From 168de16c84968848e414252a259848743e42ebbb Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Thu, 10 Oct 2024 16:56:16 +0200
Subject: [PATCH] selection colomn invariant prosit data
---
alignement.py | 54 +-
data_exploration.py | 28 +-
data_viz.py | 7 +-
database/data_ISA_augmented_1.csv | 98322 +++++++++++++++++++++++
database/data_ISA_augmented_2.csv | 103460 ++++++++++++++++++++++++
database/data_ISA_augmented_3.csv | 112555 +++++++++++++++++++++++++++
seq_cutting.py | 2 +-
7 files changed, 314409 insertions(+), 19 deletions(-)
create mode 100644 database/data_ISA_augmented_1.csv
create mode 100644 database/data_ISA_augmented_2.csv
create mode 100644 database/data_ISA_augmented_3.csv
diff --git a/alignement.py b/alignement.py
index 383e6e5..89bd344 100644
--- a/alignement.py
+++ b/alignement.py
@@ -45,6 +45,16 @@ def numerical_to_alphabetical(arr):
seq+=ALPHABET_UNMOD_REV[arr[i]]
return seq
+def numerical_to_alphabetical_str(s):
+ seq = ''
+ s = s.replace('[','')
+ s = s.replace(']', '')
+ arr = s.split(',')
+ arr = list(map(int, arr))
+ for i in range(len(arr)):
+ seq+=ALPHABET_UNMOD_REV[arr[i]]
+ return seq
+
def align(dataset, reference):
seq_ref = reference['Sequence']
seq_common = dataset['Sequence']
@@ -87,7 +97,7 @@ def compare_include_df(df, sub_df, save = True, path = 'temp.png'):
df_sub_value_list.append(r[1]['Retention time'])
except:
pass
-
+ plt.clf()
fig, ax = plt.subplots()
ax.scatter(df_sub_value_list, df_value_list)
x = np.array([min(df_value_list), max(df_value_list)])
@@ -183,16 +193,44 @@ def compare_include_df(df, sub_df, save = True, path = 'temp.png'):
# data_align.to_pickle('database/data_ISA_dual_align.pkl')
+#compare DIANN pred to DIA mesures
+# df_ori = pd.read_csv('database/data_train.csv')
+# df_ori['Sequence']=df_ori['sequence']
+# df_ori['Retention time']=df_ori['irt']
+# df_diann = pd.read_csv('database/CIT_BASE_UP000584719_546.csv')
+#
+# df_ISA = pd.read_pickle('database/data_ISA_dual_align.pkl')
+#
+#
+#
+# df_diann_aligned = align(df_diann, df_ori)
+#
+# df_value_list, df_sub_value_list = compare_include_df(df_diann_aligned, df_ISA, True)
+
+
+
+
+#create augmented dataset from ISA data + column invariant prosit peptides
+df_base = pd.read_pickle('database/data_ISA_aligned_prosit.pkl')
+df_base = df_base[['Sequence','Retention time']]
-df_ori = pd.read_csv('database/data_train.csv')
-df_ori['Sequence']=df_ori['sequence']
-df_ori['Retention time']=df_ori['irt']
-df_diann = pd.read_csv('database/CIT_BASE_UP000584719_546.csv')
+df_1 = pd.read_pickle('database/data_prosit_threshold_1.pkl')
+df_1['Sequence']= df_1['Sequence'].map(numerical_to_alphabetical_str)
-df_ISA = pd.read_pickle('database/data_ISA_dual_align.pkl')
+df_2 = pd.read_pickle('database/data_prosit_threshold_2.pkl')
+df_2['Sequence']= df_2['Sequence'].map(numerical_to_alphabetical_str)
+df_3 = pd.read_pickle('database/data_prosit_threshold_3.pkl')
+df_3['Sequence']= df_3['Sequence'].map(numerical_to_alphabetical_str)
+df_augmented_1 = pd.concat([df_1,df_base],axis=0).reset_index(drop=True)
+df_augmented_1.columns=['sequence','retention_time']
+df_augmented_1.to_csv('database/data_ISA_augmented_1.csv')
-df_diann_aligned = align(df_diann, df_ori)
+df_augmented_2 = pd.concat([df_2,df_base],axis=0).reset_index(drop=True)
+df_augmented_2.columns=['sequence','retention_time']
+df_augmented_2.to_csv('database/data_ISA_augmented_2.csv')
-df_value_list, df_sub_value_list = compare_include_df(df_diann_aligned, df_ISA, True)
+df_augmented_3 = pd.concat([df_3,df_base],axis=0).reset_index(drop=True)
+df_augmented_3.columns=['sequence','retention_time']
+df_augmented_3.to_csv('database/data_ISA_augmented_3.csv')
\ No newline at end of file
diff --git a/data_exploration.py b/data_exploration.py
index 0999118..1d0bb8f 100644
--- a/data_exploration.py
+++ b/data_exploration.py
@@ -287,11 +287,31 @@ def RT_distrib(Y, f_name):
#
#ISA DATA
-df = pd.read_pickle('database/data_ISA_aligned_prosit.pkl')
-seq = df['Sequence'].unique()
+# df = pd.read_pickle('database/data_ISA_aligned_prosit.pkl')
+# seq = df['Sequence'].unique()
# rt = df['Retention time']
-df_mean = df.groupby(['Sequence'])['Retention time'].mean()
+# df_mean = df.groupby(['Sequence'])['Retention time'].mean()
# feq_aa(seq, plot=False, save=True, f_name='fig/histo_aa_ISA_unique.png')
# dist_long(seq, plot=False, save=True, f_name='fig/histo_length_ISA_unique.png')
-RT_distrib(df_mean, 'fig/histo_RT_ISA_unique.png')
+# RT_distrib(df_mean, 'fig/histo_RT_ISA_unique.png')
+
+
+df_ISA = pd.read_pickle('database/data_ISA_dual_align.pkl')
+seq_list = []
+var_list = []
+num_list = []
+len_list=[]
+for seq, gr in df_ISA.groupby(['Sequence']) :
+ if len(gr)>10 :
+ seq_list.append(seq)
+ var_list.append(np.std(gr['Retention time']))
+ num_list.append(len(gr))
+ len_list.append(len(seq))
+idx = np.argsort(var_list)
+seq_list = np.array(seq_list)[idx][::-1]
+var_list = np.array(var_list)[idx][::-1]
+num_list = np.array(num_list)[idx][::-1]
+len_list = np.array(len_list)[idx][::-1]
+
+
diff --git a/data_viz.py b/data_viz.py
index b00a12b..7ddf745 100644
--- a/data_viz.py
+++ b/data_viz.py
@@ -252,11 +252,9 @@ def select_best_data(df1,df2,threshold):
df = pd.concat([df_group_1, df_group_2], axis=1)
df['mean']=(df['abs err 1']+df['abs err 2'])/2
df_res = df[df['mean']<