diff --git a/notebooks/Pre-process_Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs.ipynb b/notebooks/Pre-process_Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs.ipynb
index b666bec7..76047657 100644
--- a/notebooks/Pre-process_Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs.ipynb
+++ b/notebooks/Pre-process_Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "id": "8aedda0a-f8a2-46cf-a5b5-371b444c917f",
    "metadata": {},
    "outputs": [
@@ -18,8 +18,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n",
       "env: ANYWIDGET_HMR=1\n"
      ]
     }
@@ -32,156 +30,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "d220d081-49d8-44e5-a7af-7d0d08fe27af",
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "\n",
-    "# macOS requirement\n",
-    "import os\n",
-    "os.environ['DYLD_LIBRARY_PATH'] = '/opt/homebrew/lib:' + os.environ.get('DYLD_LIBRARY_PATH', '')\n",
-    "\n",
     "import celldega as dega\n",
-    "\n",
     "import tifffile\n",
     "import zarr\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import to_hex\n",
-    "\n",
     "import geopandas as gpd\n",
     "import shapely\n",
+    "import tarfile\n",
     "\n",
-    "import tarfile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5cad54fd-5eb5-4b5c-bbda-1cfcbf1a12d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "analysis.tar.gz               cells.zarr.zip\n",
-      "analysis.zarr.zip             experiment.xenium\n",
-      "analysis_summary.html         gene_panel.json\n",
-      "aux_outputs.tar.gz            metrics_summary.csv\n",
-      "cell_boundaries.csv.gz        morphology.ome.tif\n",
-      "cell_boundaries.parquet       \u001b[1m\u001b[36mmorphology_focus\u001b[m\u001b[m/\n",
-      "cell_feature_matrix.h5        nucleus_boundaries.csv.gz\n",
-      "cell_feature_matrix.tar.gz    nucleus_boundaries.parquet\n",
-      "cell_feature_matrix.zarr.zip  transcripts.parquet\n",
-      "cells.csv.gz                  transcripts.zarr.zip\n",
-      "cells.parquet\n"
-     ]
-    }
-   ],
-   "source": [
-    "ls ../data/xenium_data/Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e7f41415-36d8-48f9-bf82-b465871a9a66",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m\u001b[36mLandscape_Xenium_V1_human_Pancreas_FFPE_outs\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mLandscape_Xenium_V1_human_Pancreas_FFPE_outs_png\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mLandscape_Xenium_V1_human_Pancreas_FFPE_outs_webp\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mXenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mXenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs_landscape_files\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mXenium_Prime_Human_Prostate_FFPE_outs\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mXenium_Prime_Human_Skin_FFPE_outs\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mXenium_V1_hBoneMarrow_nondiseased_section_outs_landscape_files\u001b[m\u001b[m/\n",
-      "\u001b[1m\u001b[36mXenium_V1_hBoneMarrow_nondiseased_section_outs_unscaled\u001b[m\u001b[m/\n"
-     ]
-    }
-   ],
-   "source": [
-    "ls ../data/xenium_landscapes/"
+    "# macOS requirement\n",
+    "import os\n",
+    "os.environ['DYLD_LIBRARY_PATH'] = '/opt/homebrew/lib:' + os.environ.get('DYLD_LIBRARY_PATH', '')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "4c0b5695-ee4b-4b2b-b620-dfb1780d5d0e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset_name = 'Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "58b5924d-5a8d-45e6-9d0b-1a846c506104",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_path = '../data/xenium_data/' + dataset_name + '/'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "e62bc950-cc8c-4f16-8df2-f9818fd44ae9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path_landscape_files = '../data/xenium_landscapes/' + dataset_name + '_landscape_files/'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c26ec3ba-b9c2-480d-bf2e-fe5f7469d531",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'../data/xenium_data/Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs/'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "ab8798a4-b3ca-4bba-bafb-b74acc2365c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'../data/xenium_landscapes/Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs_landscape_files/'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "path_landscape_files"
+    "# pass values to the following variables\n",
+    "\n",
+    "dataset_name = 'Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs'\n",
+    "base_path = 'data/xenium_data/' + dataset_name + '/'\n",
+    "path_landscape_files = 'data/xenium_landscapes/' + dataset_name + '_landscape_files/'\n",
+    "\n",
+    "# zipped files are not always present in xenium\n",
+    "# input variable of celldega version\n",
+    "\n",
+    "tile_size = 250\n",
+    "image_scale = 0.5\n",
+    "technology = 'Xenium'\n",
+    "\n",
+    "suffix = '.webp[Q=100]'\n",
+    "\n",
+    "if not os.path.exists(path_landscape_files):\n",
+    "    os.mkdir(path_landscape_files)\n",
+    "\n",
+    "if not os.path.exists(path_landscape_files + 'cell_clusters/'):\n",
+    "    os.mkdir(path_landscape_files + 'cell_clusters/')"
    ]
   },
   {
@@ -190,10 +86,7 @@
    "id": "d59a0077-444c-49f4-ab3a-fadca0cd6e61",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "if not os.path.exists(path_landscape_files):\n",
-    "    os.mkdir(path_landscape_files)"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -228,15 +121,13 @@
    "source": [
     "# Path to the tar.gz file you want to decompress\n",
     "tar_file_path = base_path + 'cell_feature_matrix.tar.gz'\n",
-    "# Path to the directory where you want to extract the contents\n",
-    "output_directory = path_landscape_files\n",
     "\n",
     "# Open the tar.gz file\n",
     "with tarfile.open(tar_file_path, \"r:gz\") as tar:\n",
     "    # Extract all contents to the specified directory\n",
-    "    tar.extractall(path=output_directory)\n",
+    "    tar.extractall(path=path_landscape_files)\n",
     "\n",
-    "print(f\"File {tar_file_path} has been decompressed to {output_directory}\")\n"
+    "print(f\"File {tar_file_path} has been decompressed to {path_landscape_files}\")"
    ]
   },
   {
@@ -264,15 +155,13 @@
    "source": [
     "# Path to the tar.gz file you want to decompress\n",
     "tar_file_path = base_path + 'analysis.tar.gz'\n",
-    "# Path to the directory where you want to extract the contents\n",
-    "output_directory = path_landscape_files\n",
     "\n",
     "# Open the tar.gz file\n",
     "with tarfile.open(tar_file_path, \"r:gz\") as tar:\n",
     "    # Extract all contents to the specified directory\n",
-    "    tar.extractall(path=output_directory)\n",
+    "    tar.extractall(path=path_landscape_files)\n",
     "\n",
-    "print(f\"File {tar_file_path} has been decompressed to {output_directory}\")\n"
+    "print(f\"File {tar_file_path} has been decompressed to {path_landscape_files}\")"
    ]
   },
   {
@@ -728,8 +617,7 @@
     }
    ],
    "source": [
-    "cbg = dega.pre.read_cbg_mtx(path_landscape_files + 'cell_feature_matrix/')\n",
-    "cbg"
+    "cbg = dega.pre.read_cbg_mtx(path_landscape_files + 'cell_feature_matrix/')"
    ]
   },
   {
@@ -778,7 +666,7 @@
    "source": [
     "path_cbg = path_landscape_files + 'cell_feature_matrix/'\n",
     "path_output = path_landscape_files + 'meta_gene.parquet'\n",
-    "dega.pre.make_meta_gene('Xenium', path_cbg, path_output)"
+    "dega.pre.make_meta_gene(technology, path_cbg, path_output)"
    ]
   },
   {
@@ -796,7 +684,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# dega.pre.save_cbg_gene_parquets(path_landscape_files, cbg, verbose=True)"
+    "dega.pre.save_cbg_gene_parquets(path_landscape_files, cbg, verbose=True)"
    ]
   },
   {
@@ -809,50 +697,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "6bc3160b-8c6a-424e-8db6-fb755b0a60c7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<tifffile.TiffFile 'morphology_focus_0000.ome.tif'> OME series cannot read multi-file pyramids\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import tifffile\n",
-    "\n",
     "# Path to your OME-TIFF file\n",
     "file_path = base_path + 'morphology_focus/morphology_focus_0000.ome.tif'\n",
     "\n",
     "# Open the OME-TIFF file and read the image data\n",
     "with tifffile.TiffFile(file_path) as tif:\n",
     "    series = tif.series[0]  # Assuming you are interested in the first series\n",
-    "    image_data = series.asarray()\n"
+    "    image_data = series.asarray()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "0e0a1a57-e614-4f0b-b2eb-f8ee462d9520",
+   "execution_count": null,
+   "id": "76297112-ef6d-4993-ae28-83d40aace55f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4, 34119, 39776)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "image_data.shape"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -862,26 +727,6 @@
     "### DAPI"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "f3b5d7f0-3777-4903-a83a-bbf5ee6e85a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image_scale = 0.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "0b69f87f-f47c-418b-b6ad-2336eb575158",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "suffix = '.webp[Q=100]'"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 34,
@@ -889,93 +734,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# image_data_scaled = image_data[0,:,:] * 2\n",
+    "for channel_index, channel_name in zip(range(image_data.shape[0]), ['dapi','bound','rna','prot']):\n",
     "\n",
-    "# # Save the image data to a regular TIFF file without compression\n",
-    "# tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "# image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', 0.5, path_landscape_files)\n",
-    "# image_jpeg = dega.pre.convert_to_jpeg(image_ds, quality=100)\n",
-    "# dega.pre.make_deepzoom_pyramid(image_jpeg, path_landscape_files + 'pyramid_images/', 'dapi')\n",
-    "\n",
-    "image_data_scaled = image_data[0,:,:] * 2\n",
-    "\n",
-    "# Save the image data to a regular TIFF file without compression\n",
-    "tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', image_scale, path_landscape_files)\n",
-    "image_png = dega.pre.convert_to_png(image_ds)\n",
-    "dega.pre.make_deepzoom_pyramid(image_png, path_landscape_files + 'pyramid_images/', 'dapi', suffix=suffix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "d8c8812b-759b-4634-8ad3-cfb9f42a96e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# image_data_scaled = image_data[1,:,:] * 2\n",
-    "\n",
-    "# # Save the image data to a regular TIFF file without compression\n",
-    "# tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "# image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', 0.5, path_landscape_files)\n",
-    "# image_jpeg = dega.pre.convert_to_jpeg(image_ds, quality=100)\n",
-    "# dega.pre.make_deepzoom_pyramid(image_jpeg, path_landscape_files + 'pyramid_images/', 'bound')\n",
-    "\n",
-    "image_data_scaled = image_data[1,:,:] * 2\n",
-    "\n",
-    "# Save the image data to a regular TIFF file without compression\n",
-    "tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', image_scale, path_landscape_files)\n",
-    "image_png = dega.pre.convert_to_png(image_ds)\n",
-    "dega.pre.make_deepzoom_pyramid(image_png, path_landscape_files + 'pyramid_images/', 'bound', suffix=suffix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "9ac09697-43b7-4c89-92e4-50479aa9da62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# image_data_scaled = image_data[2,:,:] * 2\n",
-    "\n",
