added breast cancer idc ilc

Author: pakiessling

data/xenium-ffpe-bc-idc/visium_hd_cancer_colon.yml

@@ -0,0 +1,10 @@
+channels:
+  - conda-forge
+dependencies:
+  - python=3.11.6
+  - scipy=1.11.4
+  - pip
+  - pip:
+    - spatialdata==0.1.2
+    - spatialdata-io==0.1.2
+    - pypdl==1.3.2

data/xenium-ffpe-bc-idc/xenium-ffpe-bc-idc.py

@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+
+# Made by Paul Kiessling pakiessling@ukaachen.de
+
+import argparse
+import json
+import os
+import shutil
+import tempfile
+
+import pandas as pd
+import scipy
+from pypdl import Downloader
+from spatialdata_io import xenium
+
+LINKS = {
+    "https://s3-us-west-2.amazonaws.com/10x.files/samples/xenium/1.0.2/Xenium_V1_FFPE_Human_Breast_IDC_With_Addon/Xenium_V1_FFPE_Human_Breast_IDC_With_Addon_outs.zip": "7d3374472092b320ee9b876cb56c520b",
+    "https://cf.10xgenomics.com/samples/xenium/1.0.2/Xenium_V1_FFPE_Human_Breast_ILC_With_Addon/Xenium_V1_FFPE_Human_Breast_ILC_With_Addon_outs.zip": "cf779754817893dc98ff4311df2db61e",
+    "https://zenodo.org/records/15411357/files/idc.csv": "219392e7c41587efaeb315d172fba7b0",
+    "https://zenodo.org/records/15411357/files/ilc.csv": "d1b150c706539f0f20d9df5496a08984",
+}
+
+
+def download_links(links, temp_dir):
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:47.0) Gecko/20100101 Firefox/47.0"
+    }
+    dl = Downloader(headers=headers)
+    for link, checksum in links.items():
+        print(f"Downloading {link}")
+        file = dl.start(
+            url=link,
+            file_path=temp_dir,
+            segments=10,
+            display=True,
+            multithread=True,
+            block=True,
+            retries=3,
+        )
+        if not file.validate_hash(checksum, "md5"):
+            raise ValueError(f"File {file} is corrupted")
+
+
+def process_files(temp_folder, out_path, annotation_path, sample_name):
+    print(f"Transferring {temp_folder} to {out_path}")
+    sdata = xenium(
+        temp_folder,
+        cells_boundaries=False,
+        nucleus_boundaries=False,
+        cells_as_circles=False,
+        cells_labels=False,
+        nucleus_labels=False,
+        transcripts=False,
+        morphology_mip=False,
+        morphology_focus=False,
+    )
+    sdata = sdata["table"]
+    annotation = pd.read_csv(annotation_path)
+    sdata.obs = sdata.obs.merge(
+        annotation, how="left", left_on="cell_id", right_on="cell"
+    )
+    sdata = sdata[sdata.obs["domain"].notna()].copy()
+    process_adata(sdata, out_path, sample_name)
+
+
+def process_adata(adata, out_path, sample_name):
+    complete_path = os.path.join(out_path, sample_name)
+    os.makedirs(complete_path, exist_ok=True)
+
+    # Observations
+    obs = adata.obs.copy()
+    obs["selected"] = "true"
+    obs.to_csv(f"{complete_path}/observations.tsv", sep="\t", index_label="")
+
+    # Features
+    vars = adata.var.copy()
+    vars["selected"] = "true"
+    vars.to_csv(f"{complete_path}/features.tsv", sep="\t", index_label="")
+
+    # Coordinates
+    coords = pd.DataFrame(adata.obsm["spatial"], columns=["x", "y"])
+    coords.index = adata.obs.index
+    coords.to_csv(f"{complete_path}/coordinates.tsv", sep="\t", index_label="")
+
+    # Matrix
+    scipy.io.mmwrite(f"{complete_path}/counts.mtx", adata.X)
+
+    # Write labels.tsv
+    labels = adata.obs["domain"]
+    labels.to_csv(f"{complete_path}/labels.tsv", sep="\t", index_label="")
+
+
+def write_json(out_path):
+    experiment = {
+        "technology": "Xenium",
+        "species": "human",
+        "is_3D": False,
+    }
+    with open(os.path.join(out_path, "experiment.json"), "w") as f:
+        json.dump(experiment, f)
+
+
+def write_table(out_path):
+    data = {
+        "patient": ["ILC", "IDC"],
+        "sample": ["ILC", "IDC"],
+        "position": [0, 0],
+        "replicate": [0, 0],
+        "directory": ["ILC", "IDC"],
+        "n_clusters": [6, 8],
+    }
+    df = pd.DataFrame(data)
+    df.to_csv(f"{out_path}/samples.tsv", sep="\t", index_label=False)
+
+
+def main():
+    # Set up command-line argument parser
+    parser = argparse.ArgumentParser(
+        description="Convert Xenium data to Spacehack format."
+    )
+
+    # Add arguments for output folder
+    parser.add_argument(
+        "-o", "--out_dir", help="Output directory to write files to.", required=True
+    )
+
+    # Parse the command-line arguments
+    args = parser.parse_args()
+
+    # Download and process
+    with tempfile.TemporaryDirectory() as temp_dir:  #
+        download_links(LINKS, temp_dir)
+        for file in os.listdir(temp_dir):
+            if file.endswith(".zip"):
+                sample_name = file.split(".")[0]
+                sample_path = os.path.join(temp_dir, sample_name)
+
+                shutil.unpack_archive(os.path.join(temp_dir, file), sample_path)
+
+                if sample_name == "Xenium_V1_FFPE_Human_Breast_IDC_With_Addon_outs":
+                    annotation_path = os.path.join(temp_dir, "idc.csv")
+                    sample_name_short = "IDC"
+
+                elif sample_name == "Xenium_V1_FFPE_Human_Breast_ILC_With_Addon_outs":
+                    annotation_path = os.path.join(temp_dir, "ilc.csv")
+                    sample_name_short = "ILC"
+                else:
+                    raise ValueError("Unknown sample found in downloaded files")
+
+                output_path = os.path.join(args.out_dir, sample_name_short)
+                process_files(
+                    sample_path, output_path, annotation_path, sample_name_short
+                )
+        write_json(args.out_dir)
+        write_table(args.out_dir)
+
+
+if __name__ == "__main__":
+    main()