Feature/minor release updates

docker/spatial_py/build.env

@@ -1,5 +1,5 @@
 # Tool versions
-SPATIAL_PY_VERSION=1.0.0
+SPATIAL_PY_VERSION=1.0.1
 GCLOUD_CLI_VERSION=524.0.0-slim
 PYTHON3_VERSION=3.12.5
 

docker/spatial_py/scripts/cluster

@@ -14,9 +14,16 @@ def main(args):
     ##########################
     adata = sc.read_h5ad(args.adata_input)
 
+    n_comps = args.n_comps
+    if adata.n_obs < n_comps or adata.n_vars < n_comps:
+        n_comps = min(n_comps, adata.n_obs - 1, adata.n_vars - 1)
+        print(f"[INFO] Detected small dimensions (n_obs={adata.n_obs}, n_vars={adata.n_vars}). Setting n_comps={n_comps}.")
+    else:
+        print(f"[INFO] Computing the nearest neighbors distance matrix with default settings (n={n_comps}).")
+
     sc.pp.neighbors(
         adata,
-        n_pcs=args.n_comps,
+        n_pcs=n_comps,
     )
     sc.tl.umap(adata)
 

docker/spatial_py/scripts/visium_plot_spatial

@@ -15,6 +15,13 @@ def main(args):
     colors = ["total_counts", "n_genes_by_counts", "batch", "leiden"]
     titles = [f"{slide} - {color}" for slide in slides for color in colors]
 
+    # Clean up
+    present_slides = adata.obs["visium_slide_ref"].unique()
+    all_slides = list(adata.uns["spatial"].keys())
+    for slide in all_slides:
+        if slide not in present_slides:
+            del adata.uns["spatial"][slide]
+
     sq.pl.spatial_scatter(
         adata,
         library_key="visium_slide_ref",

docker/spatial_py/scripts/visium_process

@@ -72,9 +72,17 @@ def main(args):
     ##############################
     ## DIMENSIONALITY REDUCTION ##
     ##############################
+    n_comps = args.n_comps
+    if hvg_adata.n_obs < n_comps or hvg_adata.n_vars < n_comps:
+        n_comps = min(n_comps, hvg_adata.n_obs - 1, hvg_adata.n_vars - 1)
+        print(f"[INFO] Detected small dimensions (n_obs={hvg_adata.n_obs}, n_vars={hvg_adata.n_vars}). Setting n_comps={n_comps}.")
+        print(f"[WARNING] PCA might not be meaningful since dataset is small (e.g., <30 cells) or check filters.")
+    else:
+        print(f"[INFO] Running PCA with default settings (n={n_comps}).")
+
     sc.pp.pca(
         hvg_adata,
-        n_comps=args.n_comps,
+        n_comps=n_comps,
         svd_solver="arpack",
     )
 

docker/spatial_py/scripts/visium_spatially_variable_genes

@@ -15,26 +15,42 @@ def main(args):
     adata = sc.read_h5ad(args.adata_input)
 
     genes = adata.var_names[adata.var["highly_variable"]].tolist()
+
+    # Check spots per slide
+    spots_per_slide = adata.obs["visium_slide_ref"].value_counts()
+    print("Spots per slide:")
+    print(spots_per_slide)
+    slides_to_keep = spots_per_slide[spots_per_slide >= 4].index.tolist()
+    adata_filtered = adata[adata.obs["visium_slide_ref"].isin(slides_to_keep)].copy()
+
     sq.gr.spatial_neighbors(
-        adata,
+        adata_filtered,
         library_key="visium_slide_ref",
         coord_type="generic",
         delaunay=True,
     )
     sq.gr.spatial_autocorr(
-        adata,
+        adata_filtered,
         mode="moran",
         genes=genes,
         n_perms=100,
         n_jobs=1,
     )
-    top_10_variable_genes = adata.uns["moranI"].head(10)
+    top_10_variable_genes = adata_filtered.uns["moranI"].head(10)
 
