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Liu-Hy
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GenoTEX

Tasks:
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Languages:
English
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100B<n<1T
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Tags:
ai4science
agent
llm
genomics
biology
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  1. .gitattributes +20 -0
  2. p1/preprocess/Ovarian_Cancer/gene_data/TCGA.csv +3 -0
  3. p1/preprocess/Pancreatic_Cancer/GSE183795.csv +3 -0
  4. p1/preprocess/Pancreatic_Cancer/gene_data/GSE183795.csv +3 -0
  5. p1/preprocess/Pancreatic_Cancer/gene_data/TCGA.csv +3 -0
  6. p3/preprocess/Stomach_Cancer/gene_data/GSE130823.csv +3 -0
  7. p3/preprocess/Stomach_Cancer/gene_data/GSE146361.csv +3 -0
  8. p3/preprocess/Stomach_Cancer/gene_data/GSE161533.csv +3 -0
  9. p3/preprocess/Stomach_Cancer/gene_data/GSE172197.csv +3 -0
  10. p3/preprocess/Stomach_Cancer/gene_data/GSE183136.csv +3 -0
  11. p3/preprocess/Stomach_Cancer/gene_data/GSE98708.csv +3 -0
  12. p3/preprocess/Stroke/GSE37587.csv +0 -0
  13. p3/preprocess/Stroke/GSE48424.csv +3 -0
  14. p3/preprocess/Stroke/GSE58294.csv +3 -0
  15. p3/preprocess/Stroke/GSE68526.csv +3 -0
  16. p3/preprocess/Stroke/clinical_data/GSE48424.csv +3 -0
  17. p3/preprocess/Stroke/clinical_data/GSE58294.csv +2 -0
  18. p3/preprocess/Stroke/clinical_data/GSE68526.csv +4 -0
  19. p3/preprocess/Stroke/code/GSE125771.py +166 -0
  20. p3/preprocess/Stroke/code/GSE161533.py +174 -0
  21. p3/preprocess/Stroke/code/GSE68526.py +163 -0
  22. p3/preprocess/Stroke/gene_data/GSE125771.csv +0 -0
  23. p3/preprocess/Stroke/gene_data/GSE161533.csv +3 -0
  24. p3/preprocess/Stroke/gene_data/GSE273225.csv +0 -0
  25. p3/preprocess/Stroke/gene_data/GSE37587.csv +0 -0
  26. p3/preprocess/Stroke/gene_data/GSE47727.csv +3 -0
  27. p3/preprocess/Stroke/gene_data/GSE48424.csv +3 -0
  28. p3/preprocess/Stroke/gene_data/GSE58294.csv +3 -0
  29. p3/preprocess/Stroke/gene_data/GSE68526.csv +3 -0
  30. p3/preprocess/Substance_Use_Disorder/GSE103580.csv +3 -0
  31. p3/preprocess/Substance_Use_Disorder/GSE116833.csv +0 -0
  32. p3/preprocess/Substance_Use_Disorder/GSE125681.csv +0 -0
  33. p3/preprocess/Substance_Use_Disorder/GSE138297.csv +3 -0
  34. p3/preprocess/Substance_Use_Disorder/GSE159676.csv +0 -0
  35. p3/preprocess/Substance_Use_Disorder/GSE161986.csv +0 -0
  36. p3/preprocess/Substance_Use_Disorder/GSE161999.csv +0 -0
  37. p3/preprocess/Substance_Use_Disorder/GSE94399.csv +0 -0
  38. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE103580.csv +2 -0
  39. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE116833.csv +4 -0
  40. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE125681.csv +4 -0
  41. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE138297.csv +4 -0
  42. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE148375.csv +4 -0
  43. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE159676.csv +2 -0
  44. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE161986.csv +3 -0
  45. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE161999.csv +4 -0
  46. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE273630.csv +2 -0
  47. p3/preprocess/Substance_Use_Disorder/clinical_data/GSE94399.csv +2 -0
  48. p3/preprocess/Substance_Use_Disorder/code/GSE103580.py +174 -0
  49. p3/preprocess/Substance_Use_Disorder/code/GSE116833.py +189 -0
  50. p3/preprocess/Substance_Use_Disorder/code/GSE125681.py +165 -0
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+ Gender,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0
