import gradio as gr print(gr.__version__) import yaml import skimage import numpy as np import matplotlib.pyplot as plt from matplotlib.pyplot import cm import plotly.express as px import plotly.graph_objs as go from plotly.subplots import make_subplots import os import seaborn as sns from cytof import classes from classes import CytofImage, CytofCohort, CytofImageTiff from cytof.hyperion_preprocess import cytof_read_data_roi from cytof.utils import show_color_table # import shutil # shutil.rmtree("/root/.cache/huggingface", ignore_errors=True) OUTDIR = './output' def cytof_tiff_eval(file_path, marker_path, cytof_state): # set to generic names because uploaded filenames is unpredictable slide = 'slide0' roi = 'roi1' # read in the data cytof_img, _ = cytof_read_data_roi(file_path, slide, roi) # case 1. user uploaded TXT/CSV if marker_path is None: # get markers cytof_img.get_markers() # prepsocess cytof_img.preprocess() cytof_img.get_image() # case 2. user uploaded TIFF else: labels_markers = yaml.load(open(marker_path, "rb"), Loader=yaml.Loader) cytof_img.set_markers(**labels_markers) viz = cytof_img.check_channels(ncols=3, savedir='.') msg = f'Your uploaded TIFF has {len(cytof_img.markers)} markers' cytof_state = cytof_img return msg, viz, cytof_state def channel_select(cytof_img): # one for define unwanted channels, one for defining nuclei, one for defining membrane return gr.Dropdown(choices=cytof_img.channels, multiselect=True), gr.Dropdown(choices=cytof_img.channels, multiselect=True), gr.Dropdown(choices=cytof_img.channels, multiselect=True) def nuclei_select(cytof_img): # one for defining nuclei, one for defining membrane return gr.Dropdown(choices=cytof_img.channels, multiselect=True), gr.Dropdown(choices=cytof_img.channels, multiselect=True) def modify_channels(cytof_img, unwanted_channels, nuc_channels, mem_channels): """ 3-step function. 1) removes unwanted channels, 2) define nuclei channels, 3) define membrane channels """ cytof_img_updated = cytof_img.copy() cytof_img_updated.remove_special_channels(unwanted_channels) # define and remove nuclei channels nuclei_define = {'nuclei' : nuc_channels} channels_rm = cytof_img_updated.define_special_channels(nuclei_define) cytof_img_updated.remove_special_channels(channels_rm) # define and keep membrane channels membrane_define = {'membrane' : mem_channels} cytof_img_updated.define_special_channels(membrane_define) # only get image when need to derive from df. CytofImageTIFF has inherent image attribute if type(cytof_img_updated) is CytofImage: cytof_img_updated.get_image() nuclei_channel_str = ', '.join(channels_rm) membrane_channel_str = ', '.join(mem_channels) msg = 'Your remaining channels are: ' + ', '.join(cytof_img_updated.channels) + '.\n\n Nuclei channels: ' + nuclei_channel_str + '.\n\n Membrane channels: ' + membrane_channel_str return msg, cytof_img_updated def update_dropdown_options(cytof_img, selected_self, selected_other1, selected_other2): """ Remove the selected option in the dropdown from the other two dropdowns """ updated_choices = cytof_img.channels.copy() unavail_options = selected_self + selected_other1 + selected_other2 for opt in unavail_options: updated_choices.remove(opt) return gr.Dropdown(choices=updated_choices+selected_other1, value=selected_other1, multiselect=True), gr.Dropdown(choices=updated_choices+selected_other2, value=selected_other2, multiselect=True) def cell_seg(cytof_img, radius): # check if membrane channel available use_membrane = 'membrane' in cytof_img.channels nuclei_seg, cell_seg = cytof_img.get_seg(use_membrane=use_membrane, radius=radius, show_process=False) # visualize nuclei and cells segmentation marked_image_nuclei = cytof_img.visualize_seg(segtype="nuclei", show=False) marked_image_cell = cytof_img.visualize_seg(segtype="cell", show=False) # visualizing nuclei and/or membrane, plus the first marker in channels marker_visualized = cytof_img.channels[0] # similar to plt.imshow() fig = px.imshow(marked_image_cell) # add scatter plot dots as legends fig.add_trace(go.Scatter(x=[None], y=[None], mode='markers', marker=dict(color='white'), name='membrane