-    top_4_variable_gene_list = adata.uns["moranI"].head(4).index.tolist()
-    slides = adata.obs["visium_slide_ref"].unique().tolist()
+    top_4_variable_gene_list = adata_filtered.uns["moranI"].head(4).index.tolist()
+    slides = adata_filtered.obs["visium_slide_ref"].unique().tolist()
     titles = [f"{slide} - {color}" for slide in slides for color in top_4_variable_gene_list]
+
+    # Clean up
+    present_slides = adata_filtered.obs["visium_slide_ref"].unique()
+    all_slides = list(adata_filtered.uns["spatial"].keys())
+    for slide in all_slides:
+        if slide not in present_slides:
+            del adata_filtered.uns["spatial"][slide]
+
     sq.pl.spatial_scatter(
-        adata,
+        adata_filtered,
         library_key="visium_slide_ref",
         color=top_4_variable_gene_list,
         title=titles,
@@ -43,9 +59,9 @@ def main(args):
 
     # Save table and adata object
     top_10_variable_genes.to_csv(f"{args.cohort_id}.moran_top_10_variable_genes.csv")
-    metadata = adata.obs
+    metadata = adata_filtered.obs
     metadata.to_csv(f"{args.cohort_id}.final_metadata.csv")
-    adata.write_h5ad(filename=args.adata_output, compression="gzip")
+    adata_filtered.write_h5ad(filename=args.adata_output, compression="gzip")
 
 
 if __name__ == "__main__":

wdl-ci.config.json

@@ -262,7 +262,7 @@
       "tasks": {
         "integrate_sample_data": {
           "key": "integrate_sample_data",
-          "digest": "qjngtpgf2tzriftjt4hfwgo2i2z2uucg",
+          "digest": "hcdomqajbz6ntwc6xeqrsb65q4end4hn",
           "tests": [
             {
               "inputs": {
@@ -276,8 +276,7 @@
                 "integrated_adata_object": {
                   "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.harmony_integrated.h5ad",
                   "test_tasks": [
-                    "compare_file_basename",
-                    "check_hdf5"
+                    "compare_file_basename"
                   ]
                 }
               }
@@ -286,7 +285,7 @@
         },
         "cluster": {
           "key": "cluster",
-          "digest": "ibhtiot7sakpfyfuuvpxnqiqmsgzh4hj",
+          "digest": "frghkwauyalgin75nkrjs466t4vep5uk",
           "tests": [
             {
               "inputs": {
@@ -304,8 +303,7 @@
                 "clustered_adata_object": {
                   "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.clustered.h5ad",
                   "test_tasks": [
-                    "compare_file_basename",
-                    "check_hdf5"
+                    "compare_file_basename"
                   ]
                 },
                 "umap_cluster_plots_png": {
@@ -329,7 +327,7 @@
       "tasks": {
         "merge_and_plot_qc_metrics": {
           "key": "merge_and_plot_qc_metrics",
-          "digest": "tfku5xa5owqltkkjxr2ibq36iw5fh6hx",
+          "digest": "6e55pxc6kpkxuyqrthug5cy4xgpirqta",
           "tests": [
             {
               "inputs": {
@@ -369,7 +367,7 @@
         },
         "filter_and_normalize": {
           "key": "filter_and_normalize",
-          "digest": "526uk3dkuil6gmxfyxiltlyvegxbxpki",
+          "digest": "sfml3i63wsmbm4jtgvfjn32lrchcesaw",
           "tests": [
             {
               "inputs": {
@@ -429,7 +427,7 @@
         },
         "plot_spatial": {
           "key": "plot_spatial",
-          "digest": "ufcwh7kuftyt6nu74cyoawgjvro7lsfv",
+          "digest": "3pfo5jdbiyz42mvxy6geg4gd7ednbjy3",
           "tests": [
             {
               "inputs": {
@@ -463,7 +461,7 @@
       "tasks": {
         "spatially_variable_gene_analysis": {
           "key": "spatially_variable_gene_analysis",
-          "digest": "in5bndajrnrb4kupxqv7xydhlqpnmghy",
+          "digest": "ysgd246bcuttozfizskpva2frc2tnml2",
           "tests": [
             {
               "inputs": {
@@ -627,7 +625,7 @@
         },
         "counts_to_adata": {
           "key": "counts_to_adata",
-          "digest": "zh45hobarbnoa2z2b6d3i66fdyneqj3w",
+          "digest": "srukdp6rz3im6dzlcn365i36tv3422yp",
           "tests": [
             {
               "inputs": {
@@ -658,7 +656,7 @@
         },
         "qc": {
           "key": "qc",
-          "digest": "2jfnfowy32wp57i7ctsx7ni5pzzeadbi",
+          "digest": "s3lt4rtck6w3p7rc6dlav2wda4oz7f7l",
           "tests": [
             {
               "inputs": {

