{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "07f7dbb1-63c6-40e4-b0d9-6ec104aa0adc", "metadata": {}, "outputs": [], "source": [ "# !pip install zarr\n", "\n", "import os\n", "import json\n", "from pathlib import Path\n", "import glob\n", "import numpy as np\n", "import pandas as pd\n", "import scanpy as sc\n", "import pyvips\n", "import zarr\n", "import geopandas as gpd\n", "from shapely.geometry import Polygon\n", "from scipy.sparse import csc_matrix\n", "\n", "import tissuumaps.jupyter as tj\n", "from tissuumaps import read_h5ad\n", "\n", "\n", "# ----------------------------\n", "# Paths\n", "# ----------------------------\n", "# sample = \"WTA_Preview_FFPE_Cervical_Cancer_outs\"\n", "# sample = \"Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs\"\n", "# sample = \"Xenium_Prime_Ovarian_Cancer_FFPE_XRrun_outs\"\n", "# sample = \"Xenium_V1_humanLung_Cancer_FFPE_outs\"\n", "\n", "xenium_dir = os.path.abspath(f\"../data/instrument_data/{sample}\")\n", "basedir = os.path.abspath(f\"../data/processed_data/tissuumaps_h5ad/{sample}\")\n", "os.makedirs(basedir, exist_ok=True)\n", "\n", "out_h5ad_name = f\"{sample}_tmap.h5ad\"\n", "out_h5ad = os.path.join(basedir, out_h5ad_name)\n", "\n", "project_path = os.path.join(basedir, \"_project_h5ad.tmap\")" ] }, { "cell_type": "code", "execution_count": null, "id": "aec44404-259a-4042-9230-5a244ec9df0e", "metadata": {}, "outputs": [], "source": [ "# ----------------------------\n", "# Transform helpers\n", "# ----------------------------\n", "def write_xenium_transform(data_dir, path_landscape_files):\n", " cells_zarr_path = Path(data_dir) / \"cells.zarr.zip\"\n", " if not cells_zarr_path.exists():\n", " raise FileNotFoundError(f\"Missing: {cells_zarr_path}\")\n", "\n", " store = zarr.ZipStore(str(cells_zarr_path), mode=\"r\")\n", " root = zarr.group(store=store)\n", "\n", " transform = root[\"masks\"][\"homogeneous_transform\"][:]\n", "\n", " pd.DataFrame(transform[:3, :3]).to_csv(\n", " Path(path_landscape_files) / \"micron_to_image_transform.csv\",\n", " sep=\" \",\n", " header=False,\n", " index=False,\n", " )\n", "\n", " return transform\n", "\n", "\n", "def apply_homogeneous_transform_xy(x, y, transform):\n", " transform = np.asarray(transform)[:3, :3]\n", "\n", " xy1 = np.vstack([\n", " np.asarray(x, dtype=float),\n", " np.asarray(y, dtype=float),\n", " np.ones(len(x)),\n", " ])\n", "\n", " out = transform @ xy1\n", " return out[0] / out[2], out[1] / out[2]\n", " \n", "# ----------------------------\n", "# 1. Transform\n", "# ----------------------------\n", "transform = write_xenium_transform(xenium_dir, basedir)" ] }, { "cell_type": "code", "execution_count": null, "id": "16f90ba5-914a-4a3d-b3bc-9960481cef1f", "metadata": {}, "outputs": [], "source": [ "# ----------------------------\n", "# Polygon helper\n", "# ----------------------------\n", "def xenium_boundaries_to_geojson(\n", " boundary_path,\n", " out_geojson,\n", " transform,\n", " id_col=\"cell_id\",\n", " max_polygons=None,\n", "):\n", " if not os.path.exists(boundary_path):\n", " return None\n", "\n", " if boundary_path.endswith(\".parquet\"):\n", " df = pd.read_parquet(boundary_path)\n", " else:\n", " df = pd.read_csv(boundary_path)\n", "\n", " x_col = \"vertex_x\" if \"vertex_x\" in df.columns else \"x\"\n", " y_col = \"vertex_y\" if \"vertex_y\" in df.columns else \"y\"\n", "\n", " x_new, y_new = apply_homogeneous_transform_xy(\n", " df[x_col].values,\n", " df[y_col].values,\n", " transform,\n", " )\n", "\n", " df = df.copy()\n", " df[\"x_img\"] = x_new\n", " df[\"y_img\"] = y_new\n", "\n", " ids = []\n", " geoms = []\n", "\n", " for i, (cid, sub) in enumerate(df.groupby(id_col, sort=False)):\n", " if max_polygons is not None and i >= max_polygons:\n", " break\n", "\n", " if len(sub) < 3:\n", " continue\n", "\n", " coords = list(zip(sub[\"x_img\"].astype(float), sub[\"y_img\"].astype(float)))\n", "\n", " if coords[0] != coords[-1]:\n", " coords.append(coords[0])\n", "\n", " poly = Polygon(coords)\n", "\n", " if poly.is_valid and not poly.is_empty:\n", " ids.append(cid)\n", " geoms.append(poly)\n", "\n", " gdf = gpd.GeoDataFrame({id_col: ids}, geometry=geoms, crs=None)\n", " gdf.to_file(out_geojson, driver=\"GeoJSON\")\n", "\n", " return os.path.basename(out_geojson)" ] }, { "cell_type": "code", "execution_count": null, "id": "a97826c2-0864-4a35-a7d0-5a64c117a1e2", "metadata": {}, "outputs": [], "source": [ "def add_xenium_default_clustering(adata, xenium_dir):\n", " import os\n", " import glob\n", " import pandas as pd\n", "\n", " candidates = sorted(glob.glob(\n", " os.path.join(xenium_dir, \"analysis\", \"clustering\", \"**\", \"clusters.csv\"),\n", " recursive=True,\n", " ))\n", "\n", " if not candidates:\n", " print(\"No clusters.csv found.