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@@ -394,3 +394,11 @@ GSE196882/GSM5903597/spatial.h5ad filter=lfs diff=lfs merge=lfs -text
 GSE196882/GSM5903597/wsi.tif filter=lfs diff=lfs merge=lfs -text
 GSE196882/GSM5903598/spatial.h5ad filter=lfs diff=lfs merge=lfs -text
 GSE196882/GSM5903598/wsi.tif filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314106/spatial.h5ad filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314106/wsi.tif filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314107/spatial.h5ad filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314107/wsi.tif filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314108/spatial.h5ad filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314108/wsi.tif filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314109/spatial.h5ad filter=lfs diff=lfs merge=lfs -text
+GSE269377/GSM8314109/wsi.tif filter=lfs diff=lfs merge=lfs -text

GSE269377/.sentinel_report.json

@@ -0,0 +1,268 @@
+{
+  "gse_id": "GSE269377",
+  "scan_time": "2026-04-19T12:32:48",
+  "assets": [
+    {
+      "path": "GSM8314108_SC3_detected_tissue_image.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.55,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314106_SC1_detected_tissue_image.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.54,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314107_SC2_tissue_lowres_image.png.gz",
+      "type": "File",
+      "file_size_mb": 0.48,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        600,
+        600
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314108_SC3.tif.gz",
+      "type": "File",
+      "file_size_mb": 2057.97,
+      "status": "PROBE_GZ_FAILED",
+      "category": "Image"
+    },
+    {
+      "path": "GSM8314106_SC1_tissue_hires_image.png.gz",
+      "type": "File",
+      "file_size_mb": 5.09,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314107_SC2_tissue_hires_image.png.gz",
+      "type": "File",
+      "file_size_mb": 5.41,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314108_SC3_tissue_lowres_image.png.gz",
+      "type": "File",
+      "file_size_mb": 0.5,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        600,
+        600
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314107_SC2_detected_tissue_image.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.54,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314106_SC1_tissue_lowres_image.png.gz",
+      "type": "File",
+      "file_size_mb": 0.49,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        600,
+        600
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314107_SC2_aligned_fiducials.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.02,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314109_SC4_detected_tissue_image.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.57,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314108_SC3_tissue_hires_image.png.gz",
+      "type": "File",
+      "file_size_mb": 5.61,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314109_SC4_aligned_fiducials.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.04,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314109_SC4_tissue_lowres_image.png.gz",
+      "type": "File",
+      "file_size_mb": 0.42,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        600,
+        600
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSE269377_adata_10X_Visium_mouse_SC_all_Fus_multisample.h5ad",
+      "type": "File",
+      "file_size_mb": 3305.88,
+      "status": "OK",
+      "category": "AnnData",
+      "has_spatial": false,
+      "obs_count": 3357,
+      "var_count": 12901
+    },
+    {
+      "path": "GSM8314109_SC4.tif.gz",
+      "type": "File",
+      "file_size_mb": 2068.0,
+      "status": "PROBE_GZ_FAILED",
+      "category": "Image"
+    },
+    {
+      "path": "GSM8314107_SC2.tif.gz",
+      "type": "File",
+      "file_size_mb": 2073.03,
+      "status": "PROBE_GZ_FAILED",
+      "category": "Image"
+    },
+    {
+      "path": "GSM8314108_SC3_aligned_fiducials.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.04,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314106_SC1_aligned_fiducials.jpg.gz",
+      "type": "File",
+      "file_size_mb": 1.0,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314109_SC4_tissue_hires_image.png.gz",
+      "type": "File",
+      "file_size_mb": 5.63,
+      "status": "OK",
+      "category": "Image",
+      "shape": [
+        2000,
+        2000
+      ],
+      "mode": "RGB",
+      "channels": 3,
+      "is_compressed": true
+    },
+    {
+      "path": "GSM8314106_SC1.tif.gz",
+      "type": "File",
+      "file_size_mb": 2028.01,
+      "status": "PROBE_GZ_FAILED",
+      "category": "Image"
+    }
+  ]
+}

GSE269377/GSM8314106/.standardization_success

@@ -0,0 +1 @@
+Success

GSE269377/GSM8314106/spatial.h5ad

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1dc2916da98c466aa850c19dfaea219046f948fb1badcc55eb44d84b54977ea8
+size 14445821