boundaries')) fig.add_trace(go.Scatter(x=[None], y=[None], mode='markers', marker=dict(color='yellow'), name='nucleus boundaries')) fig.add_trace(go.Scatter(x=[None], y=[None], mode='markers', marker=dict(color='red'), name='nucleus')) fig.add_trace(go.Scatter(x=[None], y=[None], mode='markers', marker=dict(color='green'), name=marker_visualized)) fig.update_layout(legend=dict(orientation="v", bgcolor='lightgray')) return fig, cytof_img def feature_extraction(cytof_img, cohort_state, percentile_threshold): # extract and normalize all features cytof_img.extract_features(filename=cytof_img.filename) cytof_img.feature_quantile_normalization(qs=[percentile_threshold]) # create dir if not exist if not os.path.isdir(OUTDIR): os.makedirs(OUTDIR) cytof_img.export_feature(f"df_feature_{percentile_threshold}normed", os.path.join(OUTDIR, f"feature_{percentile_threshold}normed.csv")) df_feature = getattr(cytof_img, f"df_feature_{percentile_threshold}normed" ) # each file upload in Gradio will always have the same filename # also the temp path created by Gradio is too long to be visually satisfying. df_feature = df_feature.loc[:, df_feature.columns != 'filename'] # calculates quantiles between each marker and cell cytof_img.calculate_quantiles(qs=[75]) dict_cytof_img = {f"{cytof_img.slide}_{cytof_img.roi}": cytof_img} # convert to cohort and prepare downstream analysis cytof_cohort = CytofCohort(cytof_images=dict_cytof_img, dir_out=OUTDIR) cytof_cohort.batch_process_feature() cytof_cohort.generate_summary() cohort_state = cytof_cohort msg = 'Feature extraction completed!' return cytof_img, cytof_cohort, df_feature def co_expression(cytof_img, percentile_threshold): feat_name = f"{percentile_threshold}normed" df_co_pos_prob, df_expected_prob = cytof_img.roi_co_expression(feature_name=feat_name, accumul_type='sum', return_components=False) epsilon = 1e-6 # avoid divide by 0 or log(0) # Normalize and fix Nan edge_percentage_norm = np.log10(df_co_pos_prob.values / (df_expected_prob.values+epsilon) + epsilon) # if observed/expected = 0, then log odds ratio will have log10(epsilon) # no observed means co-expression cannot be determined, does not mean strong negative co-expression edge_percentage_norm[edge_percentage_norm == np.log10(epsilon)] = 0 # do some post processing marker_all_clean = [m.replace('_cell_sum', '') for m in df_expected_prob.columns] # fig = plt.figure() clustergrid = sns.clustermap(edge_percentage_norm, # clustergrid = sns.clustermap(edge_percentage_norm, center=np.log10(1 + epsilon), cmap='RdBu_r', vmin=-1, vmax=3, xticklabels=marker_all_clean, yticklabels=marker_all_clean) # retrieve matplotlib.Figure object from clustermap fig = clustergrid.ax_heatmap.get_figure() return fig, cytof_img def spatial_interaction(cytof_img, percentile_threshold, method, cluster_threshold): feat_name = f"{percentile_threshold}normed" df_expected_prob, df_cell_interaction_prob = cytof_img.roi_interaction_graphs(feature_name=feat_name, accumul_type='sum', method=method, threshold=cluster_threshold) epsilon = 1e-6 # Normalize and fix Nan edge_percentage_norm = np.log10(df_cell_interaction_prob.values / (df_expected_prob.values+epsilon) + epsilon) # if observed/expected = 0, then log odds ratio will have log10(epsilon) # no observed means interaction cannot be determined, does not mean strong negative interaction edge_percentage_norm[edge_percentage_norm == np.log10(epsilon)] = 0 # do some post processing marker_all_clean = [m.replace('_cell_sum', '') for m in df_expected_prob.columns] clustergrid = sns.clustermap(edge_percentage_norm, # clustergrid = sns.clustermap(edge_percentage_norm, center=np.log10(1 + epsilon), cmap='bwr', vmin=-2, vmax=2, xticklabels=marker_all_clean, yticklabels=marker_all_clean) # retrieve matplotlib.Figure object from clustermap fig = clustergrid.ax_heatmap.get_figure() return fig, cytof_img def get_marker_pos_options(cytof_img): options = cytof_img.channels.copy() # nuclei is guaranteed to exist after defining channels options.remove('nuclei') # search for channel "membrane" and delete, skip if cannot find try: options.remove('membrane') except