\")\n", " return adata\n", "\n", " preferred = None\n", " for p in candidates:\n", " if \"gene_expression_graphclust\" in p:\n", " preferred = p\n", " break\n", "\n", " if preferred is None:\n", " preferred = candidates[0]\n", "\n", " print(\"Using clustering file:\", preferred)\n", "\n", " clusters = pd.read_csv(preferred)\n", "\n", " id_col = \"Barcode\" if \"Barcode\" in clusters.columns else clusters.columns[0]\n", " cluster_col = \"Cluster\" if \"Cluster\" in clusters.columns else clusters.columns[1]\n", "\n", " clusters[id_col] = clusters[id_col].astype(str)\n", " clusters[cluster_col] = clusters[cluster_col].astype(str)\n", "\n", " s = clusters.set_index(id_col)[cluster_col]\n", "\n", " # IMPORTANT: reindex allows cells missing from clustering file\n", " adata.obs[\"xenium_default_cluster\"] = (\n", " s.reindex(adata.obs_names)\n", " .fillna(\"unclustered\")\n", " .astype(str)\n", " .astype(\"category\")\n", " )\n", "\n", " print(adata.obs[\"xenium_default_cluster\"].value_counts())\n", "\n", " return adata\n", "\n", "\n", "# ----------------------------\n", "# 2. Build h5ad in image pixel space\n", "# ----------------------------\n", "adata = sc.read_10x_h5(os.path.join(xenium_dir, \"cell_feature_matrix.h5\"))\n", "adata.var_names_make_unique()\n", "\n", "cells = pd.read_csv(os.path.join(xenium_dir, \"cells.csv.gz\"), index_col=0)\n", "cells = cells.loc[adata.obs_names]\n", "\n", "x_img, y_img = apply_homogeneous_transform_xy(\n", " cells[\"x_centroid\"].values,\n", " cells[\"y_centroid\"].values,\n", " transform,\n", ")\n", "\n", "adata.obs[\"x\"] = x_img.astype(float)\n", "adata.obs[\"y\"] = y_img.astype(float)\n", "adata.obsm[\"spatial\"] = adata.obs[[\"x\", \"y\"]].to_numpy(dtype=\"float64\")\n", "\n", "adata.obs[\"x_centroid_um\"] = cells[\"x_centroid\"].astype(float).values\n", "adata.obs[\"y_centroid_um\"] = cells[\"y_centroid\"].astype(float).values\n", "\n", "obs_cols = [\n", " \"transcript_counts\",\n", " \"control_probe_counts\",\n", " \"genomic_control_counts\",\n", " \"control_codeword_counts\",\n", " \"unassigned_codeword_counts\",\n", " \"deprecated_codeword_counts\",\n", " \"total_counts\",\n", " \"cell_area\",\n", " \"nucleus_area\",\n", " \"nucleus_count\",\n", "]\n", "\n", "for col in obs_cols:\n", " if col in cells.columns:\n", " adata.obs[col] = cells[col].values\n", "\n", "for col in adata.obs.columns:\n", " if pd.api.types.is_object_dtype(adata.obs[col]):\n", " adata.obs[col] = adata.obs[col].astype(\"category\")\n", "\n", "adata = add_xenium_default_clustering(adata, xenium_dir)\n", "\n", "adata.X = csc_matrix(adata.X)\n", "adata.uns.pop(\"tmap_obsgroups\", None)\n", "adata.write_h5ad(out_h5ad)" ] }, { "cell_type": "code", "execution_count": null, "id": "f50d1a11-11ae-40f2-a35a-d0cd24a69935", "metadata": {}, "outputs": [], "source": [ "# ----------------------------\n", "# 3. Make image pyramid\n", "# ----------------------------\n", "def make_one_xenium_pyramid_tifffile_zarr(\n", " xenium_dir,\n", " basedir,\n", " channel=\"morphology_focus_0000.ome.tif\",\n", " plane=0,\n", " low=0,\n", " high=12000,\n", " gamma=0.5,\n", " out_name=None,\n", " rows_per_chunk=512,\n", "):\n", " import os\n", " import numpy as np\n", " import tifffile\n", " import zarr\n", " import pyvips\n", "\n", " src = os.path.join(xenium_dir, \"morphology_focus\", channel)\n", "\n", " if out_name is None:\n", " out_name = channel.replace(\".ome.tif\", f\"_plane{plane}_pyramid.tif\")\n", "\n", " tmp_name = out_name.replace(\".tif\", \"_uint8_tmp.tif\")\n", " tmp_path = os.path.join(basedir, tmp_name)\n", " out_path = os.path.join(basedir, out_name)\n", "\n", " for p in [tmp_path, out_path]:\n", " if os.path.exists(p):\n", " os.remove(p)\n", "\n", " with tifffile.TiffFile(src) as tf:\n", " store = tf.aszarr(series=0, level=0)\n", " z = zarr.open(store, mode=\"r\")\n", "\n", " print(\"zarr shape:\", z.shape, \"dtype:\", z.dtype)\n", "\n", " if len(z.shape) == 3:\n", " arr2d = z[plane]\n", " elif len(z.shape) == 2:\n", " arr2d = z\n", " else:\n", " raise ValueError(f\"Unexpected image shape: {z.shape}\")\n", "\n", " height, width = arr2d.shape\n", " print(\"using plane:\", plane, \"height:\", height, \"width:\", width)\n", "\n", " # Create one full-size uint8 temp TIFF, memory-mapped on disk\n", " tmp_mm = tifffile.memmap(\n", " tmp_path,\n", " shape=(height, width),\n", " dtype=\"uint8\",\n", " photometric=\"minisblack\",\n", " bigtiff=True,\n", " )\n", "\n", " for y0 in range(0, height, rows_per_chunk):\n", " y1 = min(y0 + rows_per_chunk, height)\n", "\n", " block = arr2d[y0:y1, :].astype(\"float32\")\n", " block = (block - low) / (high - low)\n", " block = np.clip(block, 0, 1)\n", "\n", " if gamma is not None:\n", " block = block ** gamma\n", "\n", " tmp_mm[y0:y1, :] = (block * 255).astype(\"uint8\")\n", "\n", " tmp_mm.flush()\n", " del tmp_mm\n", " store.close()\n", "\n", " img = pyvips.Image.new_from_file(tmp_path, access=\"sequential\")\n", "\n", " img.tiffsave(\n", " out_path,\n", " tile=True,\n", " pyramid=True,\n", " compression=\"jpeg\",\n", " Q=90,\n", " tile_width=256,\n", " tile_height=256,\n", " bigtiff=True,\n", " )\n", "\n", " os.remove(tmp_path)\n", "\n", " print(\"wrote:\", out_path)\n", "\n", " return {\n", " \"name\": out_name,\n", " \"tileSource\": out_name + \".dzi\",\n", " \"x\": 0,\n", " \"y\": 0,\n", " \"scale\": 1,\n", " \"rotation\": 0,\n", " \"flip\": False,\n", " }\n", "\n", "morph_files = sorted(glob.glob(os.path.join(xenium_dir, \"morphology_focus\", \"*.ome.tif\")))\n", "morph_files" ] }, { "cell_type": "code", "execution_count": null, "id": "4109411c-f39c-40d8-94f1-72be456f8c4d", "metadata": {}, "outputs": [], "source": [ "# # ----------------------------\n", "# # 3. Make image pyramid\n", "# # ----------------------------\n", "# def make_one_xenium_pyramid_tifffile_zarr(\n", "# xenium_dir,\n", "# basedir,\n", "# channel=\"morphology_focus_0000.ome.tif\",\n", "# plane=0,\n", "# low=0,\n", "# high=12000,\n", "# gamma=0.5,\n", "# out_name=None,\n", "# rows_per_chunk=512,\n", "# ):\n", "# import os\n", "# import numpy as np\n", "# import tifffile\n", "# import zarr\n", "# import pyvips\n", "\n", "# src = os.path.join(xenium_dir, channel)\n", "\n", "# if out_name is None:\n", "# out_name = channel.replace(\".ome.tif\", f\"_plane{plane}_pyramid.tif\")\n", "\n", "# tmp_name = out_name.replace(\".tif\", \"_uint8_tmp.tif\")\n", "# tmp_path = os.path.join(basedir, tmp_name)\n", "# out_path = os.path.join(basedir, out_name)\n", "\n", "# for p in [tmp_path, out_path]:\n", "# if os.path.exists(p):\n", "# os.remove(p)\n", "\n", "# with tifffile.TiffFile(src) as tf:\n", "# store = tf.aszarr(series=0, level=0)\n", "# z = zarr.open(store, mode=\"r\")\n", "\n", "# print(\"zarr shape:\", z.shape, \"dtype:\", z.dtype)\n", "\n", "# if len(z.shape) == 3:\n", "# arr2d = z[plane]\n", "# elif len(z.shape) == 2:\n", "# arr2d = z\n", "# else:\n", "# raise ValueError(f\"Unexpected image shape: {z.shape}\")\n", "\n", "# height, width = arr2d.shape\n", "# print(\"using plane:\", plane, \"height:\", height, \"width:\", width)\n", "\n", "# # Create one full-size uint8 temp TIFF, memory-mapped on disk\n", "# tmp_mm = tifffile.memmap(\n", "# tmp_path,\n", "# shape=(height, width),\n", "# dtype=\"uint8\",\n", "# photometric=\"minisblack\",\n", "# bigtiff=True,\n", "# )\n", "\n", "# for y0 in range(0, height, rows_per_chunk):\n", "# y1 = min(y0 + rows_per_chunk, height)\n", "\n", "# block = arr2d[y0:y1, :].astype(\"float32\")\n", "# block = (block - low) / (high - low)\n", "# block = np.clip(block, 0, 1)\n", "\n", "# if gamma is not None:\n", "# block = block ** gamma\n", "\n", "# tmp_mm[y0:y1, :] = (block * 255).astype(\"uint8\")\n", "\n", "# tmp_mm.flush()\n", "# del tmp_mm\n", "# store.close()\n", "\n", "# img = pyvips.Image.new_from_file(tmp_path, access=\"sequential\")\n", "\n", "# img.tiffsave(\n", "# out_path,\n", "# tile=True,\n", "# pyramid=True,\n", "# compression=\"jpeg\",\n", "# Q=90,\n", "# tile_width=256,\n", "# tile_height=256,\n", "# bigtiff=True,\n", "# )\n", "\n", "# os.remove(tmp_path)\n", "\n", "# print(\"wrote:\", out_path)\n", "\n", "# return {\n", "# \"name\": out_name,\n", "# \"tileSource\": out_name + \".dzi\",\n", "# \"x\": 0,\n", "# \"y\": 0,\n", "# \"scale\": 1,\n", "# \"rotation\": 0,\n", "# \"flip\": False,\n", "# }\n", " \n", "# morph_files = sorted(glob.glob(os.path.join(xenium_dir, \"morphology_focus.ome.tif\")))\n", "# morph_files" ] }, { "cell_type": "code", "execution_count": null, "id": "622a8749-399c-4bd4-855c-10be911d0198", "metadata": { "scrolled": true }, "outputs": [], "source": [ "image_layers = []\n", "\n", "# Use the first OME file, but extract each plane as a separate stain/channel\n", "channel_file = os.path.basename(morph_files[0])\n", "\n", "for plane in range(4):\n", " image_layer = make_one_xenium_pyramid_tifffile_zarr(\n", " xenium_dir=xenium_dir,\n", " basedir=basedir,\n", " channel=channel_file,\n", " plane=plane,\n", " low=0,\n", " high=3000,\n", " gamma=0.4,\n", " out_name=f\"morphology_focus_plane{plane}_pyramid.tif\",\n", " )\n", "\n", " image_layer[\"name\"] = f\"morphology_focus_plane{plane}\"\n", " image_layers.append(image_layer)" ] }, { "cell_type": "code", "execution_count": null, "id": "784a65f6-f500-4b7f-9ca6-5c9a3be4361e", "metadata": {}, "outputs": [], "source": [ "# ----------------------------\n", "# 4. Create cell boundary GeoJSON\n", "# ----------------------------\n", "region_files = []\n", "\n", "for candidate in [\n", " os.path.join(xenium_dir, \"cell_boundaries.parquet\"),\n", " os.path.join(xenium_dir, \"cell_boundaries.csv.gz\"),\n", "]:\n", " if os.path.exists(candidate):\n", " name = xenium_boundaries_to_geojson(\n", " candidate,\n", " os.path.join(basedir, \"cell_boundaries_image_space.geojson\"),\n", " transform,\n", " id_col=\"cell_id\",\n", " )\n", " region_files.append({\n", " \"path\": name,\n", " \"title\": \"Load cell boundaries\",\n", " \"comment\": \"Cell boundaries\",\n", " \"autoLoad\": False,\n", " })\n", " break" ] }, { "cell_type": "code", "execution_count": null, "id": "43159e9d-5279-4b90-a5b8-cc39aa08facd", "metadata": {}, "outputs": [], "source": [ "# def xenium_transcripts_to_csv_streaming(\n", "# xenium_dir,\n", "# basedir,\n", "# transform,\n", "# out_name=\"transcripts_image_space.csv\",\n", "# min_qv=20,\n", "# max_transcripts=2_000_000,\n", "# rows_per_group=None,\n", "# ):\n", "# import os\n", "# import numpy as np\n", "# import pandas as pd\n", "# import pyarrow.parquet as pq\n", "\n", "# transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n", "# if not os.path.exists(transcript_path):\n", "# raise FileNotFoundError(transcript_path)\n", "\n", "# out_path = os.path.join(basedir, out_name)\n", "# if os.path.exists(out_path):\n", "# os.remove(out_path)\n", "\n", "# pf = pq.ParquetFile(transcript_path)\n", "\n", "# transform3 = np.asarray(transform)[:3, :3]\n", "\n", "# written = 0\n", "# wrote_header = False\n", "\n", "# needed_cols = [\"x_location\", \"y_location\", \"feature_name\"]\n", "# optional_cols = [\"qv\", \"cell_id\"]\n", "\n", "# # keep only columns that exist\n", "# schema_cols = set(pf.schema.names)\n", "# cols = [c for c in needed_cols + optional_cols if c in schema_cols]\n", "\n", "# print(\"Transcript columns:\", cols)\n", "# print(\"Row groups:\", pf.num_row_groups)\n", "\n", "# for rg in range(pf.num_row_groups):\n", "# if max_transcripts is not None and written >= max_transcripts:\n", "# break\n", "\n", "# table = pf.read_row_group(rg, columns=cols)\n", "# tx = table.to_pandas()\n", "\n", "# if \"qv\" in tx.columns:\n", "# tx = tx[tx[\"qv\"] >= min_qv]\n", "\n", "# if tx.empty:\n", "# continue\n", "\n", "# if max_transcripts is not None:\n", "# remaining = max_transcripts - written\n", "# if len(tx) > remaining:\n", "# tx = tx.sample(remaining, random_state=rg)\n", "\n", "# x = tx[\"x_location\"].to_numpy(dtype=float)\n", "# y = tx[\"y_location\"].to_numpy(dtype=float)\n", "\n", "# xy1 = np.vstack([x, y, np.ones(len(x))])\n", "# out = transform3 @ xy1\n", "\n", "# out_df = pd.DataFrame({\n", "# \"x\": out[0] / out[2],\n", "# \"y\": out[1] / out[2],\n", "# \"gene\": tx[\"feature_name\"].astype(str).to_numpy(),\n", "# })\n", "\n", "# if \"qv\" in tx.columns:\n", "# out_df[\"qv\"] = tx[\"qv\"].to_numpy()\n", "\n", "# if \"cell_id\" in tx.columns:\n", "# out_df[\"cell_id\"] = tx[\"cell_id\"].astype(str).to_numpy()\n", "\n", "# out_df.to_csv(\n", "# out_path,\n", "# mode=\"a\",\n", "# header=not wrote_header,\n", "# index=False,\n", "# )\n", "\n", "# wrote_header = True\n", "# written += len(out_df)\n", "\n", "# print(f\"row group {rg + 1}/{pf.num_row_groups}: wrote {written:,}\")\n", "\n", "# print(\"wrote:\", out_path, \"n=\", written)\n", "# return out_name\n", "\n", "# transcript_csv = xenium_transcripts_to_csv_streaming(\n", "# xenium_dir=xenium_dir,\n", "# basedir=basedir,\n", "# transform=transform,\n", "# min_qv=20,\n", "# max_transcripts=None,\n", "# )\n", "\n", "# print(\"Done\")" ] }, { "cell_type": "code", "execution_count": null, "id": "b5398cfe-3a3d-4841-b385-f1467f20e45f", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# def xenium_all_transcripts_to_h5ad_empty_X(\n", "# xenium_dir,\n", "# basedir,\n", "# transform,\n", "# out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n", "# min_qv=None,\n", "# include_cell_id=False,\n", "# ):\n", "# import os\n", "# import numpy as