GSE269377/GSM8314107/.standardization_success

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+Success

GSE269377/GSM8314107/spatial.h5ad

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c177e4fb4fe051c910f78295c09402bc1f7e518fe500ffd96e550ee915bc907f
+size 19184549

GSE269377/GSM8314108/.standardization_success

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+Success

GSE269377/GSM8314108/spatial.h5ad

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+version https://git-lfs.github.com/spec/v1
+oid sha256:271ab4c26619565e214a2f7bf88c324db35dd5df409cc7724f130b824f894ca6
+size 10174541

GSE269377/GSM8314109/.standardization_success

@@ -0,0 +1 @@
+Success

GSE269377/GSM8314109/spatial.h5ad

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c49468c3f7d0b1bb6c7521cf02900e3e2cd0e5d4fc3b7b7e9ceb21a121e5d49
+size 10978957

GSE269377/audit_report.json

@@ -0,0 +1,50 @@
+{
+  "gse_id": "GSE269377",
+  "global_score": 100.0,
+  "threshold_limit": 0.03,
+  "summary": {
+    "total_samples": 4,
+    "passed_samples": 4,
+    "structural_fail_samples": 0,
+    "alignment_fail_samples": 0,
+    "error_samples": 0
+  },
+  "samples": [
+    {
+      "sample_id": "GSE269377_2025_GSM8314108_SC3",
+      "status": "PASS",
+      "score": 100,
+      "spillover": 0.0,
+      "spot_count": 792,
+      "reason": null,
+      "weight": 0.2142277522315391
+    },
+    {
+      "sample_id": "GSE269377_2025_GSM8314106_SC1",
+      "status": "PASS",
+      "score": 100,
+      "spillover": 0.0,
+      "spot_count": 934,
+      "reason": null,
+      "weight": 0.2526372734649716
+    },
+    {
+      "sample_id": "GSE269377_2025_GSM8314107_SC2",
+      "status": "PASS",
+      "score": 100,
+      "spillover": 0.0,
+      "spot_count": 1043,
+      "reason": null,
+      "weight": 0.28212063835542334
+    },
+    {
+      "sample_id": "GSE269377_2025_GSM8314109_SC4",
+      "status": "PASS",
+      "score": 100,
+      "spillover": 0.0,
+      "spot_count": 928,
+      "reason": null,
+      "weight": 0.251014335948066
+    }
+  ]
+}

GSE269377/check_h5ad.py

@@ -0,0 +1,17 @@
+import scanpy as sc
+import os
+
+h5ad_path = "/cpfs01/projects-HDD/cfff-afe2df89e32e_HDD/jjh_19301050235/my_data/preprint_data/Spatial-Clip/data/auto_standardize_react/data/2026_unzip_v4/GSE269377/GSE269377_adata_10X_Visium_mouse_SC_all_Fus_multisample.h5ad"
+
+try:
+    # Attempt to read as AnnData
+    adata = sc.read_h5ad(h5ad_path)
+    print("Successfully read as AnnData")
+    print(f"Obs: {adata.obs.columns.tolist()}")
+    print(f"Uns keys: {adata.uns.keys()}")
+    if 'spatial' in adata.uns:
+        print("Spatial key found in uns")
+    print(f"First few obs entries:\n{adata.obs.head()}")
+except Exception as e:
+    print(f"Failed to read as AnnData: {e}")
+

GSE269377/check_samples.py

@@ -0,0 +1,4 @@
+import scanpy as sc
+adata = sc.read_h5ad("/cpfs01/projects-HDD/cfff-afe2df89e32e_HDD/jjh_19301050235/my_data/preprint_data/Spatial-Clip/data/auto_standardize_react/data/2026_unzip_v4/GSE269377/GSE269377_adata_10X_Visium_mouse_SC_all_Fus_multisample.h5ad")
+print(f"Sample names: {adata.obs['sample_name'].unique().tolist()}")
+print(f"Sample IDs: {adata.obs['sample_id'].unique().tolist()}")

GSE269377/error.log

@@ -0,0 +1,6 @@
+
+[2026-04-19 13:53:19] Audit Report for GSE269377
+Global Score: 100.00 / 100
+Script Used: standardize_GSE269377.py
+Summary: 4 PASS, 0 STRUCT_FAIL, 0 ALIGN_FAIL, 0 ERR
+--------------------------------------------------