workflows/integrate_data/integrate_data.wdl

@@ -92,7 +92,7 @@ task integrate_sample_data {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: 4
 		memory: "~{mem_gb} GB"
 		disks: "local-disk ~{disk_size} HDD"
@@ -155,7 +155,7 @@ task cluster {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: 4
 		memory: "~{mem_gb} GB"
 		disks: "local-disk ~{disk_size} HDD"

workflows/spatial_visium/cohort_analysis/cohort_analysis.wdl

@@ -39,7 +39,7 @@ workflow cohort_analysis {
 	}
 
 	String sub_workflow_name = "cohort_analysis"
-	String sub_workflow_version = "1.0.0"
+	String sub_workflow_version = "1.0.1"
 
 	Array[Array[String]] workflow_info = [[run_timestamp, workflow_name, workflow_version, workflow_release]]
 
@@ -273,7 +273,7 @@ task merge_and_plot_qc_metrics {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: 2
 		memory: "~{mem_gb} GB"
 		disks: "local-disk ~{disk_size} HDD"
@@ -354,7 +354,7 @@ task filter_and_normalize {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: 4
 		memory: "~{mem_gb} GB"
 		disks: "local-disk ~{disk_size} HDD"
@@ -420,7 +420,7 @@ task plot_spatial {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: 2
 		memory: "~{mem_gb} GB"
 		disks: "local-disk ~{disk_size} HDD"

workflows/spatial_visium/cohort_analysis/spatial_statistics/spatial_statistics.wdl

@@ -89,7 +89,7 @@ task spatially_variable_gene_analysis {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: 2
 		memory: "~{mem_gb} GB"
 		disks: "local-disk ~{disk_size} HDD"

workflows/spatial_visium/main.wdl

@@ -31,7 +31,7 @@ workflow spatial_visium_analysis {
 
 	String workflow_execution_path = "workflow_execution"
 	String workflow_name = "spatial_visium"
-	String workflow_version = "v1.0.0"
+	String workflow_version = "v1.0.1"
 	String workflow_release = "https://github.com/ASAP-CRN/spatial-transcriptomics-wf/releases/tag/spatial_visium_analysis-~{workflow_version}"
 
 	call GetWorkflowMetadata.get_workflow_metadata {

workflows/spatial_visium/preprocess/preprocess.wdl

@@ -461,7 +461,7 @@ task counts_to_adata {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: 2
 		memory: "4 GB"
 		disks: "local-disk ~{disk_size} HDD"
@@ -526,7 +526,7 @@ task qc {
 	}
 
 	runtime {
-		docker: "~{container_registry}/spatial_py:1.0.0"
+		docker: "~{container_registry}/spatial_py:1.0.1"
 		cpu: threads
 		memory: "~{mem_gb} GB"
 		disks: "local-disk ~{disk_size} HDD"