-    "# # Save the image data to a regular TIFF file without compression\n",
-    "# tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "# image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', 0.5, path_landscape_files)\n",
-    "# image_jpeg = dega.pre.convert_to_jpeg(image_ds, quality=100)\n",
-    "# dega.pre.make_deepzoom_pyramid(image_jpeg, path_landscape_files + 'pyramid_images/', 'rna')\n",
-    "\n",
-    "image_data_scaled = image_data[2,:,:] * 2\n",
-    "\n",
-    "# Save the image data to a regular TIFF file without compression\n",
-    "tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', image_scale, path_landscape_files)\n",
-    "image_png = dega.pre.convert_to_png(image_ds)\n",
-    "dega.pre.make_deepzoom_pyramid(image_png, path_landscape_files + 'pyramid_images/', 'rna', suffix=suffix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "1bc7122e-b7d1-4964-a937-5956c9562b17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# image_data_scaled = image_data[3,:,:] * 2\n",
-    "\n",
-    "# # Save the image data to a regular TIFF file without compression\n",
-    "# tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "# image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', 0.5, path_landscape_files)\n",
-    "# image_jpeg = dega.pre.convert_to_jpeg(image_ds, quality=100)\n",
-    "# dega.pre.make_deepzoom_pyramid(image_jpeg, path_landscape_files + 'pyramid_images/', 'prot')\n",
-    "\n",
-    "image_data_scaled = image_data[3,:,:] * 2\n",
-    "\n",
-    "# Save the image data to a regular TIFF file without compression\n",
-    "tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
-    "image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', image_scale, path_landscape_files)\n",
-    "image_png = dega.pre.convert_to_png(image_ds)\n",
-    "dega.pre.make_deepzoom_pyramid(image_png, path_landscape_files + 'pyramid_images/', 'prot', suffix=suffix)"
+    "    image_data_scaled = image_data[channel_index,:,:] * 2\n",
+    "    \n",
+    "    # Save the image data to a regular TIFF file without compression\n",
+    "    tifffile.imwrite(path_landscape_files + 'output_regular.tif', image_data_scaled, compression=None)\n",
+    "    image_ds = dega.pre.reduce_image_size(path_landscape_files + 'output_regular.tif', image_scale, path_landscape_files)\n",
+    "    image_png = dega.pre.convert_to_png(image_ds)\n",
+    "    dega.pre.make_deepzoom_pyramid(image_png, path_landscape_files + 'pyramid_images/', channel_name, suffix=suffix)"
    ]
   },
   {
@@ -1003,35 +770,7 @@
     "\n",
     "# For example, use the above function to open the cells Zarr file, which contains segmentation mask Zarr arrays\n",
     "root = open_zarr(base_path + \"cells.zarr.zip\")\n",
-    "\n",
-    "# # Look at group array info and structure\n",
-    "# root.info\n",
-    "# root.tree() # shows structure, array dimensions, data types\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "e9bcd108-14d2-4d1a-9fa9-2650d8cb9f7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[4.705882, 0.      , 0.      , 0.      ],\n",
-       "       [0.      , 4.705882, 0.      , 0.      ],\n",
-       "       [0.      , 0.      , 1.      , 0.      ],\n",
-       "       [0.      , 0.      , 0.      , 1.      ]], dtype=float32)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformation_matrix = root['masks']['homogeneous_transform'][:]\n",
-    "transformation_matrix"
+    "transformation_matrix = root['masks']['homogeneous_transform'][:]"
    ]
   },
   {
@@ -1169,8 +908,7 @@
     }
    ],
    "source": [
-    "default_clustering = pd.read_csv(path_landscape_files + 'analysis/clustering/gene_expression_graphclust/clusters.csv', index_col=0)\n",
-    "default_clustering"
+    "default_clustering = pd.read_csv(path_landscape_files + 'analysis/clustering/gene_expression_graphclust/clusters.csv', index_col=0)"
    ]
   },
   {
@@ -1181,91 +919,6 @@
     "### Save default clustering results"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "18c3d5bb-2d85-455e-9248-1d831fcea411",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(path_landscape_files + 'cell_clusters/'):\n",
-    "    os.mkdir(path_landscape_files + 'cell_clusters/')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "eec7ab86-0f7f-4d47-a5b8-73c1d26fe50d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>aaaaadoa-1</th>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aaaaclhf-1</th>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aaaafcfj-1</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aaaagamp-1</th>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aaaaiako-1</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           cluster\n",
-       "aaaaadoa-1      19\n",
-       "aaaaclhf-1       6\n",
-       "aaaafcfj-1       2\n",
-       "aaaagamp-1      19\n",
-       "aaaaiako-1      10"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "default_clustering = pd.DataFrame(default_clustering.values, index=default_clustering.index.tolist(), columns=['cluster'])\n",
-    "default_clustering.head()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 29,
@@ -1337,61 +990,13 @@
    ],
    "source": [
     "default_clustering_ini = pd.DataFrame(default_clustering.values, index=default_clustering.index.tolist(), columns=['cluster'])\n",
-    "default_clustering_ini.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "fc306c9a-efdd-446a-8f53-47751121f434",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(708983, 2)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "meta_cell = pd.read_parquet(path_landscape_files + 'cell_metadata.parquet')\n",
-    "meta_cell.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "f9f3bdf1-3703-4463-bb45-0d8ff47ad43c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "default_clustering_ini['cluster'] = default_clustering_ini['cluster'].astype('string')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "54be8933-139c-4f4c-a8a8-5d91ece8fdc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "default_clustering_ini['cluster'] = default_clustering_ini['cluster'].astype('string')\n",
     "default_clustering = pd.DataFrame(index=meta_cell.index.tolist())\n",
+    "default_clustering.loc[default_clustering_ini.index.tolist(), 'cluster'] = default_clustering_ini['cluster']\n",
+    "default_clustering.to_parquet(path_landscape_files + 'cell_clusters/cluster.parquet')\n",
     "\n",
-    "default_clustering.loc[default_clustering_ini.index.tolist(), 'cluster'] = default_clustering_ini['cluster']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "755ec403-12c3-451d-a31b-ffb64fc89253",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "default_clustering.to_parquet(path_landscape_files + 'cell_clusters/cluster.parquet')"
+    "ser_counts = default_clustering['cluster'].value_counts()\n",
+    "clusters = ser_counts.index.tolist()"
    ]
   },
   {
@@ -1411,41 +1016,13 @@
    "source": [
     "# do not including clustering information in default cell metadata\n",
     "dega.pre.make_meta_cell_image_coord(\n",
-    "    'Xenium', \n",
+    "    technology, \n",
     "    path_transformation_matrix, \n",
     "    path_meta_cell_micron, \n",
     "    path_meta_cell_image, \n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "bcf9a4bb-3d31-47c4-9900-a6546c737a45",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df_meta = pd.read_csv(path_landscape_files + 'analysis/clustering/gene_expression_graphclust/clusters.csv', index_col=0)\n",
-    "# df_meta['Cluster'] = df_meta['Cluster'].astype('string')\n",
-    "# df_meta.columns = ['cluster']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "7dce3079-1251-410d-b918-dad467581421",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dega.pre.make_meta_cell_image_coord(\n",
-    "#     'Xenium', \n",
-    "#     path_transformation_matrix, \n",
-    "#     path_meta_cell_micron, \n",
-    "#     path_meta_cell_image, \n",
-    "#     df_meta=df_meta\n",
-    "# )"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e998016a-756e-44b2-8cb3-d04b91e49d01",
@@ -1454,17 +1031,6 @@
     "### Cluster Colors"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "906c861e-3d4c-4f07-b2ff-42ef3a7de934",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ser_counts = default_clustering['cluster'].value_counts()\n",
-    "clusters = ser_counts.index.tolist()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -1497,7 +1063,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "205f0c7c-b5c8-4ed5-8f63-ea72d26ab63b",
+   "id": "82bdf36c-7ecc-4f74-a296-6eae57837233",
    "metadata": {},
    "source": [
     "### Transcripts"
@@ -1505,32 +1071,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "1fb304b8-8708-470a-bf9f-1c4b795f4a32",
+   "execution_count": null,
+   "id": "5ae4d92b-8fc2-4545-8d01-38ce4102032d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2h 59min 13s, sys: 2h 18min 17s, total: 5h 17min 31s\n",
-      "Wall time: 12h 31min 20s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# %%time \n",
-    "# technology = 'Xenium'\n",
-    "# path_trx = base_path + 'transcripts.parquet'\n",
-    "# path_trx_tiles = path_landscape_files + 'transcript_tiles'\n",
-    "# tile_bounds = dega.pre.make_trx_tiles(\n",
-    "#     'Xenium', \n",
-    "#     path_trx, \n",
-    "#     path_transformation_matrix, \n",
-    "#     path_trx_tiles,\n",
-    "#     tile_size=100,\n",
-    "#     # verbose=True\n",
-    "# )"
+    "%%time \n",
+    "\n",
+    "path_trx = base_path + 'transcripts.parquet'\n",
+    "path_trx_tiles = path_landscape_files + 'transcript_tiles'\n",
+    "tile_bounds = dega.pre.make_trx_tiles(\n",
+    "    technology, \n",
+    "    path_trx, \n",
+    "    path_transformation_matrix, \n",
+    "    path_trx_tiles,\n",
+    "    tile_size=tile_size,\n",
+    "    image_scale=image_scale\n",
+    ")"
    ]
   },
   {
@@ -1561,12 +1118,12 @@
     "path_cell_boundaries = base_path + 'cell_boundaries.parquet'\n",
     "path_output = path_landscape_files + 'cell_segmentation'\n",
     "dega.pre.make_cell_boundary_tiles(\n",
-    "    'Xenium',\n",
+    "    technology,\n",
     "    path_cell_boundaries, \n",
     "    path_meta_cell_micron, \n",
     "    path_transformation_matrix, \n",
     "    path_output,\n",
-    "    tile_size=100,\n",
+    "    tile_size=tile_size,\n",
     "    tile_bounds=tile_bounds\n",
     ")"
    ]
@@ -1598,37 +1155,7 @@
    "source": [
     "path_cbg = path_landscape_files + 'cell_feature_matrix/'\n",
     "path_output = path_landscape_files + 'gene_metadata.parquet'\n",
-    "dega.pre.make_meta_gene('Xenium', path_cbg, path_output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a4e5e1d-2159-47ef-a4e4-64870d495935",
-   "metadata": {},
-   "source": [
-    "### Max Zoom"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "8d1843a2-7a55-4d9f-b428-d9a122b8707e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Example usage:\n",
-    "path_image_pyramid = path_landscape_files + 'pyramid_images/dapi_files/'  # Change this to your actual directory path\n",
-    "max_pyramid_zoom = dega.pre.get_max_zoom_level(path_image_pyramid)\n",
-    "\n",
-    "print(max_pyramid_zoom)"
+    "dega.pre.make_meta_gene(technology, path_cbg, path_output)"
    ]
   },
   {
@@ -1763,16 +1290,11 @@
     "usecols = ['cell_id', 'x_centroid', 'y_centroid']\n",
     "meta_cell = pd.read_csv(base_path + 'cells.csv.gz', index_col=0, usecols=usecols)\n",
     "meta_cell.columns = ['center_x', 'center_y']\n",
-    "meta_cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "f508a94e-5c32-4927-8eab-c82c25ae1214",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "\n",