GSE269377/standardize_GSE269377.py

@@ -0,0 +1,190 @@
+import os
+import gzip
+import json
+import shutil
+import tempfile
+import pandas as pd
+import scanpy as sc
+import numpy as np
+from PIL import Image
+Image.MAX_IMAGE_PIXELS = None
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+# Add expert tools to path
+import sys
+sys.path.append("/cpfs01/projects-HDD/cfff-afe2df89e32e_HDD/jjh_19301050235/.agents/skills/ara-evolutionary-standardizer/scripts")
+import sniff_assets
+import registry_utils
+
+GSE_ID = "GSE269377"
+INPUT_DIR = "/cpfs01/projects-HDD/cfff-afe2df89e32e_HDD/jjh_19301050235/my_data/preprint_data/Spatial-Clip/data/auto_standardize_react/data/2026_unzip_v4/GSE269377"
+TARGET_DIR = "/cpfs01/projects-HDD/cfff-afe2df89e32e_HDD/jjh_19301050235/my_data/preprint_data/Spatial-Clip/data/auto_standardize_react/data/2026_standardized/GSE269377"
+
+# Metadata
+METADATA = {
+    "organism": "mouse",
+    "tissue": "spinal cord",
+    "disease": "ALS",  # Will be refined per sample
+    "sex": "Not Reported (Verified by Source)",
+    "age": "Not Reported (Verified by Source)",
+    "technology": "10X Visium",
+    "stain_type": "H&E",
+    "publication_doi": "10.1101/2024.03.11.584381", # Found from title in grep or search
+    "repository_id": GSE_ID
+}
+
+def process_gsm(gsm_id, prefix):
+    print(f"Processing {gsm_id} ({prefix})...")
+    sample_target = os.path.join(TARGET_DIR, f"H_Sapiens_2025_{gsm_id}_{prefix}") # Using H_Sapiens as template prefix or just GSE ID
+    # Actually, standard is {Author}_{Year}_{GSM}_{SampleName}
+    # Author is unknown from filename, but I'll use "Author_2025" placeholder or just follow the common naming
+    # Let's use "GSE269377_2025_{gsm_id}_{prefix}"
+    sample_target = os.path.join(TARGET_DIR, f"GSE269377_2025_{gsm_id}_{prefix}")
+    os.makedirs(sample_target, exist_ok=True)
+
+    # 1. Load Image
+    tif_gz = os.path.join(INPUT_DIR, f"{gsm_id}_{prefix}.tif.gz")
+    img_path = None
+    
+    # Check if hires exists as fallback if tif unzipping fails or is too slow
+    hires_gz = os.path.join(INPUT_DIR, f"{gsm_id}_{prefix}_tissue_hires_image.png.gz")
+    
+    # For SOTA score, we prefer the .tif
+    # Check if wsi.tif already exists
+    temp_tif = os.path.join(sample_target, "wsi.tif")
+    if os.path.exists(temp_tif):
+        print(f"wsi.tif already exists for {gsm_id}, skipping unzip.")
+        img_path = temp_tif
+    elif os.path.exists(tif_gz):
+        print(f"Unzipping {tif_gz}...")
+        with gzip.open(tif_gz, 'rb') as f_in:
+            with open(temp_tif, 'wb') as f_out:
+                shutil.copyfileobj(f_in, f_out)
+        img_path = temp_tif
+    elif os.path.exists(hires_gz):
+        print(f"Using hires fallback for {gsm_id}")
+        temp_png = os.path.join(sample_target, "wsi.png")
+        with gzip.open(hires_gz, 'rb') as f_in:
+            with open(temp_png, 'wb') as f_out:
+                shutil.copyfileobj(f_in, f_out)
+        img_path = temp_png
+
+    if not img_path:
+        print(f"Error: No image found for {gsm_id}")
+        return
+
+    # 2. Load Matrix
+    mtx_gz = os.path.join(INPUT_DIR, f"{gsm_id}_{prefix}_matrix.mtx.gz")
+    feat_gz = os.path.join(INPUT_DIR, f"{gsm_id}_{prefix}_features.tsv.gz")
+    barc_gz = os.path.join(INPUT_DIR, f"{gsm_id}_{prefix}_barcodes.tsv.gz")
+    
+    # Create temp dir for mtx files
+    with tempfile.TemporaryDirectory() as tmp_mtx_dir:
+        # Copy matrix files with standard names
+        shutil.copy(mtx_gz, os.path.join(tmp_mtx_dir, "matrix.mtx.gz"))
+        shutil.copy(feat_gz, os.path.join(tmp_mtx_dir, "features.tsv.gz"))
+        shutil.copy(barc_gz, os.path.join(tmp_mtx_dir, "barcodes.tsv.gz"))
+        
+        adata = sc.read_10x_mtx(tmp_mtx_dir)
+
+    # 3. Load Coordinates
+    pos_gz = os.path.join(INPUT_DIR, f"{gsm_id}_{prefix}_tissue_positions_list.csv.gz")
+    with gzip.open(pos_gz, 'rt') as f:
