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analyse_TE_genes_and_scores.py

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+### Katharina Sielemann ###
+### [email protected] ###
+### v1 ###
+
+#/FULL/PATH/TO/LIST/OF/TE/GENES.txt (contains one gene per line)
+
+#imports
+import plotly.graph_objs as go
+from plotly.offline import plot
+import scipy.stats, random
+import numpy as np
+
+#read files
+datei = open("/FULL/PATH/TO/LIST/OF/TE/GENES.txt", "r")
+te_genes = datei.readlines()
+datei.close()
+
+datei = open("/FULL/PATH/TO/QUOD/OUTPUT/gene_dispensability_scores.csv", "r")
+ds_results = datei.readlines()
+datei.close()
+
+te_genes = [value.strip() for value in te_genes]
+#extract genes and scores (TE vs. non-TE)
+TE_scores = []
+non_TE_scores = []
+for line in ds_results:
+	line = line.strip().split(",")
+	gene = line[0].split(".")[1]
+	if gene in te_genes:
+		TE_scores.append(float(line[1]))
+	else:
+		non_TE_scores.append(float(line[1]))
+
+
+max_number_1 = max([value for value in list(TE_scores) if value != np.inf])
+max_number_2 = max([value for value in list(non_TE_scores) if value != np.inf])
+max_number = max(max_number_1, max_number_2)
+
+filtered_non_TE_scores = []
+for number in non_TE_scores:
+	if number >= max_number:
+		value = max_number
+		filtered_non_TE_scores.append(value)
+	else:
+		filtered_non_TE_scores.append(number)
+
+filtered_TE_scores = []
+for number in TE_scores:
+	if number >= max_number:
+		value = max_number
+		filtered_TE_scores.append(value)
+	else:
+		filtered_TE_scores.append(number)
+
+#test for significance: Welch's t-test
+print("mean of 'TE' scores: " + str(np.mean(filtered_TE_scores)) + "n=" + str(len(filtered_TE_scores)))
+print("mean of 'non-TE' scores: " + str(np.mean(filtered_non_TE_scores)) + "n=" + str(len(filtered_non_TE_scores)))
+print(scipy.stats.mannwhitneyu(filtered_TE_scores, filtered_non_TE_scores))
+
+