+    "df_meta = pd.read_csv(path_landscape_files + 'analysis/clustering/gene_expression_graphclust/clusters.csv', index_col=0)\n",
+    "df_meta['Cluster'] = df_meta['Cluster'].astype('string')\n",
+    "df_meta.columns = ['cluster']\n",
+    "\n",
     "meta_cell['cluster'] = df_meta['cluster']"
    ]
   },
@@ -1787,14 +1309,13 @@
     "for inst_cat in meta_cell['cluster'].unique().tolist():\n",
     "    if inst_cat is not None:\n",
     "        inst_cells = meta_cell[meta_cell['cluster'] == inst_cat].index.tolist()\n",
-    "        # print(inst_cat, len(inst_cells))\n",
     "\n",
     "        inst_ser = cbg.loc[inst_cells].sum()/len(inst_cells)\n",
     "        inst_ser.name = inst_cat\n",
     "\n",
     "        list_ser.append(inst_ser)\n",
     "\n",
-    "df_sig = pd.concat(list_ser, axis=1)    \n"
+    "df_sig = pd.concat(list_ser, axis=1)    "
    ]
   },
   {
@@ -1804,8 +1325,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_sig = pd.concat(list_ser, axis=1)\n",
     "# handling weird behavior where there is a multiindex it appears\n",
+    "\n",
     "df_sig.columns = df_sig.columns.tolist()\n",
     "df_sig.index = df_sig.index.tolist()"
    ]
@@ -1832,10 +1353,8 @@
     "keep_genes = [x for x in keep_genes if 'Unassigned' not in x]\n",
     "keep_genes = [x for x in keep_genes if 'NegControl' not in x]\n",
     "keep_genes = [x for x in keep_genes if 'DeprecatedCodeword' not in x]\n",
-    "len(keep_genes)\n",
     "\n",
-    "df_sig = df_sig.loc[keep_genes, clusters]\n",
-    "df_sig.shape"
+    "df_sig = df_sig.loc[keep_genes, clusters]"
    ]
   },
   {
@@ -1919,7 +1438,7 @@
    ],
    "source": [
     "dega.pre.save_landscape_parameters(\n",
-    "    'Xenium', \n",
+    "    technology, \n",
     "    path_landscape_files,\n",
     "    'dapi_files',\n",
     "    tile_size=100,\n",
@@ -1953,7 +1472,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.20"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/Pre-process_Xenium_Prime_Human_Skin_FFPE_outs.ipynb b/notebooks/Pre-process_Xenium_Prime_Human_Skin_FFPE_outs.ipynb
index 6617e90f..1b6b047e 100644
--- a/notebooks/Pre-process_Xenium_Prime_Human_Skin_FFPE_outs.ipynb
+++ b/notebooks/Pre-process_Xenium_Prime_Human_Skin_FFPE_outs.ipynb
@@ -2128,7 +2128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.20"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/Segmentation_QC.ipynb b/notebooks/Segmentation_QC.ipynb
new file mode 100644
index 00000000..9752e5df
--- /dev/null
+++ b/notebooks/Segmentation_QC.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8af40919-471f-489c-b99f-925dcee30c87",
+   "metadata": {},
+   "source": [
+    "# Segmentation QC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d28ad6c4-ac99-4494-ad7d-6376a0b3cd18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: ANYWIDGET_HMR=1\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%env ANYWIDGET_HMR=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ba4053d-2df3-4f14-9bd1-5950514306da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# macOS requirement\n",
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "import celldega as dega\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "os.environ['DYLD_LIBRARY_PATH'] = '/opt/homebrew/lib:' + os.environ.get('DYLD_LIBRARY_PATH', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb585b0c-a607-4ce9-bf82-c0325afdfc41",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d08815f6-79bc-4c69-aea7-87c582a8c0b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_paths = [\"data/processed_data/cellpose_default/\",\n",
+    "             \"data/processed_data/cellpose2/\",\n",
+    "             \"data/processed_data/instanseg_default/\",\n",
+    "             \"data/Xenium_Prime_Human_Skin_FFPE_outs/\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6c96667b-f3d1-4dcb-a2e6-797aa2aa8e43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "segmentation metrics calculation completed\n",
+      "segmentation metrics calculation completed\n",
+      "segmentation metrics calculation completed\n",
+      "segmentation metrics calculation completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "for base_path in base_paths:\n",
+    "    dega.qc.qc_segmentation(base_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "831186b0-df2b-4a18-a316-80d3a22f9b92",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9546bb07-ef90-4561-aab4-66ec0a122662",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x900 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "combined_metrics = pd.DataFrame()\n",
+    "\n",
+    "for base_path in base_paths:\n",
+    "    df = pd.read_csv(os.path.join(base_path, \"cell_specific_qc.csv\"))\n",
+    "    combined_metrics = pd.concat([combined_metrics, df], ignore_index=True)\n",
+    "\n",
+    "combined_metrics.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
+    "\n",
+    "metrics = [\"proportion_assigned_transcripts\", \"number_cells\", \"mean_cell_area\"]\n",
+    "segmentation_approaches = combined_metrics[\"segmentation_approach\"].unique()\n",
+    "\n",
+    "bar_width = 0.2\n",
+    "x = np.arange(len(segmentation_approaches))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15, 9))\n",
+    "\n",
+    "sns.set(style='white', rc={'figure.facecolor': 'white', 'axes.facecolor': 'white'})\n",
+    "\n",
+    "# Primary axis for the first metric\n",
+    "ax.bar(\n",
+    "    x, \n",
+    "    combined_metrics[metrics[0]], \n",
+    "    width=bar_width, \n",
+    "    color='#0072B2',  # Colorblind-friendly blue\n",
+    "    label=metrics[0].replace(\"_\", \" \").title()\n",
+    ")\n",
+    "ax.set_ylabel(metrics[0].replace(\"_\", \" \").title(), color='#0072B2', fontsize=23)\n",
+    "ax.tick_params(axis='y', labelcolor='#0072B2')\n",
+    "ax.set_xlabel(\"Segmentation Approaches\", fontsize=23, labelpad=15)\n",
+    "ax.set_xticks(np.arange(0.3, 4.3, 1))\n",
+    "ax.set_xticklabels(segmentation_approaches, rotation=0, fontsize=20)\n",
+    "axes = [ax]  # Store the axes for legend handling\n",
+    "\n",
+    "# Add secondary axes for other metrics\n",
+    "colors = ['#009E73', '#D55E00', '#CC79A7']  # Green, red, and purple\n",
+    "for i, metric in enumerate(metrics[1:], start=1):\n",
+    "    ax_new = ax.twinx()\n",
+    "    ax_new.spines['right'].set_position(('outward', 75 * (i - 1)))  # Shift y-axis outward\n",
+    "    ax_new.bar(\n",
+    "        x + i * bar_width, \n",
+    "        combined_metrics[metric], \n",
+    "        width=bar_width, \n",
+    "        color=colors[i - 1], \n",
+    "        label=metric.replace(\"_\", \" \").title()\n",
+    "    )\n",
+    "    ax_new.set_ylabel(metric.replace(\"_\", \" \").title(), color=colors[i - 1], fontsize=23)\n",
+    "    ax_new.tick_params(axis='y', labelcolor=colors[i - 1])\n",
+    "    axes.append(ax_new)\n",
+    "\n",
+    "# Combine legends from all axes\n",
+    "handles, labels = [], []\n",
+    "for ax in axes:\n",
+    "    h, l = ax.get_legend_handles_labels()\n",
+    "    handles.extend(h)\n",
+    "    labels.extend(l)\n",
+    "\n",
+    "# Create the legend\n",
+    "fig.legend(\n",
+    "    handles, \n",
+    "    labels, \n",
+    "    loc='upper left',  # Adjust placement of legend\n",
+    "    bbox_to_anchor=(1, 1),  # Place the legend outside the plot\n",
+    "    fontsize=14,  # Increase font size for readability\n",
+    "    frameon=True,  # Add a border around the legend\n",
+    "    shadow=False,  # Avoid unnecessary shadowing\n",
+    "    edgecolor=\"black\"  # Define the border color explicitly\n",
+    ")\n",
+    "\n",
+    "# Title and layout adjustments\n",
+    "ax.set_title(\"Xenium Skin Dataset: Metrics for Different Segmentation Approaches\", fontsize=23)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e637642e-915c-406a-9f62-fed0286c34da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ec076d57-f45b-4922-adb7-ba3aaac32040",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T cell specific genes: ['CD3D', 'CD3E', 'CD3G', 'CD4', 'CD8A', 'CD8B', 'TRAC', 'TRBC1', 'TRBC2', 'FOXP3', 'TBX21', 'GATA3', 'LCK', 'ZAP70', 'RORA', 'RUNX3', 'CCR7']\n",
+      "B cell specific genes: ['CD19', 'MS4A1', 'CD79A', 'CD79B', 'PAX5', 'AICDA', 'BLIMP1', 'BCL6', 'CXCR5', 'IGHM', 'IGHG', 'IGHA', 'IGHE', 'IGHD', 'IGKC', 'IGLC']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Lists of genes\n",
+    "cell_A_name = \"T cell\"\n",
+    "\n",
+    "cell_B_name = \"B cell\"\n",
+    "\n",
+    "cell_type_A_specific_genes = [\n",
+    "    \"CD3D\", \"CD3E\", \"CD3G\", \"CD4\", \"CD8A\", \"CD8B\", \n",
+    "    \"TRAC\", \"TRBC1\", \"TRBC2\", \"FOXP3\", \"TBX21\", \n",
+    "    \"GATA3\", \"LCK\", \"ZAP70\", \"RORA\", \"RUNX3\", \"CCR7\"\n",
+    "]\n",
+    "\n",
+    "cell_type_B_specific_genes = [\n",
+    "    \"CD19\", \"MS4A1\", \"CD79A\", \"CD79B\", \"PAX5\", \n",
+    "    \"AICDA\", \"BLIMP1\", \"BCL6\", \"CXCR5\", \"IGHM\", \n",
+    "    \"IGHG\", \"IGHA\", \"IGHE\", \"IGHD\", \"IGKC\", \"IGLC\"\n",
+    "]\n",
+    "\n",
+    "print(f\"{cell_A_name} specific genes:\", cell_type_A_specific_genes)\n",
+    "print(f\"{cell_B_name} specific genes:\", cell_type_B_specific_genes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ded24b17-2525-4380-a551-9fdf2be19bdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading mtx file from  data/Xenium_Prime_Human_Skin_FFPE_outs/cell_feature_matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jishar/anaconda3/envs/celldega_env/lib/python3.9/site-packages/seaborn/axisgrid.py:478: UserWarning: `gridspec_kws` ignored when using `col_wrap`\n",
+      "  warnings.warn(\"`gridspec_kws` ignored when using `col_wrap`\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2250x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jishar/anaconda3/envs/celldega_env/lib/python3.9/site-packages/seaborn/axisgrid.py:478: UserWarning: `gridspec_kws` ignored when using `col_wrap`\n",
+      "  warnings.warn(\"`gridspec_kws` ignored when using `col_wrap`\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2250x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jishar/anaconda3/envs/celldega_env/lib/python3.9/site-packages/seaborn/axisgrid.py:478: UserWarning: `gridspec_kws` ignored when using `col_wrap`\n",
+      "  warnings.warn(\"`gridspec_kws` ignored when using `col_wrap`\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2250x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jishar/anaconda3/envs/celldega_env/lib/python3.9/site-packages/seaborn/axisgrid.py:478: UserWarning: `gridspec_kws` ignored when using `col_wrap`\n",
+      "  warnings.warn(\"`gridspec_kws` ignored when using `col_wrap`\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2250x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4000x2500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dega.qc.orthogonal_expression_calc(base_paths, \n",
+    "                                   cell_type_A_specific_genes, \n",
+    "                                   cell_type_B_specific_genes, \n",
+    "                                   cell_A_name, cell_B_name, \n",
+    "                                   threshold_for_A_cell_classification=5, threshold_for_B_cell_classification=3,\n",
+    "                                   threshold_for_orthogonal_exp=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54ab1225-cbbe-4a7c-9ad3-e5cec9e0b8fb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "celldega_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.20"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/celldega/__init__.py b/src/celldega/__init__.py
index 9bb3ac92..f69a0b81 100644
--- a/src/celldega/__init__.py
+++ b/src/celldega/__init__.py
@@ -2,6 +2,7 @@
 
