_coord)
+st_cells <- tr$id[tr$type == 'st']
+ok <- isTRUE(all.equal(unname(tr$coord_x[tr$type=='st']),
+                       unname(my_coord[st_cells, 1]), tolerance = 1e-6))
+cat('coord_x matches coord_df col1:', ok, '\n')
+cat('traint cells:', ncol(tr), '\n')
+
+cat('\n== Test 2: bad rownames -> expect graceful error ==\n')
+bad <- my_coord; rownames(bad) <- paste0('X', seq_len(nrow(bad)))
+r2 <- tryCatch({ CellTrek::traint(st, sc, sc_assay='RNA', cell_names='cell_type', coord_df=bad); 'NO ERROR (BAD)'},
+               error=function(e) paste0('ERRORED as expected: ', conditionMessage(e)))
+cat(r2, '\n')
+
+cat('\n== Test 3: auto-detect still works WITHOUT coord_df ==\n')
+tr2 <- CellTrek::traint(st, sc, st_assay='Spatial', sc_assay='RNA', cell_names='cell_type')
+cat('auto-detect coord_x set:', all(!is.na(tr2$coord_x[tr2$type=='st'])), '\n')
+
+cat('\n== COORD_DF PARAM TEST DONE ==\n')