spacec.plotting package

Module contents

spacec.plotting.BC_projection(adata, cnmap_dict, cn_col, plot_list, cn_col_annt=None, palette=None, figsize=(7, 7), rand_seed=1, SMALL_SIZE=14, MEDIUM_SIZE=16, BIGGER_SIZE=18, n_num=None, threshold=None, savefig=False, output_fname='', output_dir='', dpi=300)[source]

Plot barycentric projection.

Parameters:

adata (AnnData) – Annotated data object.
cnmap_dict (dict) – Dictionary containing keys ‘w’, ‘l’, ‘k’, and ‘threshold’.
cn_col (str) – Column name in adata.obs containing copy number information.
plot_list (list) – List of 3 elements from cn_col.
cn_col_annt (str, optional) – Annotated column name, by default None.
palette (dict, optional) – Color palette, by default None.
figsize (tuple, optional) – Figure size, by default (7, 7).
rand_seed (int, optional) – Random seed, by default 1.
SMALL_SIZE (int, optional) – Font size for small text, by default 14.
MEDIUM_SIZE (int, optional) – Font size for medium text, by default 16.
BIGGER_SIZE (int, optional) – Font size for large text, by default 18.
n_num (int, optional) – Number, by default None.
threshold (float, optional) – Threshold, by default None.
savefig (bool, optional) – Whether to save the figure, by default False.
output_fname (str, optional) – Output file name, by default “”.
output_dir (str, optional) – Output directory, by default “”.
dpi (int, optional) – Dots per inch, by default 300.

spacec.plotting.catplot(adata, color, unique_region, subset=None, X='x', Y='y', invert_y=False, size=6, alpha=1, palette=None, savefig=False, output_dir='./', output_fname='', figsize=5, style='white', axis='on', scatter_kws={}, n_columns=4, legend_padding=0.2, rand_seed=1)[source]

Plots cells in tissue section color coded by either cell type or node allocation.

Parameters:

adata (AnnData) – Annotated data matrix.
color (str) – Color by “Clusterid” or “Node” respectively.
unique_region (str) – Each region is one independent CODEX image.
subset (str, optional) – Subset of data to plot. If None, all data is plotted.
X (str, optional) – Column name for x-axis in the DataFrame.
Y (str, optional) – Column name for y-axis in the DataFrame.
invert_y (bool, optional) – If True, invert y-axis.
size (int, optional) – Size of point to plot for each cell.
alpha (float, optional) – Transparency of points.
palette (dict, optional) – Colors to use for different levels of the hue variable. Should be something that can be interpreted by color_palette(), or a dictionary mapping hue levels to matplotlib colors.
savefig (bool, optional) – If True, save figure.
output_dir (str, optional) – Directory to save figure.
output_fname (str, optional) – Filename to save figure.
figsize (int, optional) – Size of the figure.
style (str, optional) – Style of the plot.
axis (str, optional) – If “off”, axis is not displayed.
scatter_kws (dict, optional) – Additional keyword arguments to pass to scatterplot().
n_columns (int, optional) – Number of columns in the figure.
legend_padding (float, optional) – Padding around the legend.
rand_seed (int, optional) – Seed for random number generator.

Returns:

This function doesn’t return anything. It plots a scatterplot with the specified parameters.

Return type:

None

spacec.plotting.cn_exp_heatmap(adata, cluster_col, cn_col, palette=None, savefig=False, output_fname='', output_dir='./', row_clus=True, col_clus=True, rand_seed=1)[source]

Create a heatmap of expression data, clustered by rows and columns.

Parameters:

adata (AnnData) – Annotated data matrix.
cluster_col (str) – The column name for clustering the data.
cn_col (str) – The column name for the color selection.
palette (dict, optional) – A dictionary to manually set colors for neighborhoods. Defaults to None.
figsize (tuple, optional) – The size of the figure. Defaults to (18, 12).
savefig (bool, optional) – Whether to save the figure or not. Defaults to False.
output_fname (str, optional) – The output file name. Defaults to “”.
output_dir (str, optional) – The output directory. Defaults to “./”.
row_clus (bool, optional) – Whether to cluster the rows or not. Defaults to True.
col_clus (bool, optional) – Whether to cluster the columns or not. Defaults to True.
rand_seed (int, optional) – The random seed for color generation. Defaults to 1.

Return type:

None

spacec.plotting.cn_map(adata, cnmap_dict, cn_col, palette=None, figsize=(40, 20), savefig=False, output_fname='', output_dir='./', rand_seed=1)[source]

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.

Return type:

None

spacec.plotting.coordinates_on_image(df, overlay_data, color=None, x='x', y='y', fig_width=20, fig_height=20, dot_size=10, convert_to_grey=True, scale=False, cmap='inferno', savefig=False, output_dir='./', output_fname='')[source]

Plot coordinates on an image.