ValueError: pass return gr.Dropdown(choices=options, interactive=True), gr.Dropdown(choices=options, interactive=True) def viz_pos_marker_pair(cytof_img, marker1, marker2, percentile_threshold): stain_nuclei1, stain_cell1, color_dict = cytof_img.visualize_marker_positive( marker=marker1, feature_type="normed", accumul_type="sum", normq=percentile_threshold, show_boundary=True, color_list=[(0,0,1), (0,1,0)], # negative, positive color_bound=(0,0,0), show_colortable=False) stain_nuclei2, stain_cell2, color_dict = cytof_img.visualize_marker_positive( marker=marker2, feature_type="normed", accumul_type="sum", normq=percentile_threshold, show_boundary=True, color_list=[(0,0,1), (0,1,0)], # negative, positive color_bound=(0,0,0), show_colortable=False) # create two subplots fig = make_subplots(rows=1, cols=2, shared_xaxes=True, shared_yaxes=True, subplot_titles=(f"positive {marker1} cells", f"positive {marker2} cells")) fig.add_trace(px.imshow(stain_cell1).data[0], row=1, col=1) fig.add_trace(px.imshow(stain_cell2).data[0], row=1, col=2) # Synchronize axes fig.update_xaxes(matches='x') fig.update_yaxes(matches='y') fig.update_layout(title_text=" ") return fig def phenograph(cytof_cohort): key_pheno = cytof_cohort.clustering_phenograph() df_feats, commus, cluster_protein_exps, figs, figs_scatter, figs_exps = cytof_cohort.vis_phenograph( key_pheno=key_pheno, level="cohort", save_vis=False, show_plots=False, plot_together=False) umap = figs_scatter['cohort'] expression = figs_exps['cohort']['cell_sum'] return umap, cytof_cohort def cluster_interaction_fn(cytof_img, cytof_cohort): # avoid calling the clustering algorithm again. cohort is guaranteed to have one phenogrpah key_pheno = list(cytof_cohort.phenograph.keys())[0] epsilon = 1e-6 interacts, clustergrid = cytof_cohort.cluster_interaction_analysis(key_pheno) interact = interacts[cytof_img.slide] clustergrid_interaction = sns.clustermap(interact, center=np.log10(1+epsilon), cmap='RdBu_r', vmin=-1, vmax=1, xticklabels=np.arange(interact.shape[0]), yticklabels=np.arange(interact.shape[0])) # retrieve matplotlib.Figure object from clustermap fig = clustergrid.ax_heatmap.get_figure() return fig, cytof_img, cytof_cohort def get_cluster_pos_options(cytof_img): options = cytof_img.channels.copy() # nuclei is guaranteed to exist after defining channels options.remove('nuclei') # search for channel "membrane" and delete, skip if cannot find try: options.remove('membrane') except ValueError: pass return gr.Dropdown(choices=options, interactive=True) def viz_cluster_positive(marker, percentile_threshold, cytof_img, cytof_cohort): # avoid calling the clustering algorithm again. cohort is guaranteed to have one phenogrpah key_pheno = list(cytof_cohort.phenograph.keys())[0] # marker positive cell stain_nuclei1, stain_cell1, color_dict = cytof_img.visualize_marker_positive( marker=marker, feature_type="normed", accumul_type="sum", normq=percentile_threshold, show_boundary=True, color_list=[(0,0,1), (0,1,0)], # negative, positive color_bound=(0,0,0), show_colortable=False) # attch PhenoGraph results to individual ROIs cytof_cohort.attach_individual_roi_pheno(key_pheno, override=True) # PhenoGraph clustering visualization pheno_stain_nuclei, pheno_stain_cell, color_dict = cytof_img.visualize_pheno(key_pheno=key_pheno) # create two subplots fig = make_subplots(rows=1, cols=2, shared_xaxes=True, shared_yaxes=True, subplot_titles=(f"positive {marker} cells", "PhenoGraph clusters on cells")) fig.add_trace(px.imshow(stain_cell1).data[0], row=1, col=1) fig.add_trace(px.imshow(pheno_stain_cell).data[0], row=1, col=2) # Synchronize axes fig.update_xaxes(matches='x') fig.update_yaxes(matches='y') fig.update_layout(title_text=" ") return fig, cytof_img, cytof_cohort # Gradio App template custom_css = """ """ with gr.Blocks() as demo: gr.HTML(custom_css) cytof_state = gr.State(CytofImage()) # used in scenrios where users define/remove channels multiple times cytof_original_state = gr.State(CytofImage()) gr.Markdown('
Step 1. Upload images
') gr.Markdown('
You may upload one or two files depending on your use case.
