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@@ -32,3 +32,25 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/4_markers_tresholds.ipynb filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S1_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S2_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S3_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/TMA_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/DD3S1.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/DD3S2.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/DD3S3.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/TMA.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/all_Samples_Set_A.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S1_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S2_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S3_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/TMA_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S1_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S2_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S3_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/TMA_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S1_zscore.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S2_zscore.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S3_zscore.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/TMA_zscore.csv filter=lfs diff=lfs merge=lfs -text

wetransfer_data-zip_2024-05-17_1431/4_markers_tresholds.ipynb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df84f15a0e72c1a84daae210d6334f49150c5d2aa03aae2688f1f839fdb1aeb1
+size 49760040

wetransfer_data-zip_2024-05-17_1431/data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72623169e7fa754d5892a19670be3a1f4cd2bec5fb4736db5e8de103d70c5fe4
+size 326996556

wetransfer_data-zip_2024-05-17_1431/my_modules.py

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+import os
+import numpy as np
+import pandas as pd
+import subprocess
+import os
+import random
+import re
+import pandas as pd
+import numpy as np
+import seaborn as sb
+import matplotlib.pyplot as plt
+import matplotlib.colors as mplc
+import subprocess
+
+
+from scipy import signal
+
+import plotly.figure_factory as ff
+import plotly
+import plotly.graph_objs as go
+from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
+
+
+# This function takes in a dataframe, changes the names
+# of the column in various ways, and returns the dataframe.
+# For best accuracy and generalizability, the code uses
+# regular expressions (regex) to find strings for replacement.
+def apply_header_changes(df):
+    # remove lowercase x at beginning of name
+    df.columns = df.columns.str.replace("^x","")
+    # remove space at beginning of name
+    df.columns = df.columns.str.replace("^ ","")
+    # replace space with underscore
+    df.columns = df.columns.str.replace(" ","_")
+    # fix typos
+    df.columns = df.columns.str.replace("AF_AF","AF")
+    # change "Cell Id" into "ID"
+    df.columns = df.columns.str.replace("Cell Id","ID")
+    # if the ID is the index, change "Cell Id" into "ID"
+    df.index.name = "ID"
+    # 
+    df.columns = df.columns.str.replace("","")
+    return df
+
+def apply_df_changes(df):
+    # Remove "@1" after the ID in the index
+    df.index = df.index.str.replace(r'@1$', '')
+    return df
+
+def compare_headers(expected, actual, name):
+    missing_actual = np.setdiff1d(expected, actual)
+    extra_actual = np.setdiff1d(actual, expected)
+    if len(missing_actual) > 0:
+        #print("WARNING: File '" + name + "' lacks the following expected header(s) after import header reformatting: \n" 
+        #      + str(missing_actual))
+        print("WARNING: File '" + name + "' lacks the following expected item(s): \n" + str(missing_actual))
+    if len(extra_actual) > 0:
+        #print("WARNING: '" + name + "' has the following unexpected header(s) after import header reformatting: \n" 
+        #      + str(extra_actual))
+        print("WARNING: '" + name + "' has the following unexpected item(s): \n" + str(extra_actual))
+    
+    return None
+
+
+def add_metadata_location(row):
+    fc = row['full_column'].lower()
+    if 'cytoplasm' in fc and 'cell' not in fc and 'nucleus' not in fc:
+        return 'cytoplasm'
+    elif 'cell' in fc and 'cytoplasm' not in fc and 'nucleus' not in fc:
+        return 'cell'
+    elif 'nucleus' in fc and 'cell' not in fc and 'cytoplasm' not in fc:
+        return 'nucleus'
+    else:
+        return 'unknown'
+
+    
+def get_perc(row, cell_type):
+    total = row['stroma'] + row['immune'] + row['cancer']+row['endothelial']
+    return round(row[cell_type]/total *100,1)
+
+
+
+# Divide each marker (and its localisation) by the right exposure setting for each group of samples
+def divide_exp_time(col, exp_col, metadata):
+    exp_time = metadata.loc[metadata['full_column'] == col.name, exp_col].values[0]
+    return col/exp_time
+
+
+def do_background_sub(col, df, metadata):
+    #print(col.name)
+    location = metadata.loc[metadata['full_column'] == col.name, 'localisation'].values[0]
+    #print('location = ' + location)
+    channel = metadata.loc[metadata['full_column'] == col.name, 'Channel'].values[0]
+    #print('channel = ' + channel)
+    af_target = metadata.loc[
+        (metadata['Channel']==channel) \
+        & (metadata['localisation']==location) \
+        & (metadata['target_lower'].str.contains(r'^af\d{3}$')),\
+        'full_column'].values[0]
+    return col - df.loc[:,af_target]
+
+
+"""
+This function plots distributions. It takes in a string title (title), a list of
+dataframes from which to plot (dfs), a list of dataframe names for the legend
+(names), a list of the desired colors for the plotted samples (colors),
+a string for the x-axis label (x_label), ```a float binwidth for histrogram (bin_size)```,
+a boolean to show the legend or not (legend),
+and the names of the marker(s) to plot (input_labels). If not specified,
+the function will plot all markers in one plot. input_labels can either be a 
+single string, e.g., 'my_marker', or a list, e.g., ['my_marker1','my_marker2'].
+
+The function will create a distribution plot and save it to png. It requires 
+a list of items not to be considered as markers when evaluating column names
+(not_markers) to be in memory. It also requires a desired output location of
+the files (output_dir) to already be in memory. 
+"""
+
+
+
+def make_distr_plot_per_sample(title, location, dfs, df_names, colors, x_label, legend, xlims = None, markers = ['all'],not_intensities = None):
+    ### GET LIST OF MARKERS TO PLOT ###
+    # Get list of markers to plot if not specified by user, using columns in first df
+    # Writing function(parameter = FILLER) makes that parameter optional when user calls function,
+    # since it is given a default value!
+    if markers == ["all"]:
+        markers = [c for c in dfs[0].columns.values if c not in not_intensities]
+    elif not isinstance(markers, list):
+        markers = [markers]
+    # Make input labels a set to get only unique values, then put back into list
+    markers = list(set(markers))
+    
+    ### GET XLIMS ###
+    if xlims == None:
+        mins = [df.loc[:,markers].min().min() for df in dfs]
+        maxes = [df.loc[:,markers].max().max() for df in dfs]
+        xlims = [min(mins), max(maxes)]
+    if not isinstance(xlims, list):
+        print("Problem - xlmis not list. Exiting method...")
+        return None
+    ### CHECK DATA CAN BE PLOTTED ###
+    # Check for data with only 1 unique value - this will cause error if plotted
+    group_labels = []
+    hist_data = []
+    # Iterate through all dataframes (dfs)
+    for i in range(len(dfs)):
+        # Iterate through all marker labels
+        for f in markers:
+            # If there is only one unique value in the marker data for this dataframe,
+            # you cannot plot a distribution plot. It gives you a linear algebra
+            # singular value matrix error
+            if dfs[i][f].nunique() != 1:
+                # Add df name and marker name to labels list
+                # If we have >1 df, we want to make clear
+                # which legend label is associated with which df
+                if len(df_names) > 1:
+                    group_labels.append(df_names[i]+"_"+f)
+                else:
+                    group_labels.append(f)
+                # add the data to the data list
+                hist_data.append(dfs[i][f])
+    # if no data had >1 unique values, there is nothing to plot
+    if len(group_labels) < 1:
+        print("No markers plotted - all were singular value. Names and markers were " + str(df_names) + ", " + str(markers))
+        return None
+
+    ### TRANSFORM COLOR ITEMS TO CORRECT TYPE ###
+    if isinstance(colors[0], tuple):
+        colors = ['rgb' + str(color) for color in colors]
+    
+    ### PLOT DATA ###
+    # Create plot
+    fig = ff.create_distplot(hist_data, group_labels, bin_size=0.1,
+        #colors=colors, bin_size=bin_size,  show_rug=False)#show_hist=False,
+        colors=colors, show_rug=False)
+    # Adjust title, font, background color, legend...
+    fig.update_layout(title_text=title, font=dict(size=18), 
+        plot_bgcolor = 'white', showlegend = legend)#, legend_x = 3)
+    # Adjust opacity
+    fig.update_traces(opacity=0.6)
+    # Adjust x-axis parameters
+    fig.update_xaxes(title_text = x_label, showline=True, linewidth=2, linecolor='black', 
+        tickfont=dict(size=18), range = xlims) # x lims was here
+    # Adjust y-axis parameters
+    fig.update_yaxes(title_text = "Kernel density estimate",showline=True, linewidth=1, linecolor='black',
+        tickfont=dict(size=18))
+    
+
+    ### SAVE/DISPLAY PLOT ###
+    # Save plot to HTML
+    # plotly.io.write_html(fig, file = output_dir + "/" + title + ".html")
+    # Plot in new tab
+    #plot(fig)   
+    # Save to png
+    filename = os.path.join(location, title.replace(" ","_") + ".png")
+    fig.write_image(filename)
+    return None
+
+
+
+
+
+    # this could be changed to use recursion and make it 'smarter'
+
+def shorten_feature_names(long_names):
+    name_dict = dict(zip(long_names,[n.split('_')[0] for n in long_names]))
+    names_lts, long_names, iteration = shorten_feature_names_helper(name_dict, long_names, 1)
+    # names_lts = names long-to-short
+    # names_stl = names stl
+    names_stl = {}