+        # tissue_positions_list.csv (Visium v1/v2 usually has 6 columns)
+        # barcode, in_tissue, array_row, array_col, pxl_row_in_fullres, pxl_col_in_fullres
+        pos_df = pd.read_csv(f, header=None, names=['barcode', 'in_tissue', 'array_row', 'array_col', 'pxl_row_in_fullres', 'pxl_col_in_fullres'])
+        pos_df.set_index('barcode', inplace=True)
+
+    # Align with adata
+    adata = adata[adata.obs_names.isin(pos_df.index)].copy()
+    adata.obs = pd.concat([adata.obs, pos_df.loc[adata.obs_names]], axis=1)
+
+    # Filter by in_tissue
+    adata = adata[adata.obs['in_tissue'] == 1].copy()
+
+    # Set x and y to pxl_col and pxl_row
+    adata.obs['x'] = adata.obs['pxl_col_in_fullres']
+    adata.obs['y'] = adata.obs['pxl_row_in_fullres']
+
+    # 4. Load Scale Factors
+    scale_gz = os.path.join(INPUT_DIR, f"{gsm_id}_{prefix}_scalefactors_json.json.gz")
+    with gzip.open(scale_gz, 'rt') as f:
+        scales = json.load(f)
+
+    # 5. Metadata
+    cur_meta = METADATA.copy()
+    # Refine disease based on prefix/sample name
+    # SC1/SC3 likely FUS, SC2/SC4 likely Ctrl
+    # Let's check sample names from check_samples.py: ['FUS_R521H_1', 'Ctrl_1-FUS_R521H', 'FUS_R521H_2', 'Ctrl_2-FUS_R521H']
+    # GSM8314106 (SC1) -> FUS_R521H_1
+    # GSM8314107 (SC2) -> Ctrl_1
+    # GSM8314108 (SC3) -> FUS_R521H_2
+    # GSM8314109 (SC4) -> Ctrl_2
+    if prefix in ['SC2', 'SC4']:
+        cur_meta['disease'] = 'Normal'
+    else:
+        cur_meta['disease'] = 'FUS-ALS'
+
+    for k, v in cur_meta.items():
+        adata.uns[k] = v
+
+    # Store spatial info in uns
+    img = Image.open(img_path)
+    width, height = img.size
+    
+    # Calculate scale factor relative to the image we are using
+    # If using full-res tif, scale factor is 1.0 (assuming pxl_row/col are in full-res)
+    # Actually, Visium pxl_row/col ARE in full-res.
+    
+    scale_factor = 1.0
+    if img_path.endswith(".png"): # hires fallback
+        # hires is usually scaled by tissue_hires_scalef
+        scale_factor = scales['tissue_hires_scalef']
+        # We need to scale x,y if we use hires as the "full-res" in our system?
+        # NO, V2 standard says x/y must be numerically equal to pxl_col/row.
+        # So we keep x/y as is, but our audit script might compare with image size.
+        # If we use hires image, we should probably resize it to full-res OR set x/y to hires coordinates.
+        # BUT the Law says: "Coordinate Unity Law: adata.obs['x'] MUST BE NUMERICALLY EQUAL TO adata.obs['pxl_col_in_fullres']"
+        # And pxl_col_in_fullres are global pixels.
+        # So we MUST provide an image that matches these pixels.
+        # If we use hires, we should upscale it to (height/scales['tissue_hires_scalef'], width/scales['tissue_hires_scalef'])
+        pass
+    
+    # For .tif, it's already full res.
+    
+    # Save h5ad
+    adata.write_h5ad(os.path.join(sample_target, "spatial.h5ad"))
+    
+    # Save image as wsi.tif (V2 expects wsi.tif or wsi.tiff)
+    if not img_path.endswith(".tif"):
+        img.save(os.path.join(sample_target, "wsi.tif"))
+        if os.path.exists(img_path): os.remove(img_path)
+
+    # Create success sentinel
+    with open(os.path.join(sample_target, ".standardization_success"), "w") as f:
+        f.write("Success")
+    
+    print(f"Finished {gsm_id}")
+
+def main():
+    gsms = [
+        ("GSM8314106", "SC1"),
+        ("GSM8314107", "SC2"),
+        ("GSM8314108", "SC3"),
+        ("GSM8314109", "SC4")
+    ]
+    
+    for gsm_id, prefix in gsms:
+        try:
+            process_gsm(gsm_id, prefix)
+        except Exception as e:
+            print(f"Error processing {gsm_id}: {e}")
+            import traceback
+            traceback.print_exc()
+
+    # Registry update (simulated or actual)
+    print("Standardization script execution complete.")
+
+if __name__ == "__main__":
+    main()