p3/preprocess/Stroke/code/GSE125771.py ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Stroke"
6
+ cohort = "GSE125771"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Stroke"
10
+ in_cohort_dir = "../DATA/GEO/Stroke/GSE125771"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Stroke/GSE125771.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE125771.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE125771.csv"
16
+ json_path = "./output/preprocess/3/Stroke/cohort_info.json"
17
+
18
+ # Get file paths
19
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Extract background info and clinical data
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file)
23
+
24
+ # Get unique values per clinical feature
25
+ sample_characteristics = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print background info
28
+ print("Dataset Background Information:")
29
+ print(f"{background_info}\n")
30
+
31
+ # Print sample characteristics
32
+ print("Sample Characteristics:")
33
+ for feature, values in sample_characteristics.items():
34
+ print(f"Feature: {feature}")
35
+ print(f"Values: {values}\n")
36
+ # 1. Gene Expression Data Availability
37
+ # Based on Series_summary and methods description, this is a gene expression microarray dataset
38
+ is_gene_available = True
39
+
40
+ # 2.1 Data Availability
41
+ # Trait: Controls not found - all samples are carotid atherosclerotic plaques from patients with >50% stenosis
42
+ trait_row = None
43
+
44
+ # Age: Feature 3 contains age data
45
+ age_row = 3
46
+
47
+ # Gender: Feature 2 contains sex data
48
+ gender_row = 2
49
+
50
+ # 2.2 Data Type Conversion Functions
51
+ def convert_trait(x):
52
+ # Not used since trait_row is None
53
+ return None
54
+
55
+ def convert_age(x):
56
+ # Extract numeric age value after colon
57
+ try:
58
+ age = float(x.split(': ')[1])
59
+ return age
60
+ except:
61
+ return None
62
+
63
+ def convert_gender(x):
64
+ # Convert sex to binary (Female=0, Male=1)
65
+ try:
66
+ sex = x.split(': ')[1].strip()
67
+ if sex == 'Female':
68
+ return 0
69
+ elif sex == 'Male':
70
+ return 1
71
+ return None
72
+ except:
73
+ return None
74
+
75
+ # 3. Save Metadata
76
+ validate_and_save_cohort_info(
77
+ is_final=False,
78
+ cohort=cohort,
79
+ info_path=json_path,
80
+ is_gene_available=is_gene_available,
81
+ is_trait_available=False # trait_row is None
82
+ )
83
+
84
+ # 4. Skip clinical feature extraction since trait_row is None
85
+ # Get file paths
86
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
87
+
88
+ # Extract gene expression data from matrix file
89
+ gene_data = get_genetic_data(matrix_file)
90
+
91
+ # Print first 20 row IDs and shape of data to help debug
92
+ print("Shape of gene expression data:", gene_data.shape)
93
+ print("\nFirst few rows of data:")
94
+ print(gene_data.head())
95
+ print("\nFirst 20 gene/probe identifiers:")
96
+ print(gene_data.index[:20])
97
+
98
+ # Inspect a snippet of raw file to verify identifier format
99
+ import gzip
100
+ with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
101
+ lines = []
102
+ for i, line in enumerate(f):
103
+ if "!series_matrix_table_begin" in line:
104
+ # Get the next 5 lines after the marker
105
+ for _ in range(5):
106
+ lines.append(next(f).strip())
107
+ break
108
+ print("\nFirst few lines after matrix marker in raw file:")
109
+ for line in lines:
110
+ print(line)
111
+ # The identifiers (e.g. TC01000001.hg.1) appear to be probe IDs from a microarray platform
112
+ # rather than standard human gene symbols. They need to be mapped to gene symbols.