+    for n in names_lts.items():
+        names_stl[n[1]] = n[0]
+    return names_lts, names_stl
+    
+
+def shorten_feature_names_helper(name_dict, long_names, iteration):
+    #print("\nThis is iteration #"+str(iteration))
+    #print("name_dict is: " + str(name_dict))
+    #print("long_names is: " + str(long_names))
+    ## If the number of unique nicknames == number of long names
+    ## then the work here is done
+    #print('\nCompare lengths: ' + str(len(set(name_dict.values()))) + ", " + str(len(long_names)))
+    #print('set(name_dict.values()): ' + str(set(name_dict.values())))
+    #print('long_names: ' + str(long_names))
+    if len(set(name_dict.values())) == len(long_names): 
+        #print('All done!')
+        return name_dict, long_names, iteration
+    
+    ## otherwise, if the number of unique nicknames is not
+    ## equal to the number of long names (must be shorter than),
+    ## then we need to find more unique names
+    iteration += 1
+    nicknames_set = set()
+    non_unique_nicknames = set()
+    # construct set of current nicknames
+    for long_name in long_names:
+        #print('long_name is ' + long_name + ' and non_unique_nicknames set is ' + str(non_unique_nicknames))
+        short_name = name_dict[long_name]
+        if short_name in nicknames_set:
+            non_unique_nicknames.add(short_name)
+        else:
+            nicknames_set.add(short_name)
+    #print('non_unique_nicknames are: ' + str(non_unique_nicknames))
+    
+    # figure out all long names associated
+    # with the non-unique short names
+    trouble_long_names = set()
+    for long_name in long_names:
+        short_name = name_dict[long_name]
+        if short_name in non_unique_nicknames:
+            trouble_long_names.add(long_name)
+    
+    #print('troublesome long names are: ' + str(trouble_long_names))
+    #print('name_dict: ' + str(name_dict))
+    # operate on all names that are associated with
+    # the non-unique short nicknames
+    for long_name in trouble_long_names:
+        #print('trouble long name is: ' + long_name)
+        #print('old nickname is: ' + name_dict[long_name])
+        name_dict[long_name] = '_'.join(long_name.split('_')[0:iteration])
+        #print('new nickname is: ' + name_dict[long_name])
+    shorten_feature_names_helper(name_dict, long_names, iteration)
+    return name_dict, long_names, iteration
+
+
+def heatmap_function2(title,
+            data,
+              method, metric, cmap,
+              cbar_kws, xticklabels, save_loc,
+              row_cluster, col_cluster, 
+            annotations = {'rows':[],'cols':[]}):
+    
+    sb.set(font_scale= 6.0)
+
+    # Extract row and column mappings
+    row_mappings = []
+    col_mappings = []
+    for ann in annotations['rows']:
+        row_mappings.append(ann['mapping'])
+    for ann in annotations['cols']:
+        col_mappings.append(ann['mapping'])
+    # If empty lists, convert to None so seaborn accepts
+    # as the row_colors or col_colors objects
+    if len(row_mappings) == 0:
+        row_mappings = None
+    if len(col_mappings) == 0:
+        col_mappings = None
+
+def heatmap_function(title,
+            data,
+              method, metric, cmap,
+              cbar_kws, xticklabels, save_loc,
+              row_cluster, col_cluster, 
+            annotations = {'rows':[],'cols':[]}):
+    
+    sb.set(font_scale= 2.0)
+
+    # Extract row and column mappings
+    row_mappings = []
+    col_mappings = []
+    for ann in annotations['rows']:
+        row_mappings.append(ann['mapping'])
+    for ann in annotations['cols']:
+        col_mappings.append(ann['mapping'])
+    # If empty lists, convert to None so seaborn accepts
+    # as the row_colors or col_colors objects
+    if len(row_mappings) == 0:
+        row_mappings = None
+    if len(col_mappings) == 0:
+        col_mappings = None
+
+    # Create clustermap
+    g = sb.clustermap(data = data, 
+                  robust = True,
+                  method = method, metric = metric,
+                  cmap = cmap,
+                  row_cluster = row_cluster, col_cluster = col_cluster,
+                  figsize = (40,30),
+                  row_colors=row_mappings, col_colors=col_mappings,
+                      yticklabels = False,
+                     cbar_kws = cbar_kws,
+                     xticklabels = xticklabels)
+    
+    # To rotate slightly the x labels
+    plt.setp(g.ax_heatmap.xaxis.get_majorticklabels(), rotation=45) 
+    
+    # Add title
+    g.fig.suptitle(title, fontsize = 60.0)
+    
+    #And now for the legends:
+    # iterate through 'rows', 'cols'
+    for ann_type in annotations.keys():
+        # iterate through each individual annotation feature
+        for ann in annotations[ann_type]:
+            color_dict = ann['dict']
+            handles = []
+            for item in color_dict.keys():
+                h = g.ax_col_dendrogram.bar(0,0, color = color_dict[item], label = item,
+                                           linewidth = 0)
+                handles.append(h)
+            legend = plt.legend(handles = handles, loc = ann['location'], title = ann['label'],
+                               bbox_to_anchor=ann['bbox_to_anchor'],
+                               bbox_transform=plt.gcf().transFigure)
+            ax = plt.gca().add_artist(legend)
+
+    # Save image
+    filename = os.path.join(save_loc, title.lower().replace(" ","_") + ".png")
+    g.savefig(filename)
+    
+    return None
+
+
+    
+# sources - 
+#https://stackoverflow.com/questions/27988846/how-to-express-classes-on-the-axis-of-a-heatmap-in-seaborn
+# https://matplotlib.org/3.1.1/tutorials/intermediate/legend_guide.html
+
+
+def verify_line_no(filename, lines_read):
+    # Use Linux "wc -l" command to get the number of lines in the unopened file
+    wc = subprocess.check_output(['wc', '-l', filename]).decode("utf-8")
+    # Take that string, turn it into a list, extract the first item,
+    # and make that an int - this is the number of lines in the file
+    wc = int(wc.split()[0])
+    if lines_read != wc:
+        print("WARNING: '" + filename + "' has " + str(wc) + 
+            " lines, but imported dataframe has " 
+              + str(lines_read) + " (including header).")
+    return None
+
+
+def rgb_tuple_from_str(rgb_str):
+    rgb_str = rgb_str.replace("(","").replace(")","").replace(" ","")
+    rgb = list(map(float,rgb_str.split(",")))
+    return tuple(rgb)
+
+def color_dict_to_df(cd, column_name):
+    df = pd.DataFrame.from_dict(cd, orient = 'index')
+    df['rgb'] = df.apply(lambda row: (np.float64(row[0]), np.float64(row[1]), np.float64(row[2])), axis = 1)
+    df = df.drop(columns = [0,1,2])
+    df['hex'] = df.apply(lambda row: mplc.to_hex(row['rgb']), axis = 1)
+    df[column_name] = df.index
+    return df
+
+
+# p-values that are less than or equal to 0.05
+def p_add_star(row):
+    m = [str('{:0.3e}'.format(m)) + "*" 
+         if m <= 0.05 \
+         else str('{:0.3e}'.format(m))
+        for m in row ]
+    return pd.Series(m)
+
+# assigns a specific number of asterisks based on the thresholds 
+def p_to_star(row):
+    output  = []
+    for item in row:
+        if item <= 0.001:
+            stars = 3
+        elif item <= 0.01:
+            stars = 2
+        elif item <= 0.05:
+            stars = 1
+        else:
+            stars = 0
+        value = ''
+        for i in range(stars):
+            value += '*'
+        output.append(value)
+    return pd.Series(output)
+
+
+
+def plot_gaussian_distributions(df):
+    # Initialize thresholds list to store all calculated thresholds
+    all_thresholds = []
+
+    # Iterate over all columns except the first one (assuming the first one is non-numeric or an index)
+    for column in df.columns:  
+        # Extract the marker data
+        marker_data = df[column]
+
+        # Calculating mean and standard deviation for each marker
+        m_mean, m_std = np.mean(marker_data), np.std(marker_data)
+
+        # Generating x values for the Gaussian curve
+        x_vals = np.linspace(marker_data.min(), marker_data.max(), 100)
+
+        # Calculating Gaussian distribution curve
+        gaussian_curve = (1 / (m_std * np.sqrt(2 * np.pi))) * np.exp(-(x_vals - m_mean) ** 2 / (2 * m_std ** 2))
+
+        # Creating figure for Gaussian distribution for each marker
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=x_vals, y=gaussian_curve, mode='lines', name=f'{column} Gaussian Distribution'))
+        fig.update_layout(title=f'Gaussian Distribution for {column} Marker')
+
+        # Calculating thresholds based on each marker's distribution
+        seuil_1sigma = m_mean + m_std
+        seuil_2sigma = m_mean + 2 * m_std
+        seuil_3sigma = m_mean + 3 * m_std
+
+        # Display the figures with thresholds
+        fig.add_shape(type='line', x0=seuil_1sigma, y0=0, x1=seuil_1sigma, y1=np.max(gaussian_curve),
+                      line=dict(color='red', dash='dash'), name=f'Seuil 1σ: {seuil_1sigma:.2f}')
+        fig.add_shape(type='line', x0=seuil_2sigma, y0=0, x1=seuil_2sigma, y1=np.max(gaussian_curve),
+                      line=dict(color='green', dash='dash'), name=f'Seuil 2σ: {seuil_2sigma:.2f}')
+        fig.add_shape(type='line', x0=seuil_3sigma, y0=0, x1=seuil_3sigma, y1=np.max(gaussian_curve),
+                      line=dict(color='blue', dash='dash'), name=f'Seuil 3σ: {seuil_3sigma:.2f}')
+
+        # Add markers and values to the plot
+        fig.add_trace(go.Scatter(x=[seuil_1sigma, seuil_2sigma, seuil_3sigma],
+                                 y=[0, 0, 0],
+                                 mode='markers+text',
+                                 text=[f'{seuil_1sigma:.2f}', f'{seuil_2sigma:.2f}', f'{seuil_3sigma:.2f}'],
+                                 textposition="top center",
+                                 marker=dict(size=10, color=['red', 'green', 'blue']),
+                                 name='Threshold Values'))
+
+        fig.show()
+
+        # Append thresholds for each marker to the list
+        all_thresholds.append((column, seuil_1sigma, seuil_2sigma, seuil_3sigma))  # Include the column name
+
+    # Return thresholds for all markers
+    return all_thresholds
+
+
+

wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S1_bs.csv

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wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_1.tif.csv