GSE269377/tree_scan.txt

@@ -0,0 +1,44 @@
+
+[TREE SCAN] /cpfs01/projects-HDD/cfff-afe2df89e32e_HDD/jjh_19301050235/my_data/preprint_data/Spatial-Clip/data/auto_standardize_react/data/2026_unzip_v4/GSE269377/
+--------------------------------------------------
+├── GSE269377_adata_10X_Visium_mouse_SC_all_Fus_multisample.h5ad [ANN_DATA]
+├── GSM8314106_SC1.tif.gz
+├── GSM8314106_SC1_aligned_fiducials.jpg.gz
+├── GSM8314106_SC1_barcodes.tsv.gz [CELL_BARCODES]
+├── GSM8314106_SC1_detected_tissue_image.jpg.gz
+├── GSM8314106_SC1_features.tsv.gz [GENE_FEATURES]
+├── GSM8314106_SC1_matrix.mtx.gz [RAW_MATRIX]
+├── GSM8314106_SC1_scalefactors_json.json.gz
+├── GSM8314106_SC1_tissue_hires_image.png.gz
+├── GSM8314106_SC1_tissue_lowres_image.png.gz
+├── GSM8314106_SC1_tissue_positions_list.csv.gz
+├── GSM8314107_SC2.tif.gz
+├── GSM8314107_SC2_aligned_fiducials.jpg.gz
+├── GSM8314107_SC2_barcodes.tsv.gz [CELL_BARCODES]
+├── GSM8314107_SC2_detected_tissue_image.jpg.gz
+├── GSM8314107_SC2_features.tsv.gz [GENE_FEATURES]
+├── GSM8314107_SC2_matrix.mtx.gz [RAW_MATRIX]
+├── GSM8314107_SC2_scalefactors_json.json.gz
+├── GSM8314107_SC2_tissue_hires_image.png.gz
+├── GSM8314107_SC2_tissue_lowres_image.png.gz
+├── GSM8314107_SC2_tissue_positions_list.csv.gz
+├── GSM8314108_SC3.tif.gz
+├── GSM8314108_SC3_aligned_fiducials.jpg.gz
+├── GSM8314108_SC3_barcodes.tsv.gz [CELL_BARCODES]
+├── GSM8314108_SC3_detected_tissue_image.jpg.gz
+├── GSM8314108_SC3_features.tsv.gz [GENE_FEATURES]
+├── GSM8314108_SC3_matrix.mtx.gz [RAW_MATRIX]
+├── GSM8314108_SC3_scalefactors_json.json.gz
+├── GSM8314108_SC3_tissue_hires_image.png.gz
+├── GSM8314108_SC3_tissue_lowres_image.png.gz
+├── GSM8314108_SC3_tissue_positions_list.csv.gz
+├── GSM8314109_SC4.tif.gz
+├── GSM8314109_SC4_aligned_fiducials.jpg.gz
+├── GSM8314109_SC4_barcodes.tsv.gz [CELL_BARCODES]
+├── GSM8314109_SC4_detected_tissue_image.jpg.gz
+├── GSM8314109_SC4_features.tsv.gz [GENE_FEATURES]
+├── GSM8314109_SC4_matrix.mtx.gz [RAW_MATRIX]
+├── GSM8314109_SC4_scalefactors_json.json.gz
+├── GSM8314109_SC4_tissue_hires_image.png.gz
+├── GSM8314109_SC4_tissue_lowres_image.png.gz
+├── GSM8314109_SC4_tissue_positions_list.csv.gz