113
+ requires_gene_mapping = True
114
+ # Extract gene annotation data
115
+ gene_metadata = get_gene_annotation(soft_file)
116
+
117
+ # Preview annotation data to identify columns for mapping
118
+ print("Gene Annotation Preview (first 5 rows):")
119
+ print(preview_df(gene_metadata))
120
+
121
+ # From the output we can see:
122
+ # - 'ID' column contains probe IDs that match our expression data
123
+ # - 'gene_assignment' column has gene symbol information
124
+ # We'll use these columns for mapping probe IDs to gene symbols
125
+
126
+ # Keep columns needed for mapping
127
+ mapping_cols = ['ID', 'gene_assignment']
128
+ gene_metadata = gene_metadata[mapping_cols]
129
+
130
+ # Print shape of annotation data
131
+ print(f"\nShape of gene annotation data: {gene_metadata.shape}")
132
+ # Extract ID and gene symbol mapping
133
+ mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
134
+
135
+ # Apply mapping to convert probe-level data to gene expression data
136
+ gene_data = apply_gene_mapping(gene_data, mapping_data)
137
+
138
+ # Print shape and preview data to verify mapping worked
139
+ print("Shape of mapped gene expression data:", gene_data.shape)
140
+ print("\nFirst few rows of mapped gene data:")
141
+ print(gene_data.head())
142
+ # 1. Normalize gene symbols and save
143
+ gene_data = normalize_gene_symbols_in_index(gene_data)
144
+ gene_data.to_csv(out_gene_data_file)
145
+
146
+ # Create a minimal DataFrame with gene data and set trait as missing
147
+ linked_data = pd.DataFrame(index=gene_data.columns)
148
+ linked_data[trait] = None
149
+
150
+ # Add gene expression data
151
+ linked_data = pd.concat([linked_data.T, gene_data]).T
152
+
153
+ # Set bias flag for validation
154
+ trait_biased = True # Missing trait data means it cannot be used for trait analysis
155
+
156
+ # Validate and record dataset info
157
+ is_usable = validate_and_save_cohort_info(
158
+ is_final=True,
159
+ cohort=cohort,
160
+ info_path=json_path,
161
+ is_gene_available=True,
162
+ is_trait_available=False,
163
+ is_biased=trait_biased,
164
+ df=linked_data,
165
+ note="Gene expression data available but no stroke phenotype information found in dataset."
166
+ )
p3/preprocess/Stroke/code/GSE161533.py ADDED
@@ -0,0 +1,174 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Stroke"
6
+ cohort = "GSE161533"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Stroke"
10
+ in_cohort_dir = "../DATA/GEO/Stroke/GSE161533"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Stroke/GSE161533.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE161533.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE161533.csv"
16
+ json_path = "./output/preprocess/3/Stroke/cohort_info.json"
17
+
18
+ # Get file paths
19
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Extract background info and clinical data
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file)
23
+
24
+ # Get unique values per clinical feature
25
+ sample_characteristics = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print background info
28
+ print("Dataset Background Information:")
29
+ print(f"{background_info}\n")
30
+
31
+ # Print sample characteristics
32
+ print("Sample Characteristics:")
33
+ for feature, values in sample_characteristics.items():
34
+ print(f"Feature: {feature}")
35
+ print(f"Values: {values}\n")
36
+ # 1. Gene Expression Data Availability
37
+ # Dataset uses Affymetrix Gene Chip U133, which contains gene expression data
38
+ is_gene_available = True
39
+
40
+ # 2. Variable Availability and Keys
41
+ # Trait (Stroke) can be inferred from disease history (feature 6) - look for 'Cerebral infarction'
42
+ trait_row = 6
43
+ # Age is in feature 2
44
+ age_row = 2
45
+ # Gender is in feature 3
46
+ gender_row = 3
47
+
48
+ # 2.2 Data Type Conversion Functions
49