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wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_2.tif.csv

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wetransfer_data-zip_2024-05-17_1431/test_metadata/TMA_Clinical_Data_187-OC.csv

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+2;50;0;36;24;;C;breast;1;ACT;;0;3C;HGSOC;1;0;1;669;1;DOD;2223;Yes;1
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+10;90;5;50;20;316;C;0;1;ACT;;0;3C;serousfallopian;1;0;1;;0;;2162;No;
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+41;90;0;51;20;78;C;breast;1;ACT;;0;2C;HGSOC;1;0;1;716;1;DOD;1570;Yes;1
+42;90;0;63;29;2760;C;0;;ACT;;0;3C;HGSOC;0;1;0;1321;1;DOD;2909;Yes;1
+43;70;5;64;24;118;C;breast;;ACT;;0;3C;HGSOC;0;1;1;600;1;DOD;1719;Yes;1
+44;90;0;71;27;;C;0;;ACT;;0;1C;HGSOC;1;0;;;;NED;4494;No;0
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+46;90;0;43;34;3769;C;0;;ACT;;0;3C;HGSOC;0;1;0;190;1;DOD;1089;Yes;1
+47;80;0;25;55;83;C;0;;ACT;;0;3C;HGSOC;0;1;;;;;25;No;
+48;90;5;84;27;2927;C;0;;ACT;;0;3C;HGSOC;1;0;1;266;0;DOD;403;Yes;1
+49;90;5;62;44;;C;0;;ACT;;0;3C;HGSOC;1;0;1;433;0;;468;Yes;
+50;90;5;59;32;1040;C;0;;ACT;;0;3C;HGSOC;0;1;0;454;0;DOD;1292;Yes;1
+51;90;0;55;33;1000;C;renalcell;;NACT;;90;X;HGSOC;1;0;1;2103;0;;2124;Yes;
+52;90;0;;;;;;;ACT;;0;;mucinousadenocarcinomag2;;;;293;;DOD;487;Yes;1
+53;80;0;76;21;980;C;0;;ACT;;0;3C;HGSOC;1;0;1;529;0;DOD;670;Yes;1
+54;90;5;64;21;250;C;0;;ACT;;0;3C;HGSOC;1;0;1;297;1;DOD;709;Yes;1
+55;90;0;70;21;1665;C;0;;ACT;;0;1C;CCC;1;0;1;;0;NED;5229;No;0
+56;80;5;71;32;1124;C;0;;ACT;;0;3C;HGSOC;0;1;;;;;966;No;
+57;90;5;48;32;3800;C;breast;;ACT;;0;3C;HGSOC;0;1;;;0;;1006;;
+58;90;0;60;29;18;C;breast;0;ACT;;0;1C;CCC;1;0;1;;0;NED;5143;No;0
+59;90;5;65;22;1927;C;0;;ACT;;0;3C;HGSOC;0;1;0;203;0;AWD;386;Yes;0
+60;90;5;63;18;71;C;0;;ACT;;0;1C;mucinousadenocarcinomag2;1;0;1;;0;NED;4798;No;0
+61;60;10;65;23;281;C;0;;NACT;9;9;X;metastaticadenocarcinoma;;;1;;0;;23;No;
+62;90;0;62;24;675;C;0;0;ACT;;0;3C;HGSOC;0;1;1;1055;1;DOD;1842;Yes;1
+63;80;0;49;32;443;C;0;;ACT;;0;3B;HGSOC;1;0;1;609;1;DOD;1899;Yes;1
+64;90;0;50;29;64;C;0;;ACT;;0;2C;HGSOC;1;0;1;;0;NED;4887;No;0
+65;90;0;79;23;136;C;breast;;ACT;;0;3C;HGSOC;0;1;;;;DOD;23;No;1
+66;90;0;75;26;1703;C;0;0;ACT;;0;3C;HGSOC;1;0;0;295;1;AWD;610;Yes;0
+67;90;0;44;24;529;C;breast;1;ACT;;0;2C;HGSOC;1;0;1;892;1;DOD;4347;Yes;1
+68;90;0;80;22;7861;C;melanoma;;ACT;;0;3C;HGSOC;0;1;;;0;DOD;21;No;1
+69;80;0;60;31;78;C;0;0;ACT;;0;3A;HGSOC;1;0;1;;0;NED;4691;No;0
+70;90;0;62;23;139;C;0;;ACT;;0;3C;serousperitonealg1;1;0;0;238;0;DOD;1439;Yes;1
+71;90;5;75;20;1698;C;0;;ACT;;0;3C;HGSOC;0;1;;;0;DOC;4509;No;1
+72;80;5;;;;C;0;;ACT;;0;;;;;;365;1;;38468;Yes;
+73;70;0;75;21;30;C;0;;ACT;;0;3C;HGSOC;0;1;;;0;DOD;37;No;1
+74;90;0;68;23;1360;C;0;;ACT;;0;3C;HGSOC;1;0;1;335;1;DOD;1309;Yes;1
+75;40;0;53;26;587;C;0;;ACT;;0;1A;mucinousadenocarcinomag1;1;0;;;0;NED;4229;No;0
+76;90;5;74;29;4124;C;breaststomach;;ACT;;0;3C;HGSOC;0;1;;;0;DOD;3164;No;1
+77;80;0;54;28;3757;MiddleEastern;0;;ACT;;0;3A;CCC;1;0;1;;0;NED;4557;No;0
+78;80;5;51;30;176;C;0;0;ACT;;0;2B;ENDOOV ;1;0;1;;0;NED;4470;No;0
+79;80;0;57;21;776;Japanese;0;2;ACT;;0;2C;HGSOC;1;0;0;195;1;NED;4521;Yes;0
+80;70;5;69;24;532;C;0;;ACT;;0;3C;HGSOC;1;0;1;760;0;DOD;1111;Yes;1
+81;90;0;64;28;180;C;0;;ACT;;0;3C;HGSOC;1;0;0;535;1;DOD;3945;Yes;1
+82;80;0;64;42;12;C;0;;ACT;;0;3C;HGSOC;1;0;0;147;0;DOD;379;Yes;1
+83;90;0;72;30;959;C;0;;ACT;;0;X;HGSOC;;1;1;468;0;;1064;Yes;
+84;80;10;66;27;;C;braintumorofovarianorigin;;ACT;;0;4B;HGSOC;;;1;393;0;DOD;463;Yes;1
+85;90;0;61;30;116;hispanic;0;;NACT;8;343;X;HGSOC;1;0;1;572;0;DOD;600;Yes;1
+86;80;10;66;29;406;C;0;;ACT;;0;3C;HGSOC;0;1;0;251;1;DOD;540;Yes;1
+87;80;0;86;27;2779;C;0;;ACT;;0;3C;HGSOC;1;0;0;293;0;AWD;826;Yes;0
+88;90;5;49;41;371;C;breast;2;ACT;;0;3C;HGSOC;1;0;0;132;0;NED;4015;Yes;0
+89;90;0;64;28;855;C;0;0;NACT;48;159;X;HGSOC;1;0;1;540;1;DOD;1820;Yes;1
+90;90;5;61;29;547;C;0;;ACT;;0;3C;HGSOC;0;1;;600;0;DOD;1899;Yes;1
+91;70;5;56;40;877;C;0;2;ACT;;0;3C;HGSOC;1;0;1;1140;0;AWD;1358;Yes;0
+92;70;5;75;38;3083;C;0;0;ACT;;0;3C;HGSOC;1;0;1;615;0;DOD;705;Yes;1
+93;90;5;89;21;2895;C;0;;ACT;;0;3B;HGSOC;0;1;;;0;DOD;297;;1
+94;90;5;51;28;5242;C;0;0;ACT;;0;3C;HGSOC;1;0;1;785;1;DOD;1758;Yes;1
+95;80;5;80;24;2799;C;0;;NACT;7;97;X;HGSOC;1;0;;;0;;96;No;
+96;90;5;68;20;2683;C;0;;ACT;;0;2C;HGSOC;1;0;1;;0;NED;4082;No;0
+97;70;5;57;40;19842;C;0;;ACT;;0;2C;mixedepithelialmucosal;1;0;0;174;0;DOD;206;Yes;1
+98;70;0;79;26;2461;C;0;;NACT;12;135;X;HGSOC;1;0;1;343;0;DOD;923;Yes;1
+99;90;0;55;21;1400;C;breast;1;ACT;;0;2C;HGSOC;1;0;;141;0;;336;Yes;
+100;90;0;62;32;2059;C;0;;ACT;;0;1C;HGSOC;1;0;1;;0;NED;2938;No;0
+101;90;5;75;33;242;C;0;;ACT;;0;3C;HGSOC;0;1;1;460;0;DOD;836;Yes;1
+102;90;0;55;25;1395;C;0;;ACT;;0;3C;HGSOC;1;0;1;797;0;DOD;2554;Yes;1
+103;90;10;46;23;116;C;uterine;PMS2;ACT;;0;1C;HGSOC;1;0;1;;0;NED;3578;No;0
+104;90;0;60;29;63;C;0;;NACT;6;103;X;HGSOC;1;0;1;335;0;DOD;351;Yes;1
+105;80;10;75;26;873;C;0;0;ACT;;0;3B;HGSOC;1;0;1;468;0;DOD;622;Yes;1
+106;80;5;69;29;242;C;skintongue;;ACT;;0;3C;HGSOC;1;0;1;648;1;DOD;1320;Yes;1
+107;90;5;63;26;1928;C;0;MSH6;ACT;;0;3C;HGSOC;1;0;1;;0;NED;3725;No;0
+108;80;5;57;40;3687;C;0;;ACT;;0;4;HGSOC;0;1;0;353;1;DOD;791;Yes;1
+109;80;5;75;35;223;C;0;;ACT;;0;1A;HGSOC;0;1;1;;0;NED;3863;No;0
+110;90;0;56;29;5902;C;0;;ACT;;0;3C;HGSOC;0;1;1;438;1;DOD;1527;Yes;1
+111;90;5;50;32;1960;C;0;0;NACT;15;79;X;HGSOC;1;0;1;533;1;DOD;1888;Yes;1
+112;70;5;64;34;301;C;0;0;ACT;;0;1A;HGSOC;1;0;;896;1;AWD;3656;Yes;0