Parameters:

df (pd.DataFrame) – The input DataFrame. The DataFrame should have columns ‘x’ and ‘y’ representing the coordinates.
overlay_data (ndarray) – The image data to overlay the coordinates on.
color (str, optional) – The column name in df for the color variable. Defaults to None.
x (str, optional) – The column name in df for the x-coordinate. Defaults to “x”.
y (str, optional) – The column name in df for the y-coordinate. Defaults to “y”.
fig_width (int, optional) – The width of the figure. Defaults to 20.
fig_height (int, optional) – The height of the figure. Defaults to 20.
dot_size (int, optional) – The size of the dots representing the coordinates. Defaults to 10.
convert_to_grey (bool, optional) – Whether to convert the image to grayscale. Defaults to True.
scale (bool, optional) – Whether to scale the color variable. Defaults to False.
cmap (str, optional) – The colormap to use. Defaults to “inferno”.
savefig (bool, optional) – Whether to save the figure or not. Defaults to False.
output_dir (str, optional) – The output directory. Defaults to “./”.
output_fname (str, optional) – The output file name. Defaults to “”.

Return type:

None

spacec.plotting.count_patch_proximity_res(adata, x, hue, palette='Set3', order=True, key_name='ppa_result', savefig=False, output_dir='./', output_fname='')[source]

Create a count plot for patch proximity results.

Parameters:

adata (AnnData) – Annotated data matrix.
x (str) – The column name in the DataFrame for the x-axis variable.
hue (str) – The column name in the DataFrame for the hue variable.
palette (str, optional) – The palette to use for the plot. Defaults to “Set3”.
order (bool, optional) – Whether to order the count plot. Defaults to True.
key_name (str, optional) – The key name for the patch proximity results in adata.uns. Defaults to “ppa_result”.
savefig (bool, optional) – Whether to save the figure or not. Defaults to False.
output_dir (str, optional) – The output directory. Defaults to “./”.
output_fname (str, optional) – The output file name. Defaults to “”.

Return type:

None

spacec.plotting.create_pie_charts(adata, color, grouping, plot_order=None, show_percentages=True, palette=None, savefig=False, output_fname='', output_dir='./', rand_seed=1)[source]

Create pie charts for each group based on a grouping column, showing the percentage of total rows based on a count column.

Parameters:

adata (pd.DataFrame) – The input DataFrame.
color (str) – The column name used for counting occurrences.
grouping (str) – The column name for grouping the data.
plot_order (list, optional) – The order of groups for plotting. Defaults to None.
show_percentages (bool, optional) – Whether to show the percentage numbers on the pie charts. Defaults to True.
palette (dict, optional) – A dictionary to manually set colors for neighborhoods. Defaults to None.
savefig (bool, optional) – Whether to save the figure or not. Defaults to False.
output_fname (str, optional) – The output file name. Defaults to “”.
output_dir (str, optional) – The output directory. Defaults to “./”.
rand_seed (int, optional) – The random seed for color generation. Defaults to 1.

Return type:

None

spacec.plotting.distance_graph(dist_table, distance_pvals, palette=None, condition_pair=None, interaction_col='interaction', condition_col='condition', logfold_group_col='logfold_group', celltype1_col='celltype1', celltype2_col='celltype2', pair_col='pairs', with_labels=True, node_size=910, font_size=7, multiplication_factor=10, savefig=False, output_fname='', output_dir='', dpi=300, color_seed=0)[source]

Generates a distance graph from a dataframe.

Parameters: df (DataFrame): The input dataframe. palette (dict, optional): A dictionary mapping nodes to colors. If None, nodes are colored ‘lightgrey’. condition_pair (list, optional): A list of two conditions to compare. If None, uses unique conditions from the dataframe. interaction_col (str, optional): The name of the interaction column in the dataframe. Defaults to ‘interaction’. condition_col (str, optional): The name of the condition column in the dataframe. Defaults to ‘condition’. logfold_group_col (str, optional): The name of the logfold group column in the dataframe. Defaults to ‘logfold_group’. celltype1_col (str, optional): The name of the first cell type column in the dataframe. Defaults to ‘celltype1’. celltype2_col (str, optional): The name of the second cell type column in the dataframe. Defaults to ‘celltype2’. with_labels (bool, optional): Whether to draw labels on the nodes. Defaults to True. node_size (int, optional): The size of the nodes. Defaults to 910. font_size (int, optional): The font size for the labels. Defaults to 7.

Returns: None: The function generates a plot and does not return anything.

spacec.plotting.dumbbell(data, figsize=(10, 10), colors=['#DB444B', '#006BA2'], savefig=False, output_fname='', output_dir='./')[source]

Create a dumbbell plot.

Parameters:

data (pd.DataFrame) – The input DataFrame. The DataFrame should have two columns representing two different conditions.
figsize (tuple, optional) – The size of the figure. Defaults to (10, 10).
colors (list, optional) – The colors to use for the two conditions. Defaults to [“#DB444B”, “#006BA2”].