np\n", "# import pandas as pd\n", "# import pyarrow.parquet as pq\n", "# import anndata as ad\n", "# from scipy.sparse import csc_matrix\n", "\n", "# transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n", "# out_path = os.path.join(basedir, out_name)\n", "\n", "# pf = pq.ParquetFile(transcript_path)\n", "# transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n", "\n", "# obs_chunks = []\n", "# spatial_chunks = []\n", "# written = 0\n", "\n", "# schema_cols = set(pf.schema.names)\n", "\n", "# cols = [\"x_location\", \"y_location\", \"feature_name\"]\n", "# if min_qv is not None and \"qv\" in schema_cols:\n", "# cols.append(\"qv\")\n", "# elif \"qv\" in schema_cols:\n", "# cols.append(\"qv\")\n", "\n", "# if include_cell_id and \"cell_id\" in schema_cols:\n", "# cols.append(\"cell_id\")\n", "\n", "# for rg in range(pf.num_row_groups):\n", "# tx = pf.read_row_group(rg, columns=cols).to_pandas()\n", "\n", "# if min_qv is not None and \"qv\" in tx.columns:\n", "# tx = tx[tx[\"qv\"] >= min_qv]\n", "\n", "# if tx.empty:\n", "# continue\n", "\n", "# x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n", "# y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n", "\n", "# xy1 = np.vstack([\n", "# x,\n", "# y,\n", "# np.ones(len(tx), dtype=np.float64),\n", "# ])\n", "\n", "# out = transform3 @ xy1\n", "\n", "# spatial_chunks.append(\n", "# np.column_stack([\n", "# out[0] / out[2],\n", "# out[1] / out[2],\n", "# ]).astype(np.float32)\n", "# )\n", "\n", "# # Compact index: RangeIndex, no huge tx_... strings\n", "# obs = pd.DataFrame(index=pd.RangeIndex(written, written + len(tx)))\n", "\n", "# obs[\"gene\"] = tx[\"feature_name\"].astype(\"category\").values\n", "\n", "# if \"qv\" in tx.columns:\n", "# obs[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\")\n", "\n", "# if include_cell_id and \"cell_id\" in tx.columns:\n", "# obs[\"cell_id\"] = tx[\"cell_id\"].astype(\"category\").values\n", "\n", "# obs_chunks.append(obs)\n", "\n", "# written += len(tx)\n", "# print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n", "\n", "# obs = pd.concat(obs_chunks, axis=0)\n", "\n", "# # Ensure compact categoricals after concat\n", "# obs[\"gene\"] = obs[\"gene\"].astype(\"category\")\n", "\n", "# if include_cell_id and \"cell_id\" in obs.columns:\n", "# obs[\"cell_id\"] = obs[\"cell_id\"].astype(\"category\")\n", "\n", "# if \"qv\" in obs.columns:\n", "# obs[\"qv\"] = pd.to_numeric(obs[\"qv\"], downcast=\"integer\")\n", "\n", "# spatial = np.vstack(spatial_chunks).astype(np.float32)\n", "\n", "# # Empty X: n_obs x 0 vars\n", "# X = csc_matrix((len(obs), 0), dtype=np.float32)\n", "# var = pd.DataFrame(index=pd.Index([], dtype=str))\n", "\n", "# transcript_adata = ad.AnnData(X=X, obs=obs, var=var)\n", "# transcript_adata.obsm[\"spatial\"] = spatial\n", "\n", "# transcript_adata.write_h5ad(out_path)\n", "\n", "# print(\"wrote:\", out_path)\n", "# print(\"shape:\", transcript_adata.shape)\n", "# print(\"obs columns:\", list(transcript_adata.obs.columns))\n", "# print(\"spatial dtype:\", transcript_adata.obsm[\"spatial\"].dtype)\n", "\n", "# return out_name\n", "\n", "# transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n", "# xenium_dir=xenium_dir,\n", "# basedir=basedir,\n", "# transform=transform,\n", "# min_qv=None,\n", "# include_cell_id=False,\n", "# )" ] }, { "cell_type": "code", "execution_count": null, "id": "9423d36b-5e04-484c-8901-b32a494ce7ad", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# def xenium_all_transcripts_to_h5ad_empty_X(\n", "# xenium_dir,\n", "# basedir,\n", "# transform,\n", "# out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n", "# min_qv=None,\n", "# include_cell_id=False,\n", "# tmp_path=None,\n", "# ):\n", "# import os\n", "# import numpy as np\n", "# import pandas as pd\n", "# import pyarrow as pa\n", "# import pyarrow.parquet as pq\n", "# import anndata as ad\n", "# from scipy.sparse import csc_matrix\n", "\n", "# transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n", "# out_path = os.path.join(basedir, out_name)\n", "# if tmp_path is None:\n", "# tmp_path = out_path.replace(\".h5ad\", \"_tmp.parquet\")\n", "\n", "# pf = pq.ParquetFile(transcript_path)\n", "# transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n", "\n", "# schema_cols = set(pf.schema.names)\n", "# cols = [\"x_location\", \"y_location\", \"feature_name\"]\n", "# if \"qv\" in schema_cols:\n", "# cols.append(\"qv\")\n", "# if include_cell_id and \"cell_id\" in schema_cols:\n", "# cols.append(\"cell_id\")\n", "\n", "# writer = None\n", "# written = 0\n", "\n", "# for rg in range(pf.num_row_groups):\n", "# tx = pf.read_row_group(rg, columns=cols).to_pandas()\n", "# if min_qv is not None and \"qv\" in tx.columns:\n", "# tx = tx[tx[\"qv\"] >= min_qv]\n", "# if tx.empty:\n", "# continue\n", "\n", "# x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n", "# y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n", "# xy1 = np.vstack([x, y, np.ones(len(tx), dtype=np.float64)])\n", "# out = transform3 @ xy1\n", "\n", "# chunk = pd.DataFrame({\n", "# \"spatial_x\": (out[0] / out[2]).astype(np.float32),\n", "# \"spatial_y\": (out[1] / out[2]).astype(np.float32),\n", "# \"gene\": tx[\"feature_name\"].astype(str).values,\n", "# })\n", "# if \"qv\" in tx.columns:\n", "# chunk[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\").values\n", "# if include_cell_id and \"cell_id\" in tx.columns:\n", "# chunk[\"cell_id\"] = tx[\"cell_id\"].astype(str).values\n", "\n", "# table = pa.Table.from_pandas(chunk, preserve_index=False)\n", "# if writer is None:\n", "# writer = pq.ParquetWriter(tmp_path, table.schema)\n", "# writer.write_table(table)\n", "\n", "# written += len(chunk)\n", "# print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n", "\n", "# writer.close()\n", "# print(\"All row groups written to tmp parquet. Building AnnData...\")\n", "\n", "# df = pd.read_parquet(tmp_path)\n", "# df[\"gene\"] = df[\"gene\"].astype(\"category\")\n", "# if include_cell_id and \"cell_id\" in df.columns:\n", "# df[\"cell_id\"] = df[\"cell_id\"].astype(\"category\")\n", "\n", "# spatial = df[[\"spatial_x\", \"spatial_y\"]].to_numpy(dtype=np.float32)\n", "# obs = df.drop(columns=[\"spatial_x\", \"spatial_y\"])\n", "# obs.index = pd.RangeIndex(len(obs)).astype(str)\n", "\n", "# X = csc_matrix((len(obs), 0), dtype=np.float32)\n", "# var = pd.DataFrame(index=pd.Index([], dtype=str))\n", "# transcript_adata = ad.AnnData(X=X, obs=obs, var=var)\n", "# transcript_adata.obsm[\"spatial\"] = spatial\n", "# transcript_adata.write_h5ad(out_path)\n", "\n", "# os.remove(tmp_path)\n", "# print(\"wrote:\", out_path)\n", "# print(\"shape:\", transcript_adata.shape)\n", "# print(\"obs columns:\", list(transcript_adata.obs.columns))\n", "# return out_name\n", "\n", "\n", "# transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n", "# xenium_dir=xenium_dir,\n", "# basedir=basedir,\n", "# transform=transform,\n", "# min_qv=None,\n", "# include_cell_id=False,\n", "# tmp_path=\"/Volumes/T7 Shield/tmp/transcripts_tmp.parquet\",\n", "# )" ] }, { "cell_type": "code", "execution_count": null, "id": "02d53e78-c105-413c-9b30-f950b42ed200", "metadata": {}, "outputs": [], "source": [ "def xenium_all_transcripts_to_h5ad_empty_X(\n", " xenium_dir,\n", " basedir,\n", " transform,\n", " out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n", " min_qv=None,\n", " include_cell_id=False,\n", " tmp_path=None,\n", "):\n", " import os\n", " import numpy as np\n", " import pandas as pd\n", " import pyarrow as pa\n", " import pyarrow.parquet as pq\n", " import h5py\n", " from scipy.sparse import csc_matrix\n", "\n", " transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n", " out_path = os.path.join(basedir, out_name)\n", " if tmp_path is None:\n", " tmp_path = out_path.replace(\".h5ad\", \"_tmp.parquet\")\n", "\n", " pf = pq.ParquetFile(transcript_path)\n", " transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n", "\n", " schema_cols = set(pf.schema.names)\n", " cols = [\"x_location\", \"y_location\", \"feature_name\"]\n", " if \"qv\" in schema_cols:\n", " cols.append(\"qv\")\n", " if include_cell_id and \"cell_id\" in schema_cols:\n", " cols.append(\"cell_id\")\n", "\n", " # --- Pass 1: stream row groups → tmp parquet ---\n", " writer = None\n", " written = 0\n", " for rg in range(pf.num_row_groups):\n", " tx = pf.read_row_group(rg, columns=cols).to_pandas()\n", " if min_qv is not None and \"qv\" in tx.columns:\n", " tx = tx[tx[\"qv\"] >= min_qv]\n", " if tx.empty:\n", " continue\n", "\n", " x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n", " y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n", " xy1 = np.vstack([x, y, np.ones(len(tx), dtype=np.float64)])\n", " out = transform3 @ xy1\n", "\n", " chunk = pd.DataFrame({\n", " \"spatial_x\": (out[0] / out[2]).astype(np.float32),\n", " \"spatial_y\": (out[1] / out[2]).astype(np.float32),\n", " \"gene\": tx[\"feature_name\"].astype(str).values,\n", " })\n", " if \"qv\" in tx.columns:\n", " chunk[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\").values\n", " if include_cell_id and \"cell_id\" in tx.columns:\n", " chunk[\"cell_id\"] = tx[\"cell_id\"].astype(str).values\n", "\n", " table = pa.Table.from_pandas(chunk, preserve_index=False)\n", " if writer is None:\n", " writer = pq.ParquetWriter(tmp_path, table.schema)\n", " writer.write_table(table)\n", " written += len(chunk)\n", " print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n", "\n", " writer.close()\n", " total_rows = written\n", " print(f\"Pass 1 done. {total_rows:,} rows. Writing h5ad...