+113;90;0;62;21;115;C;0;;ACT;;0;3C;mucinousadenocarcinomag2;1;0;0;148;;;161;Yes;
+114;90;0;54;24;8571;C;0;;ACT;;0;3C;HGSOC;1;0;0;175;1;DOD;472;Yes;1
+115;80;5;58;22;942;C;;;ACT;;0;4;HGSOC;1;0;1;;0;NED;3219;No;0
+116;80;0;76;34;7453;C;0;;NACT;8;92;X;HGSOC;1;0;1;;0;;478;No;
+117;90;5;54;27;6910;C;0;;ACT;;0;3C;HGSOC;1;0;1;;0;NED;3672;No;0
+118;90;0;64;45;4;C;0;;ACT;;0;1C;HGSOC;1;0;1;609;0;DOD;688;Yes;1
+119;90;0;57;17;16234;C;0;;NACT;12;95;X;HGSOC;0;1;1;655;0;DOD;374;Yes;1
+120;90;0;77;28;18;C;cervical;;ACT;;0;3C;HGSOC;0;1;1;991;1;DOD;1616;Yes;1
+121;90;5;64;38;2207;C;melanoma;;NACT;6;136;X;HGSOC;1;0;;;1;DOD;2445;;1
+122;90;5;81;25;1884;C;0;;ACT;;0;3C;HGSOC;1;0;;517;0;;877;Yes;
+123;70;0;83;26;471;C;breast;;ACT;;0;3C;HGSOC;1;0;1;538;0;DOD;722;Yes;1
+124;90;0;47;22;1993;C;0;2;ACT;;0;4A;HGSOC;0;1;1;570;1;DOD;1042;Yes;1
+125;90;5;70;36;4078;C;breast;0;ACT;;0;3C;HGSOC;1;0;;;;;1077;;
+126;90;0;72;27;2018;C;0;;ACT;;0;3C;HGSOC;1;0;0;341;;DOD;711;Yes;1
+127;70;0;62;22;547;C;melanoma;;ACT;;0;3C;HGSOC;1;0;1;345;0;DOD;485;Yes;1
+128;80;5;58;34;776;C;0;;ACT;;0;3Bor4B;uterinepapillaryserous;1;0;0;282;0;DOD;582;Yes;1
+129;90;0;48;35;944;C;0;1;ACT;;0;4A;HGSOC;1;0;1;543;0;DOD;1763;Yes;1
+130;90;0;76;26;251;C;0;;ACT;;0;2C;ovarianmucinousgrade2intestinal;1;0;1;;0;NED;3292;No;0
+131;70;5;60;18;109;C;0;;ACT;;0;3C;HGSOC;1;0;1;630;0;AWD;1803;Yes;0
+132;90;5;67;37;428;C;0;;ACT;;0;3C;HGSOC;0;1;1;;0;DOC;731;No;1
+133;90;0;75;24;27;C;basalcellcarcinomauterineorcervical;;ACT;;0;3C;HGSOC;1;0;1;2351;0;AWD;2700;Yes;0
+134;80;5;48;17;718;C;0;;ACT;;0;3C;HGSOC;1;0;;;;DOD;977;;1
+135;80;5;44;35;976;C;0;1;ACT;;0;3C;HGSOC;1;0;1;795;0;DOC;2414;Yes;1
+136;90;0;43;38;1000;C;0;;ACT;;0;1C;ENDOOV ;1;0;1;;0;NED;3080;No;0
+137;90;0;54;19;2964;C;neurofibromatosisremotebreast;1;NACT;2;68;X;HGSOC;1;0;1;575;1;DOD;1330;Yes;1
+138;70;5;70;26;1210;Asian;0;;NACT;14;88;X;HGSOC;1;0;0;268;0;DOD;471;Yes;1
+139;80;5;58;33;2162;C;0;;ACT;;0;3C;HGSOC;0;1;0;242;1;DOD;657;Yes;1
+140;70;5;71;21;1046;C;skincancer;;NACT;4;80;X;HGSOC;1;0;;;0;;206;;
+141;70;5;31;31;575;C;0;1;NACT;12;64;X;HGSOC;1;0;1;613;1;DOD;1628;Yes;1
+142;90;0;75;22;95;C;0;0;ACT;;0;1B;HGSOC;1;0;1;1273;0;DOD;1983;Yes;1
+143;80;10;64;28;22;C;0;;ACT;;0;1A;HGSOC;1;0;;;0;NED;3096;No;0
+144;90;0;44;35;377;hispanic;0;1;ACT;;0;4A;HGSOC;1;0;1;455;0;DOD;949;Yes;1
+145;70;0;58;42;222;C;0;0;ACT;;0;3C;highgradetubal;0;1;0;510;1;DOD;849;Yes;1
+146;70;0;71;21;10824;C;0;;NACT;37;136;X;HGSOC;1;0;0;263;0;DOD;352;Yes;1
+147;90;0;51;31;16;C;0;;ACT;;0;2A;HGSOC;1;0;;;;NED;2854;;0
+148;90;5;66;37;1586;C;0;1;ACT;;0;2C;HGSOC;1;0;1;617;0;AWD;1925;Yes;0
+149;70;0;52;26;7363;C;papillarythyroid;;NACT;2;50;X;HGSOC;1;0;1;;0;NED;2706;No;0
+150;90;0;51;26;;C;0;0;ACT;;0;3A;HGSOC;1;0;1;630;0;DOD;2551;Yes;1
+151;80;0;64;;1929;C;0;0;ACT;;0;3C;HGSOC;0;1;1;884;0;DOD;1420;Yes;1
+152;70;5;67;28;15;C;0;0;ACT;;0;2B;ENDOOV ;1;0;1;;0;NED;2867;No;0
+153;80;5;64;23;968;C;othermalignantneoplasmsitenotspecified;2;NACT;19;172;X;HGSOC;1;0;1;361;1;DOD;2336;Yes;1
+154;80;5;54;34;324;C;0;0;ACT;;0;3A;HGSOC;1;0;1;465;1;DOD;2645;Yes;1
+155;90;0;50;32;1218;C;0;;ACT;;0;1C;ENDOOV ;;;;;0;DOC;2743;No;1
+156;90;5;43;38;;C;0;0;ACT;;0;;HGSOC;;;1;6940;0;DOD;9303;Yes;1
+157;80;5;56;36;1971;C;0;2;NACT;8;94;X;ENDOOV ;1;0;1;;0;DOD;1667;No;1
+158;90;5;46;31;853;C;0;0;ACT;;0;3C;HGSOC;1;0;1;1892;0;AWD;2778;Yes;0
+159;90;0;66;23;14365;C;0;;ACT;;0;3C;HGSOC;1;0;1;1209;0;DOD;2073;Yes;1
+160;90;5;59;27;89;C;breast;0;NACT;22;278;X;HGSOC;0;1;1;160;1;DOD;328;Yes;1
+161;90;0;59;28;12;C;0;MSH6;ACT;;0;1C;ENDOOV ;1;0;1;;0;NED;2642;No;0
+162;80;0;75;20;7482;C;0;0;NACT;49;143;X;serousofMullerianorigin;1;0;1;536;0;DOD;2353;Yes;1
+163;80;10;24;21;;hispanic;thymiccarcinoma;MSH2;ACT;;0;1C;mucinouscarcinoma;1;0;;;0;DOD;585;No;1
+164;80;5;68;21;249;C;0;0;ACT;;0;1B;HGSOC;1;0;1;;0;NED;2699;No;0
+165;90;5;51;37;;C;0;;ACT;;0;1A;HGSOC;1;0;1;;0;NED;2674;No;0
+166;90;5;63;23;;C;0;FBXW7;ACT;;0;3C;HGSOC;1;0;0;464;1;DOD;1184;Yes;1
+167;90;5;64;21;624;C;breast;0;ACT;;0;3C;HGSOC;0;1;1;;0;DOC;2593;No;1
+168;90;5;80;30;;C;0;0;ACT;;0;3B;HGSOC;1;0;1;;0;NED;2631;No;0
+169;80;5;75;35;245;C;0;;NACT;10;78;X;HGSOC;1;0;1;1003;0;DOD;1442;Yes;1
+170;70;5;55;21;3071;C;0;;ACT;;0;3C;HGSOC;1;0;1;;0;NED;2345;No;0
+171;80;5;60;34;500;C;0;;NACT;14;145;X;HGSOC;1;0;1;302;0;DOD;456;Yes;1
+172;90;0;56;25;371;C;concomitantmetastaticpancreaticcanceridentifiedatsurgery;;ACT;;0;1A;HGSOC;0;1;;;0;DOD;619;No;1
+173;90;5;72;33;;C;0;;ACT;;0;3C;HGSOC;1;0;1;1038;0;DOD;2404;Yes;1
+174;50;0;54;36;8529;C;0;0;NACT;15;127;X;HGSPC;1;0;1;387;0;DOD;660;Yes;1
+175;80;5;59;44;378;C;0;;ACT;;0;3C;HGSOC;1;0;1;947;0;DOD;1930;Yes;1
+176;70;0;56;29;1812;C;0;0;ACT;;0;3C;HGSOC;1;0;1;2101;0;DOD;2223;Yes;1
+177;90;0;60;32;919;C;colon;0;NACT;10;70;X;HGSOC;1;0;1;450;1;DOD;1743;Yes;1
+178;80;0;67;27;107;C;basalcellcarcinoma;0;NACT;50;603;3C;HGSOC;0;1;1;416;0;DOD;977;Yes;1
+179;80;0;83;25;;C;0;;NACT;15;62;X;HGSOC;1;0;1;;;DOD;1070;;1
+180;70;10;64;25;88;C;0;0;ACT;;0;1A;1Aendometrialadenocarcinomag31Aclearcellofovary;1;0;1;;0;NED;2274;No;0
+181;90;5;20;27;229;C;0;0;ACT;;0;3C;HGSOC;1;0;1;1170;0;AWD;4810;Yes;0
+182;80;0;81;28;990;C;0;;NACT;19;102;X;HGSOC;1;0;0;381;0;DOD;1552;Yes;1
+183;70;5;72;22;597;C;breast;0;NACT;8;88;X;HGSOC;1;0;1;;;DOD;1036;;1
+184;90;0;72;26;3788;C;breast;;NACT;22;91;X;HGSOC;1;0;1;282;0;;468;Yes;
+185;90;0;70;29;1330;C;0;;ACT;;0;3C;HGSOC;1;0;;;;DOD;1020;;1
+186;80;0;84;17;564;C;squamouscellcarcinoma;;ACT;;0;2B;HGSOC;1;0;;;;DOC;1869;No;1
+187;80;0;27;22;922;C;0;0;NACT;11;91;X;serousprimarilylowgradewfociofhighgrade;1;1;1;540;0;DOD;1279;Yes;1

