Fix2

README.md

@@ -92,4 +92,3 @@ pytest
 ## General outline of SPACEc analysis
 
 ![SPACEc](https://github.com/yuqiyuqitan/SPACEc/tree/master/docs/overview.png?raw=true "")
-

notebooks/0_tissue_extractor.ipynb

@@ -74,7 +74,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/GitHub/SPACEc/\" # replace with your path\n",
+    "root_path = \"/home/user/path/SPACEc/\" # replace with your path\n",
     "data_path = root_path + 'example_data/raw/' # where the data is stored\n",
     "\n",
     "# where you want to store the output\n",

notebooks/1_cell_segmentation.ipynb

@@ -77,11 +77,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/GitHub/SPACEc/\"\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "data_path = root_path + 'example_data/raw/' # where the data is stored\n",
     "\n",
     "# where you want to store the output\n",
-    "output_dir = root_path + 'example_data/output/'\n",
+    "output_dir = root_path + 'example_data/output/' # inset your own path\n",
     "os.makedirs(output_dir, exist_ok=True)"
    ]
   },

notebooks/2_preprocessing.ipynb

@@ -57,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/GitHub/SPACEc/\" # replace with your path\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "data_path = root_path + 'example_data/raw/' # where the data is stored\n",
     "\n",
     "# where you want to store the output\n",
@@ -399,7 +399,7 @@
     "# Identify the lowest 1% for cell size and nuclear marker intensity to get a better idea of potential segmentation artifacts.\n",
     "df_filt = sp.pp.filter_data(\n",
     "    df_seg, \n",
-    "    nuc_thres=1, # remove cells with DAPI intensity below threshold\n",
+    "    nuc_thres=one_percent_nuc, # remove cells with DAPI intensity below threshold\n",
     "    size_thres=one_percent_area, # remove cells with area below threshold\n",
     "    nuc_marker=\"DAPI\", # name of nuclear marker\n",
     "    cell_size = \"area\", # name of cell size column\n",

notebooks/3_cell_annotation_STELLAR.ipynb

@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/GitHub/SPACEc/\" # replace with your path\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "data_path = root_path + 'example_data/raw/' # where the data is stored\n",
     "\n",
     "# where you want to store the output\n",

notebooks/3_cell_annotation_ml.ipynb

@@ -50,7 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/GitHub/SPACEc/\" # replace with your path\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "data_path = root_path + 'example_data/raw/' # where the data is stored\n",
     "\n",
     "# where you want to store the output\n",

notebooks/3_clustering.ipynb

@@ -65,7 +65,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/GitHub/SPACEc/\" # replace with your path\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "data_path = root_path + 'example_data/raw/' # where the data is stored\n",
     "\n",
     "# where you want to store the output\n",

notebooks/4_cell_neighborhood_analysis.ipynb

@@ -55,7 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/SPACEc_Apr_2024/\"\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "\n",
     "data_path = root_path + 'data/' # where the data is stored\n",
     "\n",

notebooks/5_distance_permutation_analysis.ipynb

@@ -62,7 +62,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/SPACEc_Apr_2024/\"\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "\n",
     "data_path = root_path + 'data/' # where the data is stored\n",
     "\n",

notebooks/6_patch_proximity_analysis.ipynb

@@ -59,7 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/SPACEc_Apr_2024/\" \n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "\n",
     "data_path = root_path + 'data/' # where the data is stored\n",
     "\n",
@@ -99,7 +99,7 @@
     }
    ],
    "source": [
-    "adata = sc.read('/home/tim/Dokumente/GitHub/SPACEc/tests/data/processed/tonsil/1/adata_nn_demo_annotated_long.h5ad')\n",
+    "adata = sc.read(output_dir + 'adata_nn_demo_annotated.h5ad')\n",
     "adata"
    ]
   },