Return type:

None

spacec.plotting.plot_top_n_distances(dist_table_filt, dist_data_filt, n=5, colors=None, dodge=False, savefig=False, output_fname='', output_dir='./', figsize=(5, 5), unit='px', errorbars=True)[source]

spacec.plotting.ppa_res_donut(adata, cat_col, palette=None, key_names='ppa_result', radii=[1, 2, 3, 4, 5], unit='µm', figsize=(10, 10), add_guides=True, text='example CN', label_color='black', rand_seed=1, subset_column=None, subset_condition=None, title='Title', savefig=False, output_fname='', output_dir='./')[source]

spacec.plotting.segmentation_ch(file_name, channel_file, output_dir, savefig=False, output_fname='', extra_seg_ch_list=None, nuclei_channel='DAPI', input_format='Multichannel')[source]

Plot the channel selected for segmentation.

Parameters:

file_name (str) – The path to the image file for segmentation.
channel_file (str) – The path to the file containing all channels used for staining.
output_dir (str) – The directory to save the output in.
extra_seg_ch_list (list, optional) – The channels used for membrane segmentation, by default None.
nuclei_channel (str, optional) – The channel used for nuclei, by default “DAPI”.
input_format (str, optional) – The input_format used (either “CODEX”, “Multichannel” or channels), by default “Multichannel”.

Return type:

None

spacec.plotting.show_masks(seg_output, nucleus_channel, additional_channels=None, show_subsample=True, n=2, tilesize=100, idx=0, rand_seed=1)[source]

Visualize the segmentation results of an image.

Parameters:

seg_output (dict) – The output from the segmentation process. It should contain ‘image_dict’ and ‘masks’.
nucleus_channel (str) – The name of the nucleus channel in the image_dict.
additional_channels (list of str, optional) – The names of additional channels to be combined with the nucleus channel for visualization.
show_subsample (bool, optional) – Whether to show a subsample of the image. Default is True.
n (int, optional) – The number of subsamples to show. Default is 2.
tilesize (int, optional) – The size of the tiles for subsampling. Default is 100.
idx (int, optional) – The index for displaying. Default is 0.
rand_seed (int, optional) – The seed for the random number generator. Default is 1.

Returns:

overlay_data (ndarray) – The overlay of the segmentation results on the RGB images.
rgb_images (ndarray) – The RGB images.

Raises:

ValueError – If the image size is smaller than the tile size or if there are not enough tiles to display.

spacec.plotting.stacked_bar_plot(adata, color, grouping, cell_list, output_dir, norm=True, savefig=False, output_fname='', col_order=None, sub_col=None, name_cat='celltype', fig_sizing=(8, 4), plot_order=None, palette=None, remove_leg=False, rand_seed=1)[source]

Plot a stacked bar plot based on the given data.

Parameters:

data (pandas.DataFrame) – The input data containing the necessary information for plotting.
color (str) – The column name representing the categories.
grouping (str) – The column name representing the grouping.
cell_list (list) – The list of cell types to include in the plot.
output_dir (str) – The output directory for saving the plot.
norm (bool, optional) – Flag indicating whether to normalize the values. Defaults to True.
save_name (str, optional) – The name to use when saving the plot. Defaults to None.
col_order (list, optional) – The order of columns/categories for plotting. Defaults to None.
sub_col (str, optional) – The column name representing sub-categories. Defaults to None.
name_cat (str, optional) – The name for the category column in the plot. Defaults to ‘celltype’.
fig_sizing (tuple, optional) – The size of the figure (width, height) in inches. Defaults to (8, 4).
plot_order (list, optional) – The order of categories for plotting. Defaults to None.
palette (dict, optional) – A dictionary mapping categories to colors for custom colorization. Defaults to None.
remove_leg (bool, optional) – Flag indicating whether to remove the legend. Defaults to False.

Returns:

pandas.DataFrame – The pivoted data used for plotting.
list – The order of categories used for plotting.

spacec.plotting.tissue_lables(tissueframe, region='region1')[source]

Plot the tissue and region labels of the given DataFrame.

Parameters:

tissueframe (DataFrame) – The DataFrame containing the labels from the segmentation.
region (str, optional) – The region to group by, by default “region1”.

Return type:

None

spacec.plotting.zcount_thres(dfz, col_num, cut_off=0.01, count_bin=50, zsum_bin=50, figsize=(10, 5))[source]

Determines the threshold to use for removing noises. The default cut off is the top 1%.

Parameters:

dfz (DataFrame) – The input data from which the threshold is to be determined.
col_num (int) – The column number up to which the operation is performed.
cut_off (float, optional) – The cut off percentage for the threshold. By default, it is 0.01 (1%).
count_bin (int, optional) – The number of bins for the count histogram. By default, it is 50.
zsum_bin (int, optional) – The number of bins for the z-score sum histogram. By default, it is 50.
figsize (tuple, optional) – The size of the figure to be plotted. By default, it is (10, 5).

Returns:

This function doesn’t return anything. It plots two histograms for ‘Count’ and ‘Zscore sum’ with the cut off line.

Return type:

None