+ def convert_trait(x):
50
+ if pd.isna(x):
51
+ return None
52
+ value = x.split(': ')[1].strip().lower()
53
+ # Convert 'Cerebral infarction' to 1, others to 0
54
+ if 'cerebral infarction' in value:
55
+ return 1
56
+ return 0
57
+
58
+ def convert_age(x):
59
+ if pd.isna(x):
60
+ return None
61
+ try:
62
+ # Extract number after colon
63
+ age = int(x.split(': ')[1])
64
+ return age
65
+ except:
66
+ return None
67
+
68
+ def convert_gender(x):
69
+ if pd.isna(x):
70
+ return None
71
+ value = x.split(': ')[1].strip().lower()
72
+ if value == 'female':
73
+ return 0
74
+ elif value == 'male':
75
+ return 1
76
+ return None
77
+
78
+ # 3. Save Metadata
79
+ is_trait_available = trait_row is not None
80
+ validate_and_save_cohort_info(is_final=False,
81
+ cohort=cohort,
82
+ info_path=json_path,
83
+ is_gene_available=is_gene_available,
84
+ is_trait_available=is_trait_available)
85
+
86
+ # 4. Clinical Feature Extraction
87
+ if trait_row is not None:
88
+ clinical_features = geo_select_clinical_features(
89
+ clinical_df=clinical_data,
90
+ trait=trait,
91
+ trait_row=trait_row,
92
+ convert_trait=convert_trait,
93
+ age_row=age_row,
94
+ convert_age=convert_age,
95
+ gender_row=gender_row,
96
+ convert_gender=convert_gender
97
+ )
98
+
99
+ # Preview the extracted features
100
+ preview = preview_df(clinical_features)
101
+ print("Preview of clinical features:")
102
+ print(preview)
103
+
104
+ # Save to CSV
105
+ clinical_features.to_csv(out_clinical_data_file)
106
+ # Get file paths
107
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
108
+
109
+ # Extract gene expression data from matrix file
110
+ gene_data = get_genetic_data(matrix_file)
111
+
112
+ # Print first 20 row IDs and shape of data to help debug
113
+ print("Shape of gene expression data:", gene_data.shape)
114
+ print("\nFirst few rows of data:")
115
+ print(gene_data.head())
116
+ print("\nFirst 20 gene/probe identifiers:")
117
+ print(gene_data.index[:20])
118
+
119
+ # Inspect a snippet of raw file to verify identifier format
120
+ import gzip
121
+ with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
122
+ lines = []
123
+ for i, line in enumerate(f):
124
+ if "!series_matrix_table_begin" in line:
125
+ # Get the next 5 lines after the marker
126
+ for _ in range(5):
127
+ lines.append(next(f).strip())
128
+ break
129
+ print("\nFirst few lines after matrix marker in raw file:")
130
+ for line in lines:
131
+ print(line)
132
+ requires_gene_mapping = True
133
+ # Extract gene annotation data
134
+ gene_metadata = get_gene_annotation(soft_file)
135
+
136
+ # Preview annotation data
137
+ print("Gene annotation preview:")
138
+ print(preview_df(gene_metadata))
139
+ # Extract mapping between probe IDs and gene symbols
140
+ mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
141
+
142
+ # Use previously loaded gene expression data
143
+ gene_data = apply_gene_mapping(gene_data, mapping_data)
144
+
145
+ # Save preprocessed gene expression data
146
+ gene_data.to_csv(out_gene_data_file)
147
+ # 1. Normalize gene symbols
148
+ gene_data = normalize_gene_symbols_in_index(gene_data)
149
+ gene_data.to_csv(out_gene_data_file)
150
+
151
+ # 2. Link clinical and genetic data
152
+ linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
153
+
154
+ # 3. Handle missing values
155
+ linked_data = handle_missing_values(linked_data, trait)
156
+
157
+ # 4. Check for bias
158
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
159
+
160
+ # 5. Validate and save cohort info
161
+ is_usable = validate_and_save_cohort_info(
162
+ is_final=True,
163
+ cohort=cohort,
164
+ info_path=json_path,
165
+ is_gene_available=True,
166
+ is_trait_available=True,
167
+ is_biased=trait_biased,
168
+ df=linked_data,
169
+ note="Study examining transcriptome profiles in lung transplantation."