notebooks/7_TissUUmaps.ipynb

@@ -56,7 +56,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "root_path = \"/home/tim/Dokumente/SPACEc_Apr_2024/\"\n",
+    "root_path = \"/home/user/path/SPACEc/\" # inset your own path\n",
     "\n",
     "data_path = root_path + 'data/' # where the data is stored\n",
     "\n",
@@ -353,7 +353,7 @@
     "image_list, csv_paths = sp.tl.tm_viewer(\n",
     "    adata,\n",
     "    images_pickle_path= output_dir + 'seg_output_tonsil2.pickle',\n",
-    "    directory = \"/home/tim/Dokumente/SPACEc_Apr_2024/cache\",\n",
+    "    directory = \"/home/user/path/SPACEc/cache\" # inset your own path where you want to cache your images for TM visualization (you can delete this once you are done with TM)\n",
     "    region_column = \"unique_region\",\n",
     "    region = \"reg002\",\n",
     "    xSelector = \"y\",\n",

src/spacec/_shared/segmentation.py

@@ -5,6 +5,23 @@
 
 
 def create_multichannel_tiff(input_dir, output_dir, output_filename):
+    """
+    Create a multi-channel TIFF image from individual TIFF files.
+
+    Parameters
+    ----------
+    input_dir : str
+        Directory containing the input TIFF files.
+    output_dir : str
+        Directory to save the output TIFF file.
+    output_filename : str
+        Name of the output TIFF file.
+
+    Returns
+    -------
+    list of str
+        List of channel names.
+    """
     # Get a list of all TIFF files in the input directory
     tiff_files = [f for f in os.listdir(input_dir) if f.endswith((".tiff", ".tif"))]
 
@@ -35,18 +52,36 @@ def create_multichannel_tiff(input_dir, output_dir, output_filename):
 
 # combine multiple channels in one image and add as new image to image_dict with the name segmentation_channel
 def combine_channels(image_dict, channel_list, new_channel_name):
+    """
+    Combine multiple channels into a single channel.
+
+    Parameters
+    ----------
+    image_dict : dict
+        Dictionary with channel names as keys and images as values.
+    channel_list : list of str
+        List of channel names to combine.
+    new_channel_name : str
+        Name of the new channel.
+
+    Returns
+    -------
+    dict
+        Updated dictionary with the new channel added.
+    """
+    # Determine bit depth of input images
+    bit_depth = image_dict[channel_list[0]].dtype
+
     # Create empty image
     new_image = np.zeros(
-        (image_dict[channel_list[0]].shape[0], image_dict[channel_list[0]].shape[1])
+        (image_dict[channel_list[0]].shape[0], image_dict[channel_list[0]].shape[1]),
+        dtype=bit_depth,
     )
 
     # Add channels to image as maximum projection
     for channel in channel_list:
         new_image = np.maximum(new_image, image_dict[channel])
 
-    # generate greyscale image
-    new_image = np.uint8(new_image)
-
     # Add image to image_dict
     image_dict[new_channel_name] = new_image
 
@@ -62,6 +97,31 @@ def format_CODEX(
     stack=True,
     input_format="Multichannel",
 ):
+    """
+    Format images based on the input format.
+
+    Parameters
+    ----------
+    image : ndarray or str
+        Input image or directory containing images.
+    channel_names : list of str, optional
+        List of channel names.
+    number_cycles : int, optional
+        Number of cycles in the CODEX format.
+    images_per_cycle : int, optional
+        Number of images per cycle in the CODEX format.
+    stack : bool, default=True
+        If True, stack the images in the list.
+    input_format : str, default="Multichannel"
+        Format of the input images. Options are "CODEX", "Multichannel", and "Channels".
+
+    Returns
+    -------
+    dict
+        Dictionary with channel names as keys and images as values. If `stack` is True and `input_format` is "CODEX",
+        also returns a stacked image as a numpy array.
+    """
+
     if input_format == "CODEX":
         total_images = number_cycles * images_per_cycle
         image_list = [None] * total_images  # pre-allocated list

src/spacec/helperfunctions/_general.py

@@ -1053,6 +1053,18 @@ def is_dark(color):
 
 
 def check_for_gpu():
+    """
+    Check if a GPU is available for use by TensorFlow and PyTorch.
+
+    This function checks if a GPU is available for use by TensorFlow and PyTorch.
+    It prints a message indicating whether a GPU is available for each library,
+    and returns a boolean indicating whether a GPU is available for PyTorch.
+
+    Returns
+    -------
+    bool
+        True if a GPU is available for PyTorch, False otherwise.
+    """
     if tf.config.list_physical_devices("GPU"):
         print("GPU is available to Tensorflow")
     else:
@@ -1061,3 +1073,4 @@ def check_for_gpu():
     use_GPU = use_gpu()
     yn = ["GPU is not available to Pytorch", "GPU is available to Pytorch"]
     print(f"{yn[use_GPU]}")
+    return use_GPU