wetransfer_data-zip_2024-05-17_1431/test_metadata/cellsubtype_color_data.csv

@@ -0,0 +1,13 @@
+rgb,hex,cell_subtype
+"(0.6509803921568628, 0.807843137254902, 0.8901960784313725)",#a6cee3,DC
+"(0.12156862745098039, 0.47058823529411764, 0.7058823529411765)",#1f78b4,B
+"(0.6980392156862745, 0.8745098039215686, 0.5411764705882353)",#b2df8a,TCD4
+"(0.2, 0.6274509803921569, 0.17254901960784313)",#33a02c,TCD8
+"(0.984313725490196, 0.6039215686274509, 0.6)",#fb9a99,M1
+"(0.8901960784313725, 0.10196078431372549, 0.10980392156862745)",#e31a1c,M2
+"(0.9921568627450981, 0.7490196078431373, 0.43529411764705883)",#fdbf6f,Treg
+"(1.0, 0.4980392156862745, 0.0)",#ff7f00,IMMUNE_OTHER
+"(0.792156862745098, 0.6980392156862745, 0.8392156862745098)",#cab2d6,CANCER
+"(0.41568627450980394, 0.23921568627450981, 0.6039215686274509)",#6a3d9a,αSMA_myCAF
+"(1.0, 1.0, 0.6)",#ffff99,STROMA_OTHER
+"(0.6941176470588235, 0.34901960784313724, 0.1568627450980392)",#b15928,ENDOTHELIAL

wetransfer_data-zip_2024-05-17_1431/test_metadata/celltype_color_data.csv

@@ -0,0 +1,5 @@
+rgb,hex,cell_type
+"(0.1333, 0.5451, 0.1333)",#228b22,CANCER
+"(0.4, 0.4, 0.4)",#666666,STROMA
+"(1.0, 1.0, 0.0)",#ffff00,IMMUNE
+"(0.502, 0.0, 0.502)",#800080,ENDOTHELIAL

wetransfer_data-zip_2024-05-17_1431/test_metadata/channel_color_data.csv

@@ -0,0 +1,5 @@
+rgb,hex,Channel
+"(0.00784313725490196, 0.24313725490196078, 1.0)",#023eff,c2
+"(1.0, 0.48627450980392156, 0.0)",#ff7c00,c3
+"(0.10196078431372549, 0.788235294117647, 0.2196078431372549)",#1ac938,c4
+"(0.9098039215686274, 0.0, 0.043137254901960784)",#e8000b,c5

wetransfer_data-zip_2024-05-17_1431/test_metadata/combined_metadata.csv

@@ -0,0 +1,91 @@
+Channel,Name,Cycle,ChannelIndex,ExposureTime,ExposureTimeUnit,Fluor,AcquisitionMode,IlluminationType,ContrastMethod,ExcitationWavelength,ExcitationWavelengthUnit,EmissionWavelength,EmissionWavelengthUnit,Color
+0,DAPI0,0,0,30.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+1,AF488,0,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+2,AF555,0,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+3,AF647,0,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+4,AF750,0,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+5,DAPI1,1,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+6,ColVI,1,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+7,CD31,1,2,1200.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+8,CD4,1,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+9,Ecad,1,4,800.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+10,DAPI2,2,0,15.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+11,Desmin,2,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+12,B7H4,2,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+13,CD8,2,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+14,CD20,2,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+15,DAPI3,3,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+16,aSMA,3,1,50.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+17,CD68,3,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+18,PD1,3,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+19,CD45,3,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+20,DAPI4,4,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+21,Vimentin,4,1,150.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+22,AXL,4,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+23,PDL1,4,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+24,FOXP3,4,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+25,DAPI5,5,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+26,r5c2,5,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+27,CA9,5,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+28,CD163,5,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+29,Ki67,5,4,1000.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+30,DAPI6,6,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+31,CKs,6,1,200.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+32,Fibronectin,6,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+33,CD44,6,3,1200.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+34,HLA,6,4,500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+35,DAPI7,7,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+36,r7c2,7,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+37,PDGFR,7,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+38,MMP9,7,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+39,GATA3,7,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+40,DAPI8,8,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+41,r8c2,8,1,25.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+42,CD11c,8,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+43,Sting,8,3,1000.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+44,CD11b,8,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+0,DAPI0,0,0,30.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+1,AF488,0,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+2,AF555,0,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+3,AF647,0,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+4,AF750,0,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+5,DAPI1,1,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+6,ColVI,1,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+7,CD31,1,2,1200.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+8,CD4,1,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+9,Ecad,1,4,800.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+10,DAPI2,2,0,15.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+11,Desmin,2,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+12,B7H4,2,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+13,CD8,2,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+14,CD20,2,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+15,DAPI3,3,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+16,aSMA,3,1,50.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+17,CD68,3,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+18,PD1,3,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+19,CD45,3,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+20,DAPI4,4,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+21,Vimentin,4,1,150.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+22,AXL,4,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+23,PDL1,4,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+24,FOXP3,4,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+25,DAPI5,5,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+26,r5c2,5,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+27,CA9,5,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+28,CD163,5,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+29,Ki67,5,4,1000.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+30,DAPI6,6,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+31,CKs,6,1,200.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+32,Fibronectin,6,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+33,CD44,6,3,1200.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+34,HLA,6,4,500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+35,DAPI7,7,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+36,r7c2,7,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+37,PDGFR,7,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+38,MMP9,7,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+39,GATA3,7,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+40,DAPI8,8,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+41,r8c2,8,1,25.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+42,CD11c,8,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+43,Sting,8,3,1000.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+44,CD11b,8,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF

wetransfer_data-zip_2024-05-17_1431/test_metadata/full_to_short_column_names.csv

@@ -0,0 +1,109 @@
+full_name,short_name
+AF488_Cell_Intensity_Average,AF488_Cell
+AF488_Cytoplasm_Intensity_Average,AF488_Cytoplasm
+AF488_Nucleus_Intensity_Average,AF488_Nucleus
+AF555_Cell_Intensity_Average,AF555_Cell
+AF555_Cytoplasm_Intensity_Average,AF555_Cytoplasm
+AF555_Nucleus_Intensity_Average,AF555_Nucleus
+AF647_Cell_Intensity_Average,AF647_Cell
+AF647_Cytoplasm_Intensity_Average,AF647_Cytoplasm
+AF647_Nucleus_Intensity_Average,AF647_Nucleus
+AF750_Cell_Intensity_Average,AF750_Cell
+AF750_Cytoplasm_Intensity_Average,AF750_Cytoplasm
+AF750_Nucleus_Intensity_Average,AF750_Nucleus
+aSMA_Cell_Intensity_Average,aSMA_Cell
+aSMA_Cytoplasm_Intensity_Average,aSMA_Cytoplasm
+aSMA_Nucleus_Intensity_Average,aSMA_Nucleus
+AXL_Cell_Intensity_Average,AXL_Cell
+AXL_Cytoplasm_Intensity_Average,AXL_Cytoplasm
+AXL_Nucleus_Intensity_Average,AXL_Nucleus
+B7H4_Cell_Intensity_Average,B7H4_Cell
+B7H4_Cytoplasm_Intensity_Average,B7H4_Cytoplasm
+B7H4_Nucleus_Intensity_Average,B7H4_Nucleus
+CA9_Cell_Intensity_Average,CA9_Cell
+CA9_Cytoplasm_Intensity_Average,CA9_Cytoplasm
+CA9_Nucleus_Intensity_Average,CA9_Nucleus
+CD4_Cell_Intensity_Average,CD4_Cell
+CD4_Cytoplasm_Intensity_Average,CD4_Cytoplasm
+CD4_Nucleus_Intensity_Average,CD4_Nucleus
+CD8_Cell_Intensity_Average,CD8_Cell
+CD8_Cytoplasm_Intensity_Average,CD8_Cytoplasm
+CD8_Nucleus_Intensity_Average,CD8_Nucleus
+CD11b_Cell_Intensity_Average,CD11b_Cell
+CD11b_Cytoplasm_Intensity_Average,CD11b_Cytoplasm
+CD11b_Nucleus_Intensity_Average,CD11b_Nucleus
+CD11c_Cell_Intensity_Average,CD11c_Cell
+CD11c_Cytoplasm_Intensity_Average,CD11c_Cytoplasm
+CD11c_Nucleus_Intensity_Average,CD11c_Nucleus
+CD20_Cell_Intensity_Average,CD20_Cell
+CD20_Cytoplasm_Intensity_Average,CD20_Cytoplasm
+CD20_Nucleus_Intensity_Average,CD20_Nucleus
+CD31_Cell_Intensity_Average,CD31_Cell
+CD31_Cytoplasm_Intensity_Average,CD31_Cytoplasm
+CD31_Nucleus_Intensity_Average,CD31_Nucleus
+CD44_Cell_Intensity_Average,CD44_Cell
+CD44_Cytoplasm_Intensity_Average,CD44_Cytoplasm
+CD44_Nucleus_Intensity_Average,CD44_Nucleus
+CD45_Cell_Intensity_Average,CD45_Cell
+CD45_Cytoplasm_Intensity_Average,CD45_Cytoplasm
+CD45_Nucleus_Intensity_Average,CD45_Nucleus
+CD68_Cell_Intensity_Average,CD68_Cell
+CD68_Cytoplasm_Intensity_Average,CD68_Cytoplasm
+CD68_Nucleus_Intensity_Average,CD68_Nucleus
+CD163_Cell_Intensity_Average,CD163_Cell
+CD163_Cytoplasm_Intensity_Average,CD163_Cytoplasm
+CD163_Nucleus_Intensity_Average,CD163_Nucleus
+CKs_Cell_Intensity_Average,CKs_Cell
+CKs_Cytoplasm_Intensity_Average,CKs_Cytoplasm
+CKs_Nucleus_Intensity_Average,CKs_Nucleus
+ColVI_Cell_Intensity_Average,ColVI_Cell
+ColVI_Cytoplasm_Intensity_Average,ColVI_Cytoplasm
+ColVI_Nucleus_Intensity_Average,ColVI_Nucleus
+Desmin_Cell_Intensity_Average,Desmin_Cell
+Desmin_Cytoplasm_Intensity_Average,Desmin_Cytoplasm
+Desmin_Nucleus_Intensity_Average,Desmin_Nucleus
+Ecad_Cell_Intensity_Average,Ecad_Cell
+Ecad_Cytoplasm_Intensity_Average,Ecad_Cytoplasm
+Ecad_Nucleus_Intensity_Average,Ecad_Nucleus
+Fibronectin_Cell_Intensity_Average,Fibronectin_Cell
+Fibronectin_Cytoplasm_Intensity_Average,Fibronectin_Cytoplasm
+Fibronectin_Nucleus_Intensity_Average,Fibronectin_Nucleus
+FOXP3_Cell_Intensity_Average,FOXP3_Cell
+FOXP3_Cytoplasm_Intensity_Average,FOXP3_Cytoplasm
+FOXP3_Nucleus_Intensity_Average,FOXP3_Nucleus
+GATA3_Cell_Intensity_Average,GATA3_Cell
+GATA3_Cytoplasm_Intensity_Average,GATA3_Cytoplasm
+GATA3_Nucleus_Intensity_Average,GATA3_Nucleus
+HLA_Cell_Intensity_Average,HLA_Cell
+HLA_Cytoplasm_Intensity_Average,HLA_Cytoplasm
+HLA_Nucleus_Intensity_Average,HLA_Nucleus
+Ki67_Cell_Intensity_Average,Ki67_Cell
+Ki67_Cytoplasm_Intensity_Average,Ki67_Cytoplasm
+Ki67_Nucleus_Intensity_Average,Ki67_Nucleus
+MMP9_Cell_Intensity_Average,MMP9_Cell
+MMP9_Cytoplasm_Intensity_Average,MMP9_Cytoplasm
+MMP9_Nucleus_Intensity_Average,MMP9_Nucleus
+PD1_Cell_Intensity_Average,PD1_Cell
+PD1_Cytoplasm_Intensity_Average,PD1_Cytoplasm
+PD1_Nucleus_Intensity_Average,PD1_Nucleus
+PDGFR_Cell_Intensity_Average,PDGFR_Cell
+PDGFR_Cytoplasm_Intensity_Average,PDGFR_Cytoplasm
+PDGFR_Nucleus_Intensity_Average,PDGFR_Nucleus
+PDL1_Cell_Intensity_Average,PDL1_Cell
+PDL1_Cytoplasm_Intensity_Average,PDL1_Cytoplasm
+PDL1_Nucleus_Intensity_Average,PDL1_Nucleus
+r5c2_Cell_Intensity_Average,r5c2_Cell
+r5c2_Cytoplasm_Intensity_Average,r5c2_Cytoplasm
+r5c2_Nucleus_Intensity_Average,r5c2_Nucleus
+r7c2_Cell_Intensity_Average,r7c2_Cell
+r7c2_Cytoplasm_Intensity_Average,r7c2_Cytoplasm
+r7c2_Nucleus_Intensity_Average,r7c2_Nucleus
+r8c2_Cell_Intensity_Average,r8c2_Cell
+r8c2_Cytoplasm_Intensity_Average,r8c2_Cytoplasm
+r8c2_Nucleus_Intensity_Average,r8c2_Nucleus
+Sting_Cell_Intensity_Average,Sting_Cell
+Sting_Cytoplasm_Intensity_Average,Sting_Cytoplasm
+Sting_Nucleus_Intensity_Average,Sting_Nucleus
+Vimentin_Cell_Intensity_Average,Vimentin_Cell
+Vimentin_Cytoplasm_Intensity_Average,Vimentin_Cytoplasm
+Vimentin_Nucleus_Intensity_Average,Vimentin_Nucleus

wetransfer_data-zip_2024-05-17_1431/test_metadata/immunecheckpoint_color_data.csv

@@ -0,0 +1,6 @@
+rgb,hex,immune_checkpoint
+"(0.9677975592919913, 0.44127456009157356, 0.5358103155058701)",#f77189,B7H4
+"(0.3126890019504329, 0.6928754610296064, 0.1923704830330379)",#50b131,PDL1
+"(0.23299120924703914, 0.639586552066035, 0.9260706093977744)",#3ba3ec,PD1
+"(0.6402432806212122, 0.56707501056059, 0.36409039926945397)",#a3915d,B7H4_PDL1
+"(0.5044925901631545, 0.5912455243957383, 0.5514171359788941)",#81978d,None