\")\n", "\n", " # --- Pass 2: stream tmp parquet → h5ad via h5py, one chunk at a time ---\n", " pf2 = pq.ParquetFile(tmp_path)\n", " obs_col_names = [c for c in pf2.schema.names if c not in (\"spatial_x\", \"spatial_y\")]\n", "\n", " with h5py.File(out_path, \"w\") as f:\n", " # AnnData minimal structure\n", " f.attrs[\"encoding-type\"] = \"anndata\"\n", " f.attrs[\"encoding-version\"] = \"0.1.0\"\n", "\n", " # X group — empty (0 vars)\n", " xgrp = f.create_group(\"X\")\n", " xgrp.attrs[\"encoding-type\"] = \"array\"\n", " xgrp.attrs[\"encoding-version\"] = \"0.2.0\"\n", " xgrp.create_dataset(\"data\", data=np.array([], dtype=np.float32))\n", " xgrp.create_dataset(\"indices\",data=np.array([], dtype=np.int32))\n", " xgrp.create_dataset(\"indptr\", data=np.zeros(1, dtype=np.int32))\n", " xgrp.attrs[\"shape\"] = [total_rows, 0]\n", "\n", " # obsm/spatial — pre-allocate, fill in chunks\n", " obsm = f.create_group(\"obsm\")\n", " spatial_ds = obsm.create_dataset(\n", " \"spatial\", shape=(total_rows, 2), dtype=np.float32\n", " )\n", "\n", " # obs — pre-allocate string datasets per column\n", " obs_grp = f.create_group(\"obs\")\n", " obs_grp.attrs[\"_index\"] = \"_index\"\n", " obs_grp.attrs[\"encoding-type\"] = \"dataframe\"\n", " obs_grp.attrs[\"encoding-version\"] = \"0.2.0\"\n", " obs_grp.attrs[\"column-order\"] = obs_col_names\n", "\n", " # pre-allocate index\n", " idx_ds = obs_grp.create_dataset(\n", " \"_index\", shape=(total_rows,), dtype=h5py.string_dtype()\n", " )\n", " col_datasets = {}\n", " for col in obs_col_names:\n", " col_datasets[col] = obs_grp.create_dataset(\n", " col, shape=(total_rows,), dtype=h5py.string_dtype()\n", " )\n", "\n", " # var — empty\n", " var_grp = f.create_group(\"var\")\n", " var_grp.attrs[\"_index\"] = \"_index\"\n", " var_grp.attrs[\"encoding-type\"] = \"dataframe\"\n", " var_grp.attrs[\"encoding-version\"] = \"0.2.0\"\n", " var_grp.attrs[\"column-order\"] = []\n", " var_grp.create_dataset(\"_index\", data=np.array([], dtype=h5py.string_dtype()))\n", "\n", " # stream fill\n", " cursor = 0\n", " for rg in range(pf2.num_row_groups):\n", " chunk = pf2.read_row_group(rg).to_pandas()\n", " n = len(chunk)\n", " sl = slice(cursor, cursor + n)\n", "\n", " spatial_ds[sl] = chunk[[\"spatial_x\", \"spatial_y\"]].to_numpy(dtype=np.float32)\n", " idx_ds[sl] = np.arange(cursor, cursor + n).astype(str)\n", " for col in obs_col_names:\n", " col_datasets[col][sl] = chunk[col].astype(str).values\n", "\n", " cursor += n\n", " if rg % 50 == 0:\n", " print(f\" h5ad pass {rg + 1}/{pf2.num_row_groups}: {cursor:,}\")\n", "\n", " os.remove(tmp_path)\n", " print(\"wrote:\", out_path)\n", " print(\"shape:\", (total_rows, 0))\n", " print(\"obs columns:\", obs_col_names)\n", " return out_name" ] }, { "cell_type": "code", "execution_count": null, "id": "cc5c8636-ed10-47e9-bf9c-08d5608095f8", "metadata": { "scrolled": true }, "outputs": [], "source": [ "transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n", " xenium_dir=xenium_dir,\n", " basedir=basedir,\n", " transform=transform,\n", " min_qv=None,\n", " include_cell_id=False,\n", " tmp_path=\"/Volumes/T7 Shield/tmp/transcripts_tmp.parquet\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "a53c9e37-da32-4583-a74c-604d60df358b", "metadata": {}, "outputs": [], "source": [ "# basedir = os.path.abspath(f\"tissuumaps/{sample}\")\n", "# transcript_csv = f\"transcripts_image_space.csv\"\n", "\n", "# print(basedir)\n", "\n", "# region_files = []\n", "\n", "# for geojson_path in glob.glob(os.path.join(basedir, \"*boundaries*.geojson\")):\n", "# geojson_name = os.path.basename(geojson_path)\n", "\n", "# region_files.append({\n", "# \"path\": geojson_name, # relative path only\n", "# \"title\": geojson_name,\n", "# \"comment\": geojson_name,\n", "# \"autoLoad\": True,\n", "# })\n", "\n", "# print(region_files)\n", "\n", "# image_layers = []\n", "\n", "# for tif_path in sorted(\n", "# glob.glob(os.path.join(basedir, \"morphology_focus_plane*_pyramid.tif\"))\n", "# ):\n", "# name = os.path.basename(tif_path)\n", "\n", "# image_layers.append({\n", "# \"name\": name.replace(\".tif\", \"\"),\n", "# \"tileSource\": name + \".dzi\",\n", "# \"x\": 0,\n", "# \"y\": 0,\n", "# \"scale\": 1,\n", "# \"rotation\": 0,\n", "# \"flip\": False,\n", "# })\n", "\n", "# print(f\"Found {len(image_layers)} image layers\")" ] }, { "cell_type": "code", "execution_count": null, "id": "1ee1ac2d-6de4-4fe2-b31a-90725d67669c", "metadata": {}, "outputs": [], "source": [ "# # ----------------------------\n", "# # 5. Generate TissUUmaps project\n", "# # ----------------------------\n", "# project = read_h5ad.h5ad_to_tmap(basedir, out_h5ad_name)\n", "\n", "# # Images: all stacked and visible\n", "# project[\"layers\"] = image_layers\n", "# project[\"collectionMode\"] = False\n", "# project[\"compositeMode\"] = \"lighter\"\n", "# project[\"backgroundColor\"] = \"#000000\"\n", "\n", "# project[\"filters\"] = []\n", "# project[\"layerFilters\"] = {}\n", "# project[\"layerOpacities\"] = {str(i): 1 for i in range(len(image_layers))}\n", "# project[\"layerVisibilities\"] = {str(i): True for i in range(len(image_layers))}\n", "\n", "# # Polygons: autoload\n", "# project[\"regionFiles\"] = []\n", "# for rf in region_files:\n", "# rf = dict(rf)\n", "# rf[\"autoLoad\"] = True\n", "# project[\"regionFiles\"].append(rf)\n", "\n", "# # Keep h5ad/cell-expression dropdowns available, but not autoloaded\n", "# for mf in project.get(\"markerFiles\", []):\n", "# mf[\"autoLoad\"] = False\n", "# mf.setdefault(\"expectedHeader\", {})\n", "# mf[\"expectedHeader\"][\"shape_fixed\"] = \"disc\"\n", "# mf[\"expectedHeader\"][\"scale_factor\"] = 1\n", "\n", "# mf.setdefault(\"expectedRadios\", {})\n", "# mf[\"expectedRadios\"][\"shape_fixed\"] = True\n", "# mf[\"expectedRadios\"][\"shape_gr\"] = False\n", "# mf[\"expectedRadios\"][\"shape_gr_rand\"] = False\n", "# mf[\"expectedRadios\"][\"sortby_check\"] = False\n", "\n", "# # Transcripts: autoload default marker layer\n", "# if transcript_csv is not None:\n", "# project[\"markerFiles\"].insert(\n", "# 0,\n", "# {\n", "# \"path\": transcript_csv,\n", "# \"title\": \"Load transcripts\",\n", "# \"comment\": \"Transcript molecules\",\n", "# \"name\": \"Transcripts\",\n", "# \"uid\": \"transcripts\",\n", "# \"autoLoad\": True,\n", "# \"hideSettings\": True,\n", "# \"expectedHeader\": {\n", "# \"X\": \"x\",\n", "# \"Y\": \"y\",\n", "# \"gb_col\": \"gene\",\n", "# \"gb_name\": \"\",\n", "# \"cb_col\": \"\",\n", "# \"cb_cmap\": \"\",\n", "# \"scale_factor\": 0.15,\n", "# \"shape_fixed\": \"disc\",\n", "# \"opacity\": 0.7,\n", "# },\n", "# \"expectedRadios\": {\n", "# \"cb_col\": False,\n", "# \"cb_gr\": True,\n", "# \"cb_gr_rand\": True,\n", "# \"cb_gr_dict\": False,\n", "# \"cb_gr_key\": False,\n", "# \"pie_check\": False,\n", "# \"scale_check\": False,\n", "# \"shape_col\": False,\n", "# \"shape_fixed\": True,\n", "# \"shape_gr\": False,\n", "# \"shape_gr_rand\": False,\n", "# \"shape_gr_dict\": False,\n", "# \"sortby_check\": False,\n", "# },\n", "# },\n", "# )\n", "\n", "# # project[\"markerFiles\"].insert(\n", "# # 0,\n", "# # {\n", "# # \"path\": transcript_h5ad,\n", "# # \"title\": \"Load transcript AnnData\",\n", "# # \"comment\": \"All transcript molecules\",\n", "# # \"name\": \"Transcript AnnData\",\n", "# # \"uid\": \"transcript_h5ad\",\n", "# # \"autoLoad\": True,\n", "# # \"hideSettings\": True,\n", "# # \"expectedHeader\": {\n", "# # \"X\": \"/obsm/spatial;0\",\n", "# # \"Y\": \"/obsm/spatial;1\",\n", "# # \"gb_col\": \"/obs/gene\",\n", "# # \"gb_name\": \"\",\n", "# # \"cb_col\": \"\",\n", "# # \"cb_cmap\": \"\",\n", "# # \"scale_factor\": 0.15,\n", "# # \"shape_fixed\": \"disc\",\n", "# # \"opacity\": 0.7,\n", "# # },\n", "# # \"expectedRadios\": {\n", "# # \"cb_col\": False,\n", "# # \"cb_gr\": True,\n", "# # \"cb_gr_rand\": True,\n", "# # \"shape_fixed\": True,\n", "# # \"shape_gr\": False,\n", "# # \"scale_check\": False,\n", "# # \"sortby_check\": False,\n", "# # },\n", "# # },\n", "# # )\n", "\n", "# with open(project_path, \"w\") as f:\n", "# json.dump(project, f, indent=2)\n", "\n", "# viewer = tj.opentmap(project_path)" ] }, { "cell_type": "code", "execution_count": null, "id": "68db233e-7105-4862-9635-c68875497962", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "696e478b-61c6-4d35-8b90-813569c4a0ed", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (tissuumaps_env)", "language": "python", "name": "tissuumaps_env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.23" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }