adds code for figure 4g

README.md

@@ -9,6 +9,7 @@
 - `doc`: project documentation
 - `literature`: relevant publications
 - `metadata`: additional required data
+- `misc`: miscellaneous scripts
 - `plots`: generated plots
 - `renv`: R environment data
 - `tables`: exported tables
@@ -73,8 +74,8 @@ required by figures and tables.
 - [compare_tumors_pseudobulk.R](compare_tumors_pseudobulk.R) –
   comparison of tumor samples via pseudobulk correlation
 
-  
-  
+
+
 ## Plotting functions
 
 Run these R scripts in arbitrary order to generate exploratory figures:
@@ -87,7 +88,7 @@ Run these R scripts in arbitrary order to generate exploratory figures:
   canonical cell type markers
 - [plot_misc.R](plot_misc.R) -
   miscellaneous plots
-  
+
 
 Run these R scripts in arbitrary order to generate publication figures and tables:
 

misc/plot_figure_4g.py

@@ -0,0 +1,62 @@
+import seaborn as sns
+import pandas as pd
+import matplotlib.pyplot as plt
+from statannot import add_stat_annotation
+import numpy as np
+import argparse
+import os
+
+def parse():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-in', dest = 'in_path', required=True, help='Path to csv file containing normalized single-cell data.')
+    parser.add_argument('-out', dest = 'out_path', required=True, help='Path to output folder.')
+    args = parser.parse_args()
+    return args
+
+
+def split(word: str):
+    return [char for char in word]
+
+def main (args):
+
+    in_path = args.in_path
+    out_path = args.out_path
+
+    df = pd.read_csv(in_path, delimiter=';', decimal=',')
+    col = [i for i in df.columns if 'MXP_c' in i or 'batch' in i or 'cluster' in i]
+
+    crtl_list = ['BM1.2', 'BM1.3', 'BM2.3']
+    inf_list = ['BM2.1', 'BM2.2', 'BM4.1', 'BM1.1', 'BM3.1']
+
+    df = df[col]
+    df = df[df['cluster']=='MO/M']
+    df['inf'] = ['crtl' if df.iloc[i]['batch'] in crtl_list else 'NB' for i in range(0, df.shape[0])]
+    df = df.drop(['batchlegend', 'batch', 'cluster'], axis=1)
+
+    col_list = list(df.columns)
+    col_list.remove('inf')
+
+    df.inf = df.inf.astype("category")
+
+    for marker in col_list:
+
+        medians = df.groupby(['inf'])[marker].median()
+        medians = medians.reindex(index=['crtl', 'NB'], copy=True)
+        vertical_offset = df[marker].median() * 0.05 
+
+        ax = sns.boxplot(x='inf', y=marker, data=df, linewidth=0.4, fliersize=1, width=0.5, order=['crtl', 'NB'])
+
+        plot = add_stat_annotation(ax, data=df, x='inf', y=marker, box_pairs=[('crtl', 'NB')], test='Mann-Whitney', text_format='star', loc='outside', verbose=2)
+
+        for xtick in ax.get_xticks():
+            ax.text(xtick,medians[xtick] + vertical_offset,medians[xtick], horizontalalignment='center',size='x-small',color='black',weight='semibold')
+
+        plt.tight_layout()
+        plt.savefig(os.path.join(out_path, marker +'_expr_crtl_vs_NB.eps'), format='eps')
+        plt.savefig(os.path.join(out_path, marker +'_expr_crtl_vs_NB.pdf'), format='pdf')
+        plt.close()
+
+
+if __name__ == "__main__":
+        args = parse()
+        main(args)