src/spacec/plotting/_general.py

@@ -3816,6 +3816,34 @@ def cn_map(
     output_dir="./",
     rand_seed=1,
 ):
+    """
+    Generates a CNMap plot using the provided data and parameters.
+
+    Parameters
+    ----------
+    adata : anndata.AnnData
+        Annotated data matrix.
+    cnmap_dict : dict
+        Dictionary containing graph, tops, e0, e1, and simp_freqs.
+    cn_col : str
+        Column name in adata to be used for color coding.
+    palette : dict, optional
+        Color palette to use for the plot. If None, a random color palette is generated.
+    figsize : tuple, optional
+        Size of the figure. Defaults to (40, 20).
+    savefig : bool, optional
+        Whether to save the figure or not. Defaults to False.
+    output_fname : str, optional
+        The filename for the saved figure. Required if savefig is True. Defaults to "".
+    output_dir : str, optional
+        The directory where the figure will be saved. Defaults to "./".
+    rand_seed : int, optional
+        Seed for random number generator. Defaults to 1.
+
+    Returns
+    -------
+    None
+    """
     graph = cnmap_dict["g"]
     tops = cnmap_dict["tops"]
     e0 = cnmap_dict["e0"]
@@ -3854,6 +3882,46 @@ def cn_map(
             c=col,
             zorder=-1,
         )
+        # Dummy scatter plots for legend
+        freqs = simp_freqs * 10000
+        max_size = max(freqs)
+        sizes = [
+            round(max_size) / 4,
+            round(max_size) / 2,
+            round(max_size),
+        ]  # Replace with the sizes you want in the legend
+        labels = [
+            str(round(max_size / 100) / 4) + "%",
+            str(round(max_size / 100) / 2) + "%",
+            str(round(max_size / 100)) + "%",
+        ]  # Replace with the labels you want in the legend
+
+        # Add legend
+        legend_elements = [
+            plt.Line2D(
+                [0],
+                [0],
+                marker="o",
+                color="w",
+                label=label,
+                markerfacecolor="black",
+                markersize=size**0.5,
+            )
+            for size, label in zip(sizes, labels)
+        ]
+
+        # Add first legend
+        legend1 = plt.legend(
+            handles=legend_elements,
+            loc="lower right",
+            title="Total frequency",
+            title_fontsize=30,
+            fontsize=30,
+            handlelength=6,
+            handletextpad=1,
+            bbox_to_anchor=(0.0, -0.15, 1.0, 0.102),
+        )
+
         if n in tops:
             plt.text(
                 pos[n][0],
@@ -3903,10 +3971,11 @@ def cn_map(
     ]
 
     # Add legend to bottom of plot
+    plt.gca().add_artist(legend1)
     plt.legend(
         handles=legend_patches,
         bbox_to_anchor=(0.0, -0.15, 1.0, 0.102),
-        loc="lower center",
+        loc="lower left",
         ncol=3,
         borderaxespad=0.0,
         fontsize=35,

src/spacec/plotting/_segmentation.py

@@ -13,7 +13,9 @@
 def segmentation_ch(
     file_name,  # image for segmentation
     channel_file,  # all channels used for staining
-    output_dir,  #
+    output_dir,
+    savefig=False,  # new
+    output_fname="",  # new
     extra_seg_ch_list=None,  # channels used for membrane segmentation
     nuclei_channel="DAPI",
     input_format="Multichannel",  # CODEX or Phenocycler --> This depends on the machine you are using and the resulting file format (see documentation above)
@@ -68,7 +70,18 @@ def segmentation_ch(
     ax[1].imshow(image_dict["segmentation_channel"])
     ax[0].set_title("nuclei")
     ax[1].set_title("membrane")
-    plt.show()
+
+    # save or plot figure
+    if savefig:
+        plt.savefig(
+            output_dir + output_fname + ".pdf",
+            format="pdf",
+            dpi=300,
+            transparent=True,
+            bbox_inches="tight",
+        )
+    else:
+        plt.show()
 
 
 def show_masks(