') gr.Markdown('
Case 1:   Upload a single file
') gr.Markdown('
') gr.Markdown('
Case 2:   Upload multiple files
') gr.Markdown('
') gr.Markdown('
Select Input Case:
') choices = gr.Radio(["Case 1", "Case 2"], value="Case 1", label="Choose Input Case", elem_classes='input-choices') def toggle_file_input(choice): if choice == "Case 1": return ( gr.update(visible=True, file_types=['.txt', '.csv'], label="TXT or CSV File"), gr.update(visible=False) ) else: return ( gr.update(visible=True, file_types=[".tiff", '.tif'], label="TIFF File"), gr.update(visible=True) ) with gr.Row(equal_height=True): # second row where 1) asks for marker file upload and 2) displays the visualization of individual channels with gr.Column(scale=2): gr.Markdown('
File Input:
') img_path = gr.File(file_types=['.txt', '.csv'], label='TXT or CSV File') marker_path = gr.File(file_types=['.txt'], label='Marker File', visible=False) with gr.Row(): clear_btn = gr.Button("Clear") submit_btn = gr.Button("Upload") with gr.Column(scale=3): gr.Markdown('
Marker Information:
') img_info = gr.Textbox(label='Ensure the number of markers displayed below matches the expected number.') gr.Markdown('
Visualization of individual channels:
') with gr.Accordion("", open=True): img_viz = gr.Plot(elem_classes='no-label no-border') choices.change(fn=toggle_file_input, inputs=choices, outputs=[img_path, marker_path]) # img_viz = gr.Plot(label="Visualization of individual channels") gr.Markdown('
') gr.Markdown('
Step 2. Modify Existing Channels
') gr.Markdown('
(Required) Define channels designed to visualize nuclei.
') gr.Markdown('
(Optional) Remove unwanted channel after visualizing the individual channels.
') gr.Markdown('
(Optional) Define channels degisned to visualize membranes.
') with gr.Row(equal_height=True): # third row selects nuclei channels with gr.Column(scale=2): selected_nuclei = gr.Dropdown(label='(Required) Select the nuclei channel', interactive=True) selected_unwanted_channel = gr.Dropdown(label='(Optional) Select the unwanted channel', interactive=True) selected_membrane = gr.Dropdown(label='(Optional) Select the membrane channel', interactive=True) define_btn = gr.Button('Modify channels') with gr.Column(scale=3): channel_feedback = gr.Textbox(label='Channels info update') # upload the file, and gather channel info. Then populate to the unwanted_channel, nuclei, and membrane components submit_btn.click( fn=cytof_tiff_eval, inputs=[img_path, marker_path, cytof_original_state], outputs=[img_info, img_viz, cytof_original_state], api_name='upload' ).success( fn=channel_select, inputs=cytof_original_state, outputs=[selected_unwanted_channel, selected_nuclei, selected_membrane] ) selected_unwanted_channel.change(fn=update_dropdown_options, inputs=[cytof_original_state, selected_unwanted_channel, selected_nuclei, selected_membrane], outputs=[selected_nuclei, selected_membrane], api_name='dropdown_monitor1') # api_name used to identify in the endpoints selected_nuclei.change(fn=update_dropdown_options, inputs=[cytof_original_state, selected_nuclei, selected_membrane, selected_unwanted_channel], outputs=[selected_membrane, selected_unwanted_channel], api_name='dropdown_monitor2') selected_membrane.change(fn=update_dropdown_options, inputs=[cytof_original_state, selected_membrane, selected_nuclei, selected_unwanted_channel], outputs=[selected_nuclei, selected_unwanted_channel], api_name='dropdown_monitor3') # modifies the channels per user input define_btn.click(fn=modify_channels, inputs=[cytof_original_state, selected_unwanted_channel, selected_nuclei, selected_membrane], outputs=[channel_feedback, cytof_state]) gr.Markdown('
') gr.Markdown('
Step 3. Perform Cell Segmentation
') gr.Markdown('