 from celldega.viz import Landscape, Matrix
 from celldega.pre import landscape
+from celldega.qc import qc_segmentation
 from celldega.nbhd import alpha_shape
 
 from celldega.clust import Network
diff --git a/src/celldega/pre/boundary_tile.py b/src/celldega/pre/boundary_tile.py
index e9f93bb2..6d65dc9b 100644
--- a/src/celldega/pre/boundary_tile.py
+++ b/src/celldega/pre/boundary_tile.py
@@ -1,4 +1,3 @@
-
 import numpy as np
 import pandas as pd
 import os
@@ -7,6 +6,112 @@
 import geopandas as gpd
 from shapely.geometry import Polygon, MultiPolygon
 
+def numpy_affine_transform(coords, matrix):
+    """Apply affine transformation to numpy coordinates."""
+    # Homogeneous coordinates for affine transformation
+    coords = np.hstack([coords, np.ones((coords.shape[0], 1))])
+    transformed_coords = coords @ matrix.T
+    return transformed_coords[:, :2]  # Drop the homogeneous coordinate
+    
+def batch_transform_geometries(geometries, transformation_matrix, scale):
+    """
+    Batch transform geometries using numpy for optimized performance.
+    """
+    # Extract affine transformation parameters into a 3x3 matrix for numpy
+    affine_matrix = np.array([
+        [transformation_matrix[0, 0], transformation_matrix[0, 1], transformation_matrix[0, 2]],
+        [transformation_matrix[1, 0], transformation_matrix[1, 1], transformation_matrix[1, 2]],
+        [0, 0, 1]
+    ])
+    
+    transformed_geometries = []
+    
+    for polygon in geometries:
+        # Extract coordinates and transform them
+        if isinstance(polygon, MultiPolygon):
+            polygon = next(polygon.geoms)  # Use the first geometry
+        
+        # Transform the exterior of the polygon
+        exterior_coords = np.array(polygon.exterior.coords)
+        
+        # Apply the affine transformation and scale
+        transformed_coords = numpy_affine_transform(exterior_coords, affine_matrix) / scale
+        
+        # Append the result to the transformed_geometries list
+        transformed_geometries.append([transformed_coords.tolist()])
+    
+    return transformed_geometries
+
+def filter_and_save_fine_boundary(coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output):
+    cell_ids = coarse_tile.index.values
+    
+    tile_filter = (
+        (coarse_tile["center_x"] >= fine_tile_x_min) & (coarse_tile["center_x"] < fine_tile_x_max) &
+        (coarse_tile["center_y"] >= fine_tile_y_min) & (coarse_tile["center_y"] < fine_tile_y_max)
+    )
+    filtered_indices = np.where(tile_filter)[0]
+
+    keep_cells = cell_ids[filtered_indices]
+    fine_tile_cells = coarse_tile.loc[keep_cells, ["GEOMETRY"]]
+    fine_tile_cells = fine_tile_cells.assign(name=fine_tile_cells.index)
+
+    if not fine_tile_cells.empty:
+        filename = f"{path_output}/cell_tile_{fine_i}_{fine_j}.parquet"
+        fine_tile_cells.to_parquet(filename)
+
+def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=1):
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = []
+        for fine_i in range(n_fine_tiles_x):
+            fine_tile_x_min = x_min + fine_i * tile_size
+            fine_tile_x_max = fine_tile_x_min + tile_size
+
+            if not (fine_tile_x_min >= coarse_tile_x_min and fine_tile_x_max <= coarse_tile_x_max):
+                continue
+
+            for fine_j in range(n_fine_tiles_y):
+                fine_tile_y_min = y_min + fine_j * tile_size
+                fine_tile_y_max = fine_tile_y_min + tile_size
+
+                if not (fine_tile_y_min >= coarse_tile_y_min and fine_tile_y_max <= coarse_tile_y_max):
+                    continue
+
+                futures.append(executor.submit(
+                    filter_and_save_fine_boundary, coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output
+                ))
+
+        for future in futures:
+            future.result()
+
+def get_cell_polygons(technology, path_cell_boundaries, path_output=None, path_meta_cell_micron=None):
+
+    # Load cell boundary data based on the technology
+    if technology == "MERSCOPE":
+        df_meta = pd.read_parquet(f"{path_output.replace('cell_segmentation','cell_metadata.parquet')}")
+        entity_to_cell_id_dict = pd.Series(df_meta.index.values, index=df_meta.EntityID).to_dict()
+        cells_orig = gpd.read_parquet(path_cell_boundaries)
+        cells_orig['cell_id'] = cells_orig['EntityID'].map(entity_to_cell_id_dict)
+        cells_orig = cells_orig[cells_orig["ZIndex"] == 1]
+
+        # Correct cell_id issues with meta_cell
+        meta_cell = pd.read_csv(path_meta_cell_micron)
+        meta_cell['cell_id'] = meta_cell['EntityID'].map(entity_to_cell_id_dict)
+        cells_orig.index = meta_cell[meta_cell["cell_id"].isin(cells_orig['cell_id'])].index
+
+        # Correct 'MultiPolygon' to 'Polygon'
+        cells_orig["geometry"] = cells_orig["Geometry"].apply(
+            lambda x: list(x.geoms)[0] if isinstance(x, MultiPolygon) else x
+        )
+
+        cells_orig.set_index('cell_id', inplace=True)
+
+    elif technology == "Xenium":
+        xenium_cells = pd.read_parquet(path_cell_boundaries)
+        grouped = xenium_cells.groupby("cell_id")[["vertex_x", "vertex_y"]].agg(lambda x: x.tolist())
+        grouped["geometry"] = grouped.apply(lambda row: Polygon(zip(row["vertex_x"], row["vertex_y"])), axis=1)
+        cells_orig = gpd.GeoDataFrame(grouped, geometry="geometry")[["geometry"]]
+
+    return cells_orig
 