wetransfer_data-zip_2024-05-17_1431/test_metadata/marker_intensity_metadata.csv

@@ -0,0 +1,109 @@
+Round,Target,Exp,Channel,target_lower,full_column,marker,localisation
+R0,AF488,300,c2,af488,AF488_Cell_Intensity_Average,AF488,cell
+R0,AF488,300,c2,af488,AF488_Cytoplasm_Intensity_Average,AF488,cytoplasm
+R0,AF488,300,c2,af488,AF488_Nucleus_Intensity_Average,AF488,nucleus
+R0,AF555,1500,c3,af555,AF555_Cell_Intensity_Average,AF555,cell
+R0,AF555,1500,c3,af555,AF555_Cytoplasm_Intensity_Average,AF555,cytoplasm
+R0,AF555,1500,c3,af555,AF555_Nucleus_Intensity_Average,AF555,nucleus
+R0,AF647,1500,c4,af647,AF647_Cell_Intensity_Average,AF647,cell
+R0,AF647,1500,c4,af647,AF647_Cytoplasm_Intensity_Average,AF647,cytoplasm
+R0,AF647,1500,c4,af647,AF647_Nucleus_Intensity_Average,AF647,nucleus
+R0,AF750,1500,c5,af750,AF750_Cell_Intensity_Average,AF750,cell
+R0,AF750,1500,c5,af750,AF750_Cytoplasm_Intensity_Average,AF750,cytoplasm
+R0,AF750,1500,c5,af750,AF750_Nucleus_Intensity_Average,AF750,nucleus
+R1,ColVI,300,c2,colvi,ColVI_Cell_Intensity_Average,ColVI,cell
+R1,ColVI,300,c2,colvi,ColVI_Cytoplasm_Intensity_Average,ColVI,cytoplasm
+R1,ColVI,300,c2,colvi,ColVI_Nucleus_Intensity_Average,ColVI,nucleus
+R1,CD31,1200,c3,cd31,CD31_Cell_Intensity_Average,CD31,cell
+R1,CD31,1200,c3,cd31,CD31_Cytoplasm_Intensity_Average,CD31,cytoplasm
+R1,CD31,1200,c3,cd31,CD31_Nucleus_Intensity_Average,CD31,nucleus
+R1,CD4,1500,c4,cd4,CD4_Cell_Intensity_Average,CD4,cell
+R1,CD4,1500,c4,cd4,CD4_Cytoplasm_Intensity_Average,CD4,cytoplasm
+R1,CD4,1500,c4,cd4,CD4_Nucleus_Intensity_Average,CD4,nucleus
+R1,Ecad,800,c5,ecad,Ecad_Cell_Intensity_Average,Ecad,cell
+R1,Ecad,800,c5,ecad,Ecad_Cytoplasm_Intensity_Average,Ecad,cytoplasm
+R1,Ecad,800,c5,ecad,Ecad_Nucleus_Intensity_Average,Ecad,nucleus
+R2,Desmin,300,c2,desmin,Desmin_Cell_Intensity_Average,Desmin,cell
+R2,Desmin,300,c2,desmin,Desmin_Cytoplasm_Intensity_Average,Desmin,cytoplasm
+R2,Desmin,300,c2,desmin,Desmin_Nucleus_Intensity_Average,Desmin,nucleus
+R2,B7H4,1500,c3,b7h4,B7H4_Cell_Intensity_Average,B7H4,cell
+R2,B7H4,1500,c3,b7h4,B7H4_Cytoplasm_Intensity_Average,B7H4,cytoplasm
+R2,B7H4,1500,c3,b7h4,B7H4_Nucleus_Intensity_Average,B7H4,nucleus
+R2,CD8,1500,c4,cd8,CD8_Cell_Intensity_Average,CD8,cell
+R2,CD8,1500,c4,cd8,CD8_Cytoplasm_Intensity_Average,CD8,cytoplasm
+R2,CD8,1500,c4,cd8,CD8_Nucleus_Intensity_Average,CD8,nucleus
+R2,CD20,1500,c5,cd20,CD20_Cell_Intensity_Average,CD20,cell
+R2,CD20,1500,c5,cd20,CD20_Cytoplasm_Intensity_Average,CD20,cytoplasm
+R2,CD20,1500,c5,cd20,CD20_Nucleus_Intensity_Average,CD20,nucleus
+R3,aSMA,50,c2,asma,aSMA_Cell_Intensity_Average,aSMA,cell
+R3,aSMA,50,c2,asma,aSMA_Cytoplasm_Intensity_Average,aSMA,cytoplasm
+R3,aSMA,50,c2,asma,aSMA_Nucleus_Intensity_Average,aSMA,nucleus
+R3,CD68,1500,c3,cd68,CD68_Cell_Intensity_Average,CD68,cell
+R3,CD68,1500,c3,cd68,CD68_Cytoplasm_Intensity_Average,CD68,cytoplasm
+R3,CD68,1500,c3,cd68,CD68_Nucleus_Intensity_Average,CD68,nucleus
+R3,PD1,1500,c4,pd1,PD1_Cell_Intensity_Average,PD1,cell
+R3,PD1,1500,c4,pd1,PD1_Cytoplasm_Intensity_Average,PD1,cytoplasm
+R3,PD1,1500,c4,pd1,PD1_Nucleus_Intensity_Average,PD1,nucleus
+R3,CD45,1500,c5,cd45,CD45_Cell_Intensity_Average,CD45,cell
+R3,CD45,1500,c5,cd45,CD45_Cytoplasm_Intensity_Average,CD45,cytoplasm
+R3,CD45,1500,c5,cd45,CD45_Nucleus_Intensity_Average,CD45,nucleus
+R4,Vimentin,150,c2,vimentin,Vimentin_Cell_Intensity_Average,Vimentin,cell
+R4,Vimentin,150,c2,vimentin,Vimentin_Cytoplasm_Intensity_Average,Vimentin,cytoplasm
+R4,Vimentin,150,c2,vimentin,Vimentin_Nucleus_Intensity_Average,Vimentin,nucleus
+R4,AXL,1500,c3,axl,AXL_Cell_Intensity_Average,AXL,cell
+R4,AXL,1500,c3,axl,AXL_Cytoplasm_Intensity_Average,AXL,cytoplasm
+R4,AXL,1500,c3,axl,AXL_Nucleus_Intensity_Average,AXL,nucleus
+R4,PDL1,1500,c4,pdl1,PDL1_Cell_Intensity_Average,PDL1,cell
+R4,PDL1,1500,c4,pdl1,PDL1_Cytoplasm_Intensity_Average,PDL1,cytoplasm
+R4,PDL1,1500,c4,pdl1,PDL1_Nucleus_Intensity_Average,PDL1,nucleus
+R4,FOXP3,1500,c5,foxp3,FOXP3_Cell_Intensity_Average,FOXP3,cell
+R4,FOXP3,1500,c5,foxp3,FOXP3_Cytoplasm_Intensity_Average,FOXP3,cytoplasm
+R4,FOXP3,1500,c5,foxp3,FOXP3_Nucleus_Intensity_Average,FOXP3,nucleus
+R5,r5c2,20,c2,r5c2,r5c2_Cell_Intensity_Average,r5c2,cell
+R5,r5c2,20,c2,r5c2,r5c2_Cytoplasm_Intensity_Average,r5c2,cytoplasm
+R5,r5c2,20,c2,r5c2,r5c2_Nucleus_Intensity_Average,r5c2,nucleus
+R5,CA9,1500,c3,ca9,CA9_Cell_Intensity_Average,CA9,cell
+R5,CA9,1500,c3,ca9,CA9_Cytoplasm_Intensity_Average,CA9,cytoplasm
+R5,CA9,1500,c3,ca9,CA9_Nucleus_Intensity_Average,CA9,nucleus
+R5,CD163,1500,c4,cd163,CD163_Cell_Intensity_Average,CD163,cell
+R5,CD163,1500,c4,cd163,CD163_Cytoplasm_Intensity_Average,CD163,cytoplasm
+R5,CD163,1500,c4,cd163,CD163_Nucleus_Intensity_Average,CD163,nucleus
+R5,Ki67,1000,c5,ki67,Ki67_Cell_Intensity_Average,Ki67,cell
+R5,Ki67,1000,c5,ki67,Ki67_Cytoplasm_Intensity_Average,Ki67,cytoplasm
+R5,Ki67,1000,c5,ki67,Ki67_Nucleus_Intensity_Average,Ki67,nucleus
+R6,CKs,200,c2,cks,CKs_Cell_Intensity_Average,CKs,cell
+R6,CKs,200,c2,cks,CKs_Cytoplasm_Intensity_Average,CKs,cytoplasm
+R6,CKs,200,c2,cks,CKs_Nucleus_Intensity_Average,CKs,nucleus
+R6,Fibronectin,1500,c3,fibronectin,Fibronectin_Cell_Intensity_Average,Fibronectin,cell
+R6,Fibronectin,1500,c3,fibronectin,Fibronectin_Cytoplasm_Intensity_Average,Fibronectin,cytoplasm
+R6,Fibronectin,1500,c3,fibronectin,Fibronectin_Nucleus_Intensity_Average,Fibronectin,nucleus
+R6,CD44,1200,c4,cd44,CD44_Cell_Intensity_Average,CD44,cell
+R6,CD44,1200,c4,cd44,CD44_Cytoplasm_Intensity_Average,CD44,cytoplasm
+R6,CD44,1200,c4,cd44,CD44_Nucleus_Intensity_Average,CD44,nucleus
+R6,HLA,500,c5,hla,HLA_Cell_Intensity_Average,HLA,cell
+R6,HLA,500,c5,hla,HLA_Cytoplasm_Intensity_Average,HLA,cytoplasm
+R6,HLA,500,c5,hla,HLA_Nucleus_Intensity_Average,HLA,nucleus
+R7,r7c2,20,c2,r7c2,r7c2_Cell_Intensity_Average,r7c2,cell
+R7,r7c2,20,c2,r7c2,r7c2_Cytoplasm_Intensity_Average,r7c2,cytoplasm
+R7,r7c2,20,c2,r7c2,r7c2_Nucleus_Intensity_Average,r7c2,nucleus
+R7,PDGFR,1500,c3,pdgfr,PDGFR_Cell_Intensity_Average,PDGFR,cell
+R7,PDGFR,1500,c3,pdgfr,PDGFR_Cytoplasm_Intensity_Average,PDGFR,cytoplasm
+R7,PDGFR,1500,c3,pdgfr,PDGFR_Nucleus_Intensity_Average,PDGFR,nucleus
+R7,MMP9,1500,c4,mmp9,MMP9_Cell_Intensity_Average,MMP9,cell
+R7,MMP9,1500,c4,mmp9,MMP9_Cytoplasm_Intensity_Average,MMP9,cytoplasm
+R7,MMP9,1500,c4,mmp9,MMP9_Nucleus_Intensity_Average,MMP9,nucleus
+R7,GATA3,1500,c5,gata3,GATA3_Cell_Intensity_Average,GATA3,cell
+R7,GATA3,1500,c5,gata3,GATA3_Cytoplasm_Intensity_Average,GATA3,cytoplasm
+R7,GATA3,1500,c5,gata3,GATA3_Nucleus_Intensity_Average,GATA3,nucleus
+R8,r8c2,25,c2,r8c2,r8c2_Cell_Intensity_Average,r8c2,cell
+R8,r8c2,25,c2,r8c2,r8c2_Cytoplasm_Intensity_Average,r8c2,cytoplasm
+R8,r8c2,25,c2,r8c2,r8c2_Nucleus_Intensity_Average,r8c2,nucleus
+R8,CD11c,1500,c3,cd11c,CD11c_Cell_Intensity_Average,CD11c,cell
+R8,CD11c,1500,c3,cd11c,CD11c_Cytoplasm_Intensity_Average,CD11c,cytoplasm
+R8,CD11c,1500,c3,cd11c,CD11c_Nucleus_Intensity_Average,CD11c,nucleus
+R8,Sting,1000,c4,sting,Sting_Cell_Intensity_Average,Sting,cell
+R8,Sting,1000,c4,sting,Sting_Cytoplasm_Intensity_Average,Sting,cytoplasm
+R8,Sting,1000,c4,sting,Sting_Nucleus_Intensity_Average,Sting,nucleus
+R8,CD11b,1500,c5,cd11b,CD11b_Cell_Intensity_Average,CD11b,cell
+R8,CD11b,1500,c5,cd11b,CD11b_Cytoplasm_Intensity_Average,CD11b,cytoplasm
+R8,CD11b,1500,c5,cd11b,CD11b_Nucleus_Intensity_Average,CD11b,nucleus

wetransfer_data-zip_2024-05-17_1431/test_metadata/not_intensities.csv

@@ -0,0 +1,12 @@
+Marker
+Cell_Size
+Nuc_X
+Nuc_Y_Inv
+ROI_index
+Nucleus_Size
+Nucleus_Roundness
+Sample_ID
+Round
+Exp
+Channel
+Target

wetransfer_data-zip_2024-05-17_1431/test_metadata/round_color_data.csv

@@ -0,0 +1,10 @@
+rgb,hex,Round
+"(0.28685356234627135, 0.13009829239513535, 0.23110332132624437)",#49213b,R0
+"(0.36541462435986094, 0.2025447048359916, 0.37693310021636883)",#5d3460,R1
+"(0.40867533458903105, 0.2940761173840091, 0.5166711878800253)",#684b84,R2
+"(0.42890613750051265, 0.4082290173220481, 0.6335348887063806)",#6d68a2,R3
+"(0.4444462906865238, 0.5264664993764805, 0.7056321892616532)",#7186b4,R4
+"(0.47707206309601013, 0.6427061780374552, 0.7418477948908153)",#7aa4bd,R5
+"(0.5414454866716836, 0.7466759172596551, 0.7572905778378964)",#8abec1,R6
+"(0.6414710091647722, 0.8321551072276492, 0.7746773027952071)",#a4d4c6,R7
+"(0.7684256891219349, 0.8992667116749021, 0.8171383269422353)",#c4e5d0,R8

wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv

@@ -0,0 +1,5 @@
+rgb,hex,Sample_ID
+"(0.9677975592919913, 0.44127456009157356, 0.5358103155058701)",#f77189,DD3S1.csv
+"(0.5920891529639701, 0.6418467016378244, 0.1935069134991043)",#97a431,DD3S2.csv
+"(0.21044753832183283, 0.6773105080456748, 0.6433941168468681)",#36ada4,DD3S3.csv
+"(0.5019607843137255, 0.5019607843137255, 0.5019607843137255)",#808080,TMA.csv

wetransfer_data-zip_2024-05-17_1431/test_metadata/short_to_full_column_names.csv

@@ -0,0 +1,109 @@
+short_name,full_name
+AF488_Cell,AF488_Cell_Intensity_Average
+AF488_Cytoplasm,AF488_Cytoplasm_Intensity_Average
+AF488_Nucleus,AF488_Nucleus_Intensity_Average
+AF555_Cell,AF555_Cell_Intensity_Average
+AF555_Cytoplasm,AF555_Cytoplasm_Intensity_Average
+AF555_Nucleus,AF555_Nucleus_Intensity_Average
+AF647_Cell,AF647_Cell_Intensity_Average
+AF647_Cytoplasm,AF647_Cytoplasm_Intensity_Average
+AF647_Nucleus,AF647_Nucleus_Intensity_Average
+AF750_Cell,AF750_Cell_Intensity_Average
+AF750_Cytoplasm,AF750_Cytoplasm_Intensity_Average
+AF750_Nucleus,AF750_Nucleus_Intensity_Average
+aSMA_Cell,aSMA_Cell_Intensity_Average
+aSMA_Cytoplasm,aSMA_Cytoplasm_Intensity_Average
+aSMA_Nucleus,aSMA_Nucleus_Intensity_Average
+AXL_Cell,AXL_Cell_Intensity_Average
+AXL_Cytoplasm,AXL_Cytoplasm_Intensity_Average
+AXL_Nucleus,AXL_Nucleus_Intensity_Average
+B7H4_Cell,B7H4_Cell_Intensity_Average
+B7H4_Cytoplasm,B7H4_Cytoplasm_Intensity_Average
+B7H4_Nucleus,B7H4_Nucleus_Intensity_Average
+CA9_Cell,CA9_Cell_Intensity_Average
+CA9_Cytoplasm,CA9_Cytoplasm_Intensity_Average
+CA9_Nucleus,CA9_Nucleus_Intensity_Average
+CD4_Cell,CD4_Cell_Intensity_Average
+CD4_Cytoplasm,CD4_Cytoplasm_Intensity_Average
+CD4_Nucleus,CD4_Nucleus_Intensity_Average
+CD8_Cell,CD8_Cell_Intensity_Average
+CD8_Cytoplasm,CD8_Cytoplasm_Intensity_Average
+CD8_Nucleus,CD8_Nucleus_Intensity_Average
+CD11b_Cell,CD11b_Cell_Intensity_Average
+CD11b_Cytoplasm,CD11b_Cytoplasm_Intensity_Average
+CD11b_Nucleus,CD11b_Nucleus_Intensity_Average
+CD11c_Cell,CD11c_Cell_Intensity_Average
+CD11c_Cytoplasm,CD11c_Cytoplasm_Intensity_Average
+CD11c_Nucleus,CD11c_Nucleus_Intensity_Average
+CD20_Cell,CD20_Cell_Intensity_Average
+CD20_Cytoplasm,CD20_Cytoplasm_Intensity_Average
+CD20_Nucleus,CD20_Nucleus_Intensity_Average
+CD31_Cell,CD31_Cell_Intensity_Average
+CD31_Cytoplasm,CD31_Cytoplasm_Intensity_Average
+CD31_Nucleus,CD31_Nucleus_Intensity_Average
+CD44_Cell,CD44_Cell_Intensity_Average
+CD44_Cytoplasm,CD44_Cytoplasm_Intensity_Average
+CD44_Nucleus,CD44_Nucleus_Intensity_Average
+CD45_Cell,CD45_Cell_Intensity_Average
+CD45_Cytoplasm,CD45_Cytoplasm_Intensity_Average
+CD45_Nucleus,CD45_Nucleus_Intensity_Average
+CD68_Cell,CD68_Cell_Intensity_Average
+CD68_Cytoplasm,CD68_Cytoplasm_Intensity_Average
+CD68_Nucleus,CD68_Nucleus_Intensity_Average
+CD163_Cell,CD163_Cell_Intensity_Average
+CD163_Cytoplasm,CD163_Cytoplasm_Intensity_Average
+CD163_Nucleus,CD163_Nucleus_Intensity_Average
+CKs_Cell,CKs_Cell_Intensity_Average
+CKs_Cytoplasm,CKs_Cytoplasm_Intensity_Average
+CKs_Nucleus,CKs_Nucleus_Intensity_Average
+ColVI_Cell,ColVI_Cell_Intensity_Average
+ColVI_Cytoplasm,ColVI_Cytoplasm_Intensity_Average
+ColVI_Nucleus,ColVI_Nucleus_Intensity_Average
+Desmin_Cell,Desmin_Cell_Intensity_Average
+Desmin_Cytoplasm,Desmin_Cytoplasm_Intensity_Average
+Desmin_Nucleus,Desmin_Nucleus_Intensity_Average
+Ecad_Cell,Ecad_Cell_Intensity_Average
+Ecad_Cytoplasm,Ecad_Cytoplasm_Intensity_Average
+Ecad_Nucleus,Ecad_Nucleus_Intensity_Average
+Fibronectin_Cell,Fibronectin_Cell_Intensity_Average
+Fibronectin_Cytoplasm,Fibronectin_Cytoplasm_Intensity_Average
+Fibronectin_Nucleus,Fibronectin_Nucleus_Intensity_Average
+FOXP3_Cell,FOXP3_Cell_Intensity_Average
+FOXP3_Cytoplasm,FOXP3_Cytoplasm_Intensity_Average
+FOXP3_Nucleus,FOXP3_Nucleus_Intensity_Average
+GATA3_Cell,GATA3_Cell_Intensity_Average
+GATA3_Cytoplasm,GATA3_Cytoplasm_Intensity_Average
+GATA3_Nucleus,GATA3_Nucleus_Intensity_Average
+HLA_Cell,HLA_Cell_Intensity_Average
+HLA_Cytoplasm,HLA_Cytoplasm_Intensity_Average
+HLA_Nucleus,HLA_Nucleus_Intensity_Average
+Ki67_Cell,Ki67_Cell_Intensity_Average
+Ki67_Cytoplasm,Ki67_Cytoplasm_Intensity_Average
+Ki67_Nucleus,Ki67_Nucleus_Intensity_Average
+MMP9_Cell,MMP9_Cell_Intensity_Average
+MMP9_Cytoplasm,MMP9_Cytoplasm_Intensity_Average
+MMP9_Nucleus,MMP9_Nucleus_Intensity_Average
+PD1_Cell,PD1_Cell_Intensity_Average
+PD1_Cytoplasm,PD1_Cytoplasm_Intensity_Average
+PD1_Nucleus,PD1_Nucleus_Intensity_Average
+PDGFR_Cell,PDGFR_Cell_Intensity_Average
+PDGFR_Cytoplasm,PDGFR_Cytoplasm_Intensity_Average
+PDGFR_Nucleus,PDGFR_Nucleus_Intensity_Average
+PDL1_Cell,PDL1_Cell_Intensity_Average
+PDL1_Cytoplasm,PDL1_Cytoplasm_Intensity_Average
+PDL1_Nucleus,PDL1_Nucleus_Intensity_Average
+r5c2_Cell,r5c2_Cell_Intensity_Average
+r5c2_Cytoplasm,r5c2_Cytoplasm_Intensity_Average
+r5c2_Nucleus,r5c2_Nucleus_Intensity_Average
+r7c2_Cell,r7c2_Cell_Intensity_Average
+r7c2_Cytoplasm,r7c2_Cytoplasm_Intensity_Average
+r7c2_Nucleus,r7c2_Nucleus_Intensity_Average
+r8c2_Cell,r8c2_Cell_Intensity_Average
+r8c2_Cytoplasm,r8c2_Cytoplasm_Intensity_Average
+r8c2_Nucleus,r8c2_Nucleus_Intensity_Average
+Sting_Cell,Sting_Cell_Intensity_Average
+Sting_Cytoplasm,Sting_Cytoplasm_Intensity_Average
+Sting_Nucleus,Sting_Nucleus_Intensity_Average
+Vimentin_Cell,Vimentin_Cell_Intensity_Average
+Vimentin_Cytoplasm,Vimentin_Cytoplasm_Intensity_Average
+Vimentin_Nucleus,Vimentin_Nucleus_Intensity_Average

wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S1_mt.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5e5d712093b48a1b53b42e9b21a7c5b0a98946f70cd4729a54511329af6b8c9
+size 42782100

wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S2_mt.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6430f2839ce44437a4016c73fb532e8bc704cf9b92b514c0ec771c8a0fee74cb
+size 38749776

wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S3_mt.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:797b5ec539dc0f32407330fd8aedfac86135664ce8387e17ba71b4da8ace1859
+size 72513073

wetransfer_data-zip_2024-05-17_1431/test_mt/TMA_mt.csv

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