In this step, we perform cell segmentation based on the defined nuclei and membrane channels
') with gr.Row(): # This row defines cell radius and performs segmentation with gr.Column(scale=2): gr.Markdown('
Cell Size:
') cell_radius = gr.Number(value=5, precision=0, label='Cell size', info='Please enter the desired radius for cell segmentation (in pixels; default value: 5)', elem_classes='cell-no-label') seg_btn = gr.Button("Segment") with gr.Column(scale=3): gr.Markdown('
Visualization of the segmentation:
') with gr.Accordion("Hover over graph to zoom, pan, save, etc.", open=True): seg_viz = gr.Plot(label="Hover over graph to zoom, pan, save, etc.", elem_classes='no-border no-label') seg_btn.click(fn=cell_seg, inputs=[cytof_state, cell_radius], outputs=[seg_viz, cytof_state]) gr.Markdown('
') gr.Markdown('
Step 4. Extract cell features
') gr.Markdown('
Note: This step will take significantly longer than the previous ones. (A 300MB IMC file takes about 7 minutes to compute.)
') cohort_state = gr.State(CytofCohort()) with gr.Row(): # feature extraction related functinos with gr.Column(scale=2): # gr.CheckboxGroup(choices=['Yes', 'Yes', 'Yes'], label='') norm_percentile = gr.Slider(minimum=50, maximum=99, step=1, value=75, interactive=True, label='Normalized quantification percentile') extract_btn = gr.Button('Extract') with gr.Column(scale=3): feat_df = gr.DataFrame(headers=['id','coordinate_x','coordinate_y','area_nuclei'],col_count=(4, "fixed")) extract_btn.click(fn=feature_extraction, inputs=[cytof_state, cohort_state, norm_percentile], outputs=[cytof_state, cohort_state, feat_df]) gr.Markdown('
') gr.Markdown('
Step 5. Downstream analysis
') gr.Markdown('
(1) Co-expression Analysis
') with gr.Row(): # show co-expression and spatial analysis with gr.Column(scale=2): gr.Markdown('
This analysis measures the level of co-expression for each pair of biomarkers by calculating the odds ratio between the observed co-occurrence and the expected expressing even
') co_exp_btn = gr.Button('Run co-expression analysis') with gr.Column(scale=3): gr.Markdown('
Visualization of cell coexpression of markers
') with gr.Accordion("", open=True): co_exp_viz = gr.Plot(elem_classes='no-label no-border') gr.Markdown('
(2) Spatial Interactoin Analysis
') def update_info_text(choice): if choice == "k-neighbor": return 'K-neighbor: classifies the threshold number of surrounding cells as neighborhood pairs.' else: return 'Distance: classifies cells within threshold distance as neighborhood pairs.' with gr.Row(): with gr.Column(scale=2): gr.Markdown('
This analysis measures the degree of co-expression within a pair of neighborhoods.
') gr.Markdown('
Select the clustering method:
') info_text = gr.Markdown(update_info_text('K-neighbor')) cluster_method = gr.Radio(['k-neighbor', 'distance'], value='k-neighbor', elem_classes='test', label='') cluster_threshold = gr.Slider(minimum=1, maximum=100, step=1, value=30, interactive=True, label='Clustering threshold') spatial_btn = gr.Button('Run spatial interaction analysis') with gr.Column(scale=3): gr.Markdown('
Visualization of spatial interaction of markers
') with gr.Accordion("", open=True): spatial_viz = gr.Plot(elem_classes='no-label no-border') cluster_method.change(fn=update_info_text, inputs=cluster_method, outputs=info_text) co_exp_btn.click(fn=co_expression, inputs=[cytof_state, norm_percentile], outputs=[co_exp_viz, cytof_state]) # spatial_btn logic is in step6. This is populate the marker positive dropdown options gr.Markdown('
') gr.Markdown('
Step 6. Visualize positive markers
') gr.Markdown('
Select two markers for side-by-side comparison to visualize their positive states in cells. This serves two purposes:
') gr.Markdown('
(1) Validate the co-expression analysis results.