 def make_cell_boundary_tiles(
     technology,
@@ -18,7 +123,7 @@ def make_cell_boundary_tiles(
     tile_size=250,
     tile_bounds=None,
     image_scale=1,
-    max_workers=8
+    max_workers=1
 ):
     
 
@@ -47,7 +152,7 @@ def make_cell_boundary_tiles(
         Dictionary containing the minimum and maximum bounds for x and y coordinates.
     image_scale : float, optional, default=1
         Scale factor to apply to the geometry data.
-    max_workers : int, optional, default=8
+    max_workers : int, optional, default=1
         Maximum number of parallel workers for processing tiles.
 
     Returns
@@ -55,117 +160,13 @@ def make_cell_boundary_tiles(
     None
     """
 
-    def numpy_affine_transform(coords, matrix):
-        """Apply affine transformation to numpy coordinates."""
-        # Homogeneous coordinates for affine transformation
-        coords = np.hstack([coords, np.ones((coords.shape[0], 1))])
-        transformed_coords = coords @ matrix.T
-        return transformed_coords[:, :2]  # Drop the homogeneous coordinate
-    
-    def batch_transform_geometries(geometries, transformation_matrix, scale):
-        """
-        Batch transform geometries using numpy for optimized performance.
-        """
-        # Extract affine transformation parameters into a 3x3 matrix for numpy
-        affine_matrix = np.array([
-            [transformation_matrix[0, 0], transformation_matrix[0, 1], transformation_matrix[0, 2]],
-            [transformation_matrix[1, 0], transformation_matrix[1, 1], transformation_matrix[1, 2]],
-            [0, 0, 1]
-        ])
-        
-        transformed_geometries = []
-        
-        for polygon in geometries:
-            # Extract coordinates and transform them
-            if isinstance(polygon, MultiPolygon):
-                polygon = next(polygon.geoms)  # Use the first geometry
-            
-            # Transform the exterior of the polygon
-            exterior_coords = np.array(polygon.exterior.coords)
-            
-            # Apply the affine transformation and scale
-            transformed_coords = numpy_affine_transform(exterior_coords, affine_matrix) / scale
-            
-            # Append the result to the transformed_geometries list
-            transformed_geometries.append([transformed_coords.tolist()])
-        
-        return transformed_geometries
-
-
-    def filter_and_save_fine_boundary(coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output):
-        cell_ids = coarse_tile.index.values
-        
-        tile_filter = (
-            (coarse_tile["center_x"] >= fine_tile_x_min) & (coarse_tile["center_x"] < fine_tile_x_max) &
-            (coarse_tile["center_y"] >= fine_tile_y_min) & (coarse_tile["center_y"] < fine_tile_y_max)
-        )
-        filtered_indices = np.where(tile_filter)[0]
-
-        keep_cells = cell_ids[filtered_indices]
-        fine_tile_cells = coarse_tile.loc[keep_cells, ["GEOMETRY"]]
-        fine_tile_cells = fine_tile_cells.assign(name=fine_tile_cells.index)
-  
-        if not fine_tile_cells.empty:
-            filename = f"{path_output}/cell_tile_{fine_i}_{fine_j}.parquet"
-            fine_tile_cells.to_parquet(filename)
-
-    def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y):
-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
-            futures = []
-            for fine_i in range(n_fine_tiles_x):
-                fine_tile_x_min = x_min + fine_i * tile_size
-                fine_tile_x_max = fine_tile_x_min + tile_size
-
-                if not (fine_tile_x_min >= coarse_tile_x_min and fine_tile_x_max <= coarse_tile_x_max):
-                    continue
-
-                for fine_j in range(n_fine_tiles_y):
-                    fine_tile_y_min = y_min + fine_j * tile_size
-                    fine_tile_y_max = fine_tile_y_min + tile_size
-
-                    if not (fine_tile_y_min >= coarse_tile_y_min and fine_tile_y_max <= coarse_tile_y_max):
-                        continue
-
-                    futures.append(executor.submit(
-                        filter_and_save_fine_boundary, coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output
-                    ))
-
-            for future in futures:
-                future.result()
-
-    tile_size_x = tile_size
-    tile_size_y = tile_size
-
     transformation_matrix = pd.read_csv(path_transformation_matrix, header=None, sep=" ").values
 
-    # Load cell boundary data based on the technology
-    if technology == "MERSCOPE":
-        df_meta = pd.read_parquet(f"{path_output.replace('cell_segmentation','cell_metadata.parquet')}")
-        entity_to_cell_id_dict = pd.Series(df_meta.index.values, index=df_meta.EntityID).to_dict()
-        cells_orig = gpd.read_parquet(path_cell_boundaries)
-        cells_orig['cell_id'] = cells_orig['EntityID'].map(entity_to_cell_id_dict)
-        cells_orig = cells_orig[cells_orig["ZIndex"] == 1]
-
-        # Correct cell_id issues with meta_cell
-        meta_cell = pd.read_csv(path_meta_cell_micron)
-        meta_cell['cell_id'] = meta_cell['EntityID'].map(entity_to_cell_id_dict)
-        cells_orig.index = meta_cell[meta_cell["cell_id"].isin(cells_orig['cell_id'])].index
-
-        # Correct 'MultiPolygon' to 'Polygon'
-        cells_orig["geometry"] = cells_orig["Geometry"].apply(
-            lambda x: list(x.geoms)[0] if isinstance(x, MultiPolygon) else x
-        )
-
-        cells_orig.set_index('cell_id', inplace=True)
-
-    elif technology == "Xenium":
-        xenium_cells = pd.read_parquet(path_cell_boundaries)
-        grouped = xenium_cells.groupby("cell_id")[["vertex_x", "vertex_y"]].agg(lambda x: x.tolist())
-        grouped["geometry"] = grouped.apply(lambda row: Polygon(zip(row["vertex_x"], row["vertex_y"])), axis=1)
-        cells_orig = gpd.GeoDataFrame(grouped, geometry="geometry")[["geometry"]]
+    # Ensure the output directory exists
+    if not os.path.exists(path_output):
+        os.makedirs(path_output)
 
-    elif technology == "custom":
-        cells_orig = gpd.read_parquet(path_cell_boundaries)
+    cells_orig = get_cell_polygons(technology, path_cell_boundaries, path_output, path_meta_cell_micron)
 
     # Transform geometries
     cells_orig["GEOMETRY"] = batch_transform_geometries(cells_orig["geometry"], transformation_matrix, image_scale)
@@ -173,13 +174,10 @@ def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_
     # Convert transformed geometries to polygons and calculate centroids
     cells_orig["polygon"] = cells_orig["GEOMETRY"].apply(lambda x: Polygon(x[0]))
     gdf_cells = gpd.GeoDataFrame(geometry=cells_orig["polygon"])
-    gdf_cells["center_x"] = gdf_cells.geometry.centroid.x
-    gdf_cells["center_y"] = gdf_cells.geometry.centroid.y
     gdf_cells["GEOMETRY"] = cells_orig["GEOMETRY"]
 
-    # Ensure the output directory exists
-    if not os.path.exists(path_output):
-        os.makedirs(path_output)
+    gdf_cells["center_x"] = gdf_cells.geometry.centroid.x
+    gdf_cells["center_y"] = gdf_cells.geometry.centroid.y
 
     # Calculate tile bounds and fine/coarse tiles
     x_min, x_max = tile_bounds["x_min"], tile_bounds["x_max"]
@@ -203,4 +201,4 @@ def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_
                 (gdf_cells["center_y"] >= coarse_tile_y_min) & (gdf_cells["center_y"] < coarse_tile_y_max)
             ]
             if not coarse_tile.empty:
-                process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y)
+                process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)
diff --git a/src/celldega/pre/trx_tile.py b/src/celldega/pre/trx_tile.py
index e140f308..202752e3 100644
--- a/src/celldega/pre/trx_tile.py
+++ b/src/celldega/pre/trx_tile.py
@@ -1,129 +1,82 @@
-
 import numpy as np
 import os
 import polars as pl
 from tqdm import tqdm
 import concurrent.futures
+import pandas as pd
 
-def make_trx_tiles(
-    technology,
-    path_trx,
-    path_transformation_matrix,
-    path_trx_tiles,
-    coarse_tile_factor=10,
-    tile_size=250,
-    chunk_size=1000000,
-    verbose=False,
-    image_scale=1,
-    max_workers=8
-):
-    """
-    Processes transcript data by dividing it into coarse-grain and fine-grain tiles,
-    applying transformations, and saving the results in a parallelized manner.
-
-    Parameters
-    ----------
-    technology : str
-        The technology used for generating the transcript data (e.g., "MERSCOPE" or "Xenium").
-    path_trx : str
-        Path to the file containing the transcript data.
-    path_transformation_matrix : str
-        Path to the file containing the transformation matrix (CSV file).
-    path_trx_tiles : str
-        Directory path where the output files (Parquet files) for each tile will be saved.
-    coarse_tile_factor : int, optional
-        Scaling factor of each coarse-grain tile comparing to the fine tile size.
-    tile_size : int, optional
-        Size of each fine-grain tile in microns (default is 250).
-    chunk_size : int, optional
-        Number of rows to process per chunk for memory efficiency (default is 1000000).
-    verbose : bool, optional
-        Flag to enable verbose output (default is False).
-    image_scale : float, optional
-        Scale factor to apply to the transcript coordinates (default is 0.5).
-    max_workers : int, optional
-        Maximum number of parallel workers for processing tiles (default is 8).
+def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=1):
+    # Filter the entire dataset for the current coarse tile
+    coarse_tile = trx.filter(
+        (pl.col("transformed_x") >= coarse_tile_x_min) & (pl.col("transformed_x") < coarse_tile_x_max) &
+        (pl.col("transformed_y") >= coarse_tile_y_min) & (pl.col("transformed_y") < coarse_tile_y_max)
+    )
 
-    Returns
-    -------
-    dict
-        A dictionary containing the bounds of the processed data in both x and y directions.
-    """
+    if not coarse_tile.is_empty():
+        # Now process fine tiles using global fine tile indices
+        process_fine_tiles(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)   
 
-    def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers):
-        # Filter the entire dataset for the current coarse tile
-        coarse_tile = trx.filter(
-            (pl.col("transformed_x") >= coarse_tile_x_min) & (pl.col("transformed_x") < coarse_tile_x_max) &
-            (pl.col("transformed_y") >= coarse_tile_y_min) & (pl.col("transformed_y") < coarse_tile_y_max)
-        )
-    
-        if not coarse_tile.is_empty():
-            # Now process fine tiles using global fine tile indices
-            process_fine_tiles(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)   
+def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=1):
 
+    # Use ThreadPoolExecutor for parallel processing of fine-grain tiles within the coarse tile
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = []
+        
+        # Iterate over fine-grain tiles within the global bounds
+        for fine_i in range(n_fine_tiles_x):
+            fine_tile_x_min = x_min + fine_i * tile_size
+            fine_tile_x_max = fine_tile_x_min + tile_size
 
-    def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=8):
+            # Process only if the fine tile falls within the current coarse tile's bounds
+            if not (fine_tile_x_min >= coarse_tile_x_min and fine_tile_x_max <= coarse_tile_x_max):
+                continue
 
-        # Use ThreadPoolExecutor for parallel processing of fine-grain tiles within the coarse tile
-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
-            futures = []
-            
-            # Iterate over fine-grain tiles within the global bounds
-            for fine_i in range(n_fine_tiles_x):
-                fine_tile_x_min = x_min + fine_i * tile_size
-                fine_tile_x_max = fine_tile_x_min + tile_size
+            for fine_j in range(n_fine_tiles_y):
+                fine_tile_y_min = y_min + fine_j * tile_size
+                fine_tile_y_max = fine_tile_y_min + tile_size
 
                 # Process only if the fine tile falls within the current coarse tile's bounds
-                if not (fine_tile_x_min >= coarse_tile_x_min and fine_tile_x_max <= coarse_tile_x_max):
+                if not (fine_tile_y_min >= coarse_tile_y_min and fine_tile_y_max <= coarse_tile_y_max):
                     continue
 
-                for fine_j in range(n_fine_tiles_y):
-                    fine_tile_y_min = y_min + fine_j * tile_size
-                    fine_tile_y_max = fine_tile_y_min + tile_size
-
-                    # Process only if the fine tile falls within the current coarse tile's bounds
-                    if not (fine_tile_y_min >= coarse_tile_y_min and fine_tile_y_max <= coarse_tile_y_max):
-                        continue
-
-                    # Submit the task for each fine tile to process in parallel
-                    futures.append(executor.submit(
-                        filter_and_save_fine_tile, coarse_tile, coarse_i, coarse_j, fine_i, fine_j, 
-                        fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles
-                    ))
+                # Submit the task for each fine tile to process in parallel
+                futures.append(executor.submit(
+                    filter_and_save_fine_tile, coarse_tile, coarse_i, coarse_j, fine_i, fine_j, 
+                    fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles
+                ))
 
-            # Wait for all futures to complete
-            for future in concurrent.futures.as_completed(futures):
-                future.result()  # Raise exceptions if any occurred during execution
+        # Wait for all futures to complete
+        for future in concurrent.futures.as_completed(futures):
+            future.result()  # Raise exceptions if any occurred during execution
 
+def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles):
 
-    def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles):
+    # Filter the coarse tile for the current fine tile's boundaries
+    fine_tile_trx = coarse_tile.filter(
+        (pl.col("transformed_x") >= fine_tile_x_min) & (pl.col("transformed_x") < fine_tile_x_max) &
+        (pl.col("transformed_y") >= fine_tile_y_min) & (pl.col("transformed_y") < fine_tile_y_max)
+    )
     
-        # Filter the coarse tile for the current fine tile's boundaries
-        fine_tile_trx = coarse_tile.filter(
-            (pl.col("transformed_x") >= fine_tile_x_min) & (pl.col("transformed_x") < fine_tile_x_max) &
-            (pl.col("transformed_y") >= fine_tile_y_min) & (pl.col("transformed_y") < fine_tile_y_max)
-        )
-        
-        if not fine_tile_trx.is_empty():
-            # Add geometry column as a list of [x, y] pairs
-            fine_tile_trx = fine_tile_trx.with_columns(
-                pl.concat_list([pl.col("transformed_x"), pl.col("transformed_y")]).alias("geometry")
-            ).drop(['transformed_x', 'transformed_y'])
-    
-            # Define the filename based on fine tile coordinates
-            filename = f"{path_trx_tiles}/transcripts_tile_{fine_i}_{fine_j}.parquet"
-    
-            # Save the filtered DataFrame to a Parquet file
-            fine_tile_trx.to_pandas().to_parquet(filename)
+    if not fine_tile_trx.is_empty():
+        # Add geometry column as a list of [x, y] pairs
+        fine_tile_trx = fine_tile_trx.with_columns(
+            pl.concat_list([pl.col("transformed_x"), pl.col("transformed_y")]).alias("geometry")
+        ).drop(['transformed_x', 'transformed_y'])
 
+        # Define the filename based on fine tile coordinates
+        filename = f"{path_trx_tiles}/transcripts_tile_{fine_i}_{fine_j}.parquet"
 