') gr.Markdown('
(2) Validate teh spatial interaction analysis results.
') with gr.Row(): # two marker positive visualization - dropdown options with gr.Column(scale=2): selected_marker1 = gr.Dropdown(label='Select one marker', info='Select a marker to visualize', interactive=True) selected_marker2 = gr.Dropdown(label='Select another marker', info='Selecting the same marker as the previous one is allowed', interactive=True) pos_viz_btn = gr.Button('Visualize these two markers') with gr.Column(scale=3): gr.Markdown('
Visualization of the two markers.
') with gr.Accordion("Hover over graph to zoom, pan, save, etc.", open=True): marker_pos_viz = gr.Plot(elem_classes='no-label no-border') spatial_btn.click( fn=spatial_interaction, inputs=[cytof_state, norm_percentile, cluster_method, cluster_threshold], outputs=[spatial_viz, cytof_state] ).success( fn=get_marker_pos_options, inputs=[cytof_state], outputs=[selected_marker1, selected_marker2] ) pos_viz_btn.click(fn=viz_pos_marker_pair, inputs=[cytof_state, selected_marker1, selected_marker2, norm_percentile], outputs=[marker_pos_viz]) gr.Markdown('
') gr.Markdown('
Step 7. Phenogrpah Clustering
') gr.Markdown('
Cells can be clustered into sub-groups based on the extracted single-cell data. (A 300MB IMC file takes about 2 minutes to compute.)
') with gr.Row(): # add two plots to visualize phenograph results with gr.Column(scale=2): gr.Markdown('
We used UMAP to project the high-dimensional data onto a 2-D space.
') umap_btn = gr.Button('Run Phenograph clustering') with gr.Column(scale=3): gr.Markdown('
UMAP Results
') with gr.Accordion("", open=True): phenograph_umap = gr.Plot(elem_classes='no-label no-border') with gr.Row(): # add two plots to visualize phenograph results with gr.Column(scale=2): gr.Markdown('
The previously assigned clusters are also reflected in this figure.
') cluster_interact_btn = gr.Button('Run clustering interaction') with gr.Column(scale=3): gr.Markdown('
Spatial interaction of clusters
') with gr.Accordion("", open=True): cluster_interaction = gr.Plot(elem_classes='no-label no-border') cluster_interact_btn.click(cluster_interaction_fn, inputs=[cytof_state, cohort_state], outputs=[cluster_interaction, cytof_state, cohort_state]) gr.Markdown('
') gr.Markdown('
In additional, you could visualizing the cluster assignments against the positive markers to oberve any patterns:
') with gr.Row(): with gr.Column(scale=2): selected_cluster_marker = gr.Dropdown(label='Select one marker', info='Select a marker to visualize', interactive=True) cluster_positive_btn = gr.Button('Compare clusters and positive markers') with gr.Column(scale=3): gr.Markdown('
Cluster assignment vs. positive cells
') with gr.Accordion("Hover over graph to zoom, pan, save, etc.", open=True): cluster_v_positive = gr.Plot(elem_classes='no-label no-border') umap_btn.click( fn=phenograph, inputs=[cohort_state], outputs=[phenograph_umap, cohort_state] ).success( fn=get_cluster_pos_options, inputs=[cytof_state], outputs=[selected_cluster_marker], api_name='selectClusterMarker' ) cluster_positive_btn.click(fn=viz_cluster_positive, inputs=[selected_cluster_marker, norm_percentile, cytof_state, cohort_state], outputs=[cluster_v_positive, cytof_state, cohort_state]) # clear everything if clicked clear_components = [img_path, marker_path, img_info, img_viz, channel_feedback, seg_viz, feat_df, co_exp_viz, spatial_viz, marker_pos_viz, phenograph_umap, cluster_interaction, cluster_v_positive] clear_btn.click(lambda: [None]*len(clear_components), outputs=clear_components) if __name__ == "__main__": demo.launch(share=True)