-    # Load transformation matrix
-    transformation_matrix = np.loadtxt(path_transformation_matrix)
+        # Save the filtered DataFrame to a Parquet file
+        fine_tile_trx.to_pandas().to_parquet(filename)
+
+def transform_transcript_coordinates(technology, path_trx, chunk_size, transformation_matrix, image_scale=1):
 
     # Load the transcript data based on the technology using Polars
     if technology == "MERSCOPE":
         trx_ini = pl.read_csv(path_trx, columns=["gene", "global_x", "global_y"])
         trx_ini = trx_ini.with_columns([
+            pl.col("cell_id"),
+            pl.col("transcript_id"),
             pl.col("global_x").alias("x"),
             pl.col("global_y").alias("y"),
             pl.col("gene").alias("name")
@@ -131,6 +84,8 @@ def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, f
 
     elif technology == "Xenium":
         trx_ini = pl.read_parquet(path_trx).select([
+            pl.col("cell_id"),
+            pl.col("transcript_id"),
             pl.col("feature_name").alias("name"),
             pl.col("x_location").alias("x"),
             pl.col("y_location").alias("y")
@@ -155,11 +110,62 @@ def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, f
 
     # Concatenate all chunks after processing
     trx = pl.concat(all_chunks)
+    
+    return trx
+
+def make_trx_tiles(
+    technology,
+    path_trx,
+    path_transformation_matrix,
+    path_trx_tiles,
+    coarse_tile_factor=10,
+    tile_size=250,
+    chunk_size=1000000,
+    verbose=False,
+    image_scale=1,
+    max_workers=1
+):
+    """
+    Processes transcript data by dividing it into coarse-grain and fine-grain tiles,
+    applying transformations, and saving the results in a parallelized manner.
+
+    Parameters
+    ----------
+    technology : str
+        The technology used for generating the transcript data (e.g., "MERSCOPE" or "Xenium").
+    path_trx : str
+        Path to the file containing the transcript data.
+    path_transformation_matrix : str
+        Path to the file containing the transformation matrix (CSV file).
+    path_trx_tiles : str
+        Directory path where the output files (Parquet files) for each tile will be saved.
+    coarse_tile_factor : int, optional
+        Scaling factor of each coarse-grain tile comparing to the fine tile size.
+    tile_size : int, optional
+        Size of each fine-grain tile in microns (default is 250).
+    chunk_size : int, optional
+        Number of rows to process per chunk for memory efficiency (default is 1000000).
+    verbose : bool, optional
+        Flag to enable verbose output (default is False).
+    image_scale : float, optional
+        Scale factor to apply to the transcript coordinates (default is 0.5).
+    max_workers : int, optional
+        Maximum number of parallel workers for processing tiles (default is 1).
+
+    Returns
+    -------
+    dict
+        A dictionary containing the bounds of the processed data in both x and y directions.
+    """
 
     # Ensure the output directory exists
     if not os.path.exists(path_trx_tiles):
         os.makedirs(path_trx_tiles)
 
+    transformation_matrix = np.loadtxt(path_transformation_matrix)
+
+    trx = transform_transcript_coordinates(technology, path_trx, chunk_size, transformation_matrix, image_scale)
+
     # Get min and max x, y values
     x_min, x_max = trx.select([
         pl.col("transformed_x").min().alias("x_min"),
diff --git a/src/celldega/qc/__init__.py b/src/celldega/qc/__init__.py
new file mode 100644
index 00000000..da545a26
--- /dev/null
+++ b/src/celldega/qc/__init__.py
@@ -0,0 +1,311 @@
+import os
+import json
+import pandas as pd
+import numpy as np
+import geopandas as gpd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from shapely.geometry import Polygon, MultiPolygon
+from ..pre.landscape import read_cbg_mtx
+from ..pre.boundary_tile import get_cell_polygons
+
+def get_largest_polygon(geometry):
+    if isinstance(geometry, MultiPolygon):
+        return max(geometry.geoms, key=lambda p: p.area)
+    return geometry
+
+def qc_segmentation(base_path, path_output=None, path_meta_cell_micron=None):
+
+    """
+    Calculate segmentation quality control (QC) metrics for imaging spatial transcriptomics data.
+
+    This function computes QC metrics to assess the quality of cell segmentation and transcript assignment
+    in spatial transcriptomics datasets. Metrics include transcript assignment proportion, cell count,
+    mean cell area, and transcript and gene distribution statistics.
+
+    Parameters
+    ----------
+    base_path : str
+        Path to the data directory
+
+    Returns
+    -------
+    None
+        Outputs two CSV files containing cell-level and gene-specific QC metrics.
+
+    Example
+    -------
+    qc_segmentation(base_path="path/to/data")
+
+    """
+
+    metrics = {}
+
+    try:
+        if os.path.exists(os.path.join(base_path, "segmentation_parameters.json")):
+            with open(os.path.join(base_path, "segmentation_parameters.json"), 'r') as parameter_file:
+                segmentation_parameters = json.load(parameter_file)
+        else:
+            print("segmentation_parameters.json does not exist")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+    if segmentation_parameters['technology'] == 'custom':
+        cell_index = "cell_index"
+        gene = "gene"
+        transcript_index = "transcript_index"
+
+        trx = pd.read_parquet(os.path.join(base_path, "transcripts.parquet"))
+        trx_meta = trx[trx[cell_index] != -1][[transcript_index, cell_index, gene]]
+        cell_gdf = gpd.read_parquet(os.path.join(base_path, "cell_polygons.parquet"))
+        cell_meta_gdf = gpd.read_parquet(os.path.join(base_path, "cell_metadata.parquet"))
+        
+    elif segmentation_parameters['technology'] == 'Xenium':
+        cell_index = "cell_id"
+        gene = "feature_name"
+        transcript_index = "transcript_id"
+
+        trx = pd.read_parquet(os.path.join(base_path, "transcripts.parquet"))
+        trx = trx.rename(columns={'name': gene})
+        trx_meta = trx[trx[cell_index] != 'UNASSIGNED'][[transcript_index, cell_index, gene]]
+        
+        cell_gdf = get_cell_polygons(technology=segmentation_parameters['technology'], 
+                                     path_cell_boundaries=os.path.join(base_path, "cell_boundaries.parquet"))
+        
+        cell_gdf = gpd.GeoDataFrame(geometry=cell_gdf["geometry"])
+        cell_gdf["geometry"] = cell_gdf["geometry"].apply(get_largest_polygon)
+
+        cell_gdf.reset_index(inplace=True)
+        cell_gdf['area'] = cell_gdf['geometry'].area
+        cell_gdf['centroid'] = cell_gdf['geometry'].centroid
+        cell_meta_gdf = cell_gdf[['cell_id', 'area', 'centroid']]
+
+    elif segmentation_parameters['technology'] == 'MERSCOPE':
+        cell_index = 'EntityID'
+        gene = "gene"
+        transcript_index = 'transcript_id'
+        
+        trx = pd.read_csv(os.path.join(base_path, "detected_transcripts.csv"))
+        trx = trx.rename(columns={'name': gene})
+        trx_meta = trx[trx[cell_index] != -1][[transcript_index, cell_index, gene]]
+
+        cell_gdf = get_cell_polygons(technology=segmentation_parameters['technology'], 
+                                     path_cell_boundaries=os.path.join(base_path, "cell_boundaries.parquet"), 
+                                     path_output=path_output,
+                                     path_meta_cell_micron=path_meta_cell_micron)
+        
+        cell_gdf["geometry"] = cell_gdf["Geometry"].apply(get_largest_polygon)
+        cell_gdf.drop(['Geometry'], axis=1, inplace=True)
+        cell_gdf = gpd.GeoDataFrame(geometry=cell_gdf["Geometry"])
+        
+        cell_gdf.reset_index(inplace=True)
+        cell_gdf['area'] = cell_gdf['geometry'].area
+        cell_gdf['centroid'] = cell_gdf['geometry'].centroid
+        cell_meta_gdf = cell_gdf[['cell_id', 'area', 'centroid']]
+
+    percentage_of_assigned_transcripts = (len(trx_meta) / len(trx))
+
+    metrics['dataset_name'] = segmentation_parameters['dataset_name']
+    metrics['segmentation_approach'] = segmentation_parameters['segmentation_approach']
+    
+    metrics['proportion_assigned_transcripts'] = percentage_of_assigned_transcripts
+    metrics['number_cells'] = len(cell_gdf)
+    metrics['mean_cell_area'] = cell_gdf['geometry'].area.mean()
+    
+    metrics['mean_transcripts_per_cell'] = trx_meta.groupby(cell_index).size().mean()
+    metrics['median_transcripts_per_cell'] = trx_meta.groupby(cell_index)[transcript_index].count().median()
+
+    metrics['average_genes_per_cell'] = trx_meta.groupby(cell_index)[gene].nunique().mean()
+    metrics['median_genes_per_cell'] = trx_meta.groupby(cell_index)[gene].nunique().median()
+
+    metrics['proportion_empty_cells'] = ((len(cell_meta_gdf) - len(cell_gdf)) / len(cell_meta_gdf))
+
+    metrics_df = pd.DataFrame([metrics])
+    metrics_df = metrics_df.T
+    metrics_df.columns = [f"{segmentation_parameters['dataset_name']}_{segmentation_parameters['segmentation_approach']}"]
+    metrics_df = metrics_df.T
+    
+    gene_specific_metrics_df = pd.DataFrame({
+        "proportion_of_cells_expressing": (trx_meta.groupby(gene)[cell_index].nunique()) / len(cell_gdf),
+        "average_expression": (trx_meta.groupby(gene)[cell_index].nunique()) / (trx_meta.groupby(gene)[cell_index].nunique().sum()),
+        "assigned_transcripts": (trx_meta.groupby(gene)[transcript_index].count() / trx.groupby(gene)[transcript_index].count()).fillna(0)
+    })
+
+    metrics_df.to_csv(os.path.join(base_path, "cell_specific_qc.csv"))
+    gene_specific_metrics_df.to_csv(os.path.join(base_path, "gene_specific_qc.csv"))
+
+    print("segmentation metrics calculation completed")
+
+def classify_cells(dataframe, cell_A_name, cell_B_name, threshold_for_A_cell_classification, threshold_for_B_cell_classification):
+    dataframe['Classification'] = np.where(
+        dataframe[f'Total {cell_A_name} transcripts'] >= threshold_for_A_cell_classification, cell_A_name,
+        np.where(dataframe[f'Total {cell_B_name} transcripts'] >= threshold_for_B_cell_classification, cell_B_name, 'Orthogonal Expression')
+    )
+    return dataframe
+
+def filter_orthogonal_expression(dataframe, cell_A_name, cell_B_name, threshold_for_orthogonal_exp):
+    A_cells_with_B_genes = dataframe[
+        (dataframe['Classification'] == cell_A_name) &
+        (dataframe[f'Total {cell_B_name} transcripts'] > threshold_for_orthogonal_exp)
+    ]
+    B_cells_with_A_genes = dataframe[
+        (dataframe['Classification'] == cell_B_name) &
+        (dataframe[f'Total {cell_A_name} transcripts'] > threshold_for_orthogonal_exp)
+    ]
+    return len(A_cells_with_B_genes)/len(dataframe[f'Total {cell_A_name} transcripts']), len(B_cells_with_A_genes)/len(dataframe[f'Total {cell_B_name} transcripts'])
+
+def orthogonal_expression_calc(base_paths, cell_type_A_specific_genes, 
+                          cell_type_B_specific_genes, cell_A_name, cell_B_name, threshold_for_A_cell_classification=3, threshold_for_B_cell_classification=3, threshold_for_orthogonal_exp=3, cmap='cividis'):
+    
+    """
+    Analyze and visualize orthogonal expression patterns of cell-type-specific genes across multiple segmentation algorithms.
+
+    This function calculates the overlap of specific genes for two cell types (A and B) within cells across multiple segmentation algorithms. 
+    It then generates a histogram comparing the total transcripts for each cell type in cells that express genes from both cell types.
+
+    Parameters
+    ----------
+    base_path : str
+        Path to the data directory
+
+    cell_type_A_specific_genes : list of str
+        List of genes specific to cell type A.
+
+    cell_type_B_specific_genes : list of str
+        List of genes specific to cell type B.
+
+    cell_A_name : str
+        Name or label for cell type A (used in plot labeling).
+
+    cell_B_name : str
+        Name or label for cell type B (used in plot labeling).
+
+    threshold : int
+        Threshold to perform orthogonal expression quantification.
+
+
+    Returns
+    -------
+    None
+        Displays histograms comparing total transcripts for cell types A and B, grouped by segmentation algorithm.
+
+    Example
+    -------
+    
+    orthogonal_expression_calc(
+        base_path="path/to/data",
+        cell_type_A_specific_genes=["GeneA1", "GeneA2"],
+        cell_type_B_specific_genes=["GeneB1", "GeneB2"],
+        cell_A_name="CellTypeA",
+        cell_B_name="CellTypeB"
+    )
+    
+    """
+        
+    cbg_dict = {}
+
+    cell_A_with_B_cell_specific_genes = {}
+    cell_B_with_A_cell_specific_genes = {}
+
+    for base_path in base_paths:
+
+        with open(os.path.join(base_path, "segmentation_parameters.json"), 'r') as parameter_file:
+            segmentation_parameters = json.load(parameter_file)
+
+            if segmentation_parameters['technology'] == 'custom':
+                cbg_dict[segmentation_parameters['segmentation_approach']] = pd.read_parquet(os.path.join(base_path, 
+                                                                                                    "cell_by_gene_matrix.parquet"))
+            elif segmentation_parameters['technology'] == 'Xenium':
+                cbg_dict[segmentation_parameters['segmentation_approach']] = read_cbg_mtx(os.path.join(base_path, "cell_feature_matrix"))
+                
+            elif segmentation_parameters['technology'] == 'MERSCOPE':
+                cbg_dict[segmentation_parameters['segmentation_approach']] = pd.read_csv(os.path.join(base_path, 
+                                                                                                    "cell_by_gene_matrix.csv"))
+
+    for algorithm_name, cbg in cbg_dict.items():
+
+        A_cell_overlap = [gene for gene in cell_type_A_specific_genes if gene in cbg.columns]
+        B_cell_overlap = [gene for gene in cell_type_B_specific_genes if gene in cbg.columns]
+        
+        cells_with_A_genes = cbg[A_cell_overlap].sum(axis=1) > 0
+        cells_with_B_genes = cbg[B_cell_overlap].sum(axis=1) > 0
+        
+        cells_with_both = cbg[cells_with_A_genes & cells_with_B_genes]
+        
+        A_cell_genes_expressed = cells_with_both[A_cell_overlap].apply(
+            lambda row: {gene: int(row[gene]) for gene in row[row > 0].index}, axis=1
+        )
+        
+        B_cell_genes_expressed = cells_with_both[B_cell_overlap].apply(
+            lambda row: {gene: int(row[gene]) for gene in row[row > 0].index}, axis=1
+        )
+        
+        results = pd.DataFrame({
+            f"{cell_A_name} genes and transcripts": A_cell_genes_expressed,
+            f"{cell_B_name} genes and transcripts": B_cell_genes_expressed
+        }, index=cells_with_both.index)
+        
+        results[f"Total {cell_A_name} transcripts"] = A_cell_genes_expressed.apply(lambda x: sum(x.values()))
+        results[f"Total {cell_B_name} transcripts"] = B_cell_genes_expressed.apply(lambda x: sum(x.values()))
+        
+        results["Total"] = A_cell_genes_expressed.apply(lambda x: sum(x.values())) + B_cell_genes_expressed.apply(lambda x: sum(x.values()))
+        results['Technology'] = algorithm_name
+        
+        sns.set(style='white', rc={'figure.dpi': 250, 'axes.facecolor': (0, 0, 0, 0), 'figure.facecolor': (0, 0, 0, 0)})
+    
+        height_of_each_facet = 3
+        aspect_ratio_of_each_facet = 1
+        
+        g = sns.FacetGrid(results, col="Technology", sharex=False, sharey=False,
+                        margin_titles=True, despine=True, col_wrap=4,
+                        height=height_of_each_facet, aspect=aspect_ratio_of_each_facet,
+                        gridspec_kws={"wspace": 0.01})
+        
+        g.map_dataframe(
+            lambda data, **kwargs: sns.histplot(
+                data=data,
+                x=f"Total {cell_A_name} transcripts",
+                y=f"Total {cell_B_name} transcripts",
+                bins=15,
+                cbar=True,
+                cmap=cmap,
+                vmin=1,
+                vmax=data[f"Total {cell_A_name} transcripts"].max(),
+                **kwargs
+            )
+        )
+        
+        g.set_axis_labels(f"Total {cell_A_name} transcripts", f"Total {cell_B_name} transcripts")
+        for ax in g.axes.flat:
+            ax.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
+            ax.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
+            ax.tick_params(axis='both', which='major', labelsize=8)
+        
+        plt.tight_layout()
+        plt.show()
+
+        results = classify_cells(results, cell_A_name, cell_B_name, threshold_for_A_cell_classification, threshold_for_B_cell_classification)
+        cell_A_with_B_cell_specific_genes[algorithm_name], cell_B_with_A_cell_specific_genes[algorithm_name] = filter_orthogonal_expression(results, cell_A_name, cell_B_name, threshold_for_orthogonal_exp)
+
+    orthogonal_data = pd.DataFrame({
+        'Technology': [i for i in cell_A_with_B_cell_specific_genes.keys() for _ in range(2)],
+        'Category': [f'{cell_A_name} with {cell_B_name} genes', f'{cell_B_name} with {cell_A_name} genes'] * 4,
+        'Count': [gene for pair in zip(cell_A_with_B_cell_specific_genes.values(), 
+                                        cell_B_with_A_cell_specific_genes.values()) 
+                   for gene in pair]
+    })
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    sns.barplot(data=orthogonal_data, x='Technology', y='Count', hue='Category', ax=ax)
+
+    ax.set_title(f'Orthogonal Expression: Classified {cell_A_name} and {cell_B_name} Expressing Opposite Gene Type', fontsize=15)
+    ax.set_xlabel('Technology', fontsize=15, labelpad=10)
+    ax.set_ylabel('Proportion of Cells', fontsize=15, labelpad=10)
+    plt.yticks(fontsize=15)
+    plt.xticks(fontsize=15)
+
+    ax.legend(title='Category', title_fontsize=15, bbox_to_anchor=(1.05, 1), loc='upper left', facecolor="white", edgecolor="black", fontsize=15)
+
+    plt.tight_layout()
+    plt.show()
\ No newline at end of file