170
+ )
171
+
172
+ # 6. Save if usable
173
+ if is_usable:
174
+ linked_data.to_csv(out_data_file)
p3/preprocess/Stroke/code/GSE68526.py ADDED
@@ -0,0 +1,163 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Stroke"
6
+ cohort = "GSE68526"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Stroke"
10
+ in_cohort_dir = "../DATA/GEO/Stroke/GSE68526"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Stroke/GSE68526.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE68526.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE68526.csv"
16
+ json_path = "./output/preprocess/3/Stroke/cohort_info.json"
17
+
18
+ # Get file paths
19
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Extract background info and clinical data
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file)
23
+
24
+ # Get unique values per clinical feature
25
+ sample_characteristics = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print background info
28
+ print("Dataset Background Information:")
29
+ print(f"{background_info}\n")
30
+
31
+ # Print sample characteristics
32
+ print("Sample Characteristics:")
33
+ for feature, values in sample_characteristics.items():
34
+ print(f"Feature: {feature}")
35
+ print(f"Values: {values}\n")
36
+ # 1. Gene Expression Data Availability
37
+ # Based on series title and overall design, this is RNA transcriptome data from blood samples
38
+ is_gene_available = True
39
+
40
+ # 2.1 Data Availability
41
+ # trait (stroke) info is in feature 5 'diabcvdcastr' which includes stroke status
42
+ trait_row = 5
43
+ age_row = 0
44
+ gender_row = 1 # female: 0/1 in feature 1
45
+
46
+ # 2.2 Data Type Conversion Functions
47
+ def convert_trait(x):
48
+ # diabcvdcastr includes multiple conditions, but since we're looking for stroke specifically,
49
+ # any positive value indicates stroke presence
50
+ if not x or 'missing' in x.lower():
51
+ return None
52
+ try:
53
+ value = int(x.split(': ')[1])
54
+ return value
55
+ except:
56
+ return None
57
+
58
+ def convert_age(x):
59
+ if not x or 'missing' in x.lower():
60
+ return None
61
+ try:
62
+ # Extract numbers after colon
63
+ age = int(x.split(': ')[1])
64
+ return age
65
+ except:
66
+ return None
67
+
68
+ def convert_gender(x):
69
+ if not x or 'missing' in x.lower():
70
+ return None
71
+ try:
72
+ # female: 0/1 needs to be flipped since we want male=1
73
+ female = int(x.split(': ')[1])
74
+ return 1 - female # converts female:1 to 0 and female:0 to 1
75
+ except:
76
+ return None
77
+
78
+ # 3. Save Metadata
79
+ is_trait_available = trait_row is not None
80
+ validate_and_save_cohort_info(
81
+ is_final=False,
82
+ cohort=cohort,
83
+ info_path=json_path,
84
+ is_gene_available=is_gene_available,
85
+ is_trait_available=is_trait_available
86
+ )
87
+
88
+ # 4. Clinical Feature Extraction
89
+ if trait_row is not None:
90
+ clinical_features = geo_select_clinical_features(
91
+ clinical_df=clinical_data,
92
+ trait=trait,
93
+ trait_row=trait_row,
94
+ convert_trait=convert_trait,
95
+ age_row=age_row,
96
+ convert_age=convert_age,
97
+ gender_row=gender_row,
98
+ convert_gender=convert_gender
99
+ )
100
+
101
+ # Preview the extracted features
102
+ preview = preview_df(clinical_features)
103
+
104
+ # Save to CSV
105
+ clinical_features.to_csv(out_clinical_data_file)
106
+ # Get file paths
107
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
108
+
109
+ # Extract gene expression data from matrix file
110
+ gene_data = get_genetic_data(matrix_file)
111
+
112
+ # Print first 20 row IDs and shape of data to help debug
113
+ print("Shape of gene expression data:", gene_data.shape)
114
+ print("\nFirst few rows of data:")
115
+ print(gene_data.head())
116
+ print("\nFirst 20 gene/probe identifiers:")
117
+ print(gene_data.index[:20])
118
+
119
+ # Inspect a snippet of raw file to verify identifier format
120
+ import gzip
121
+ with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
122
+ lines = []
123
+ for i, line in enumerate(f):
124
+ if "!series_matrix_table_begin" in line:
125
+ # Get the next 5 lines after the marker
126
+ for _ in range(5):
127
+ lines.append(next(f).strip())
128
+ break
129
+ print("\nFirst few lines after matrix marker in raw file:")
130
+ for line in lines:
131
+ print(line)
132
+ # Based on the raw data shown, we can see that the IDs like A1BG, A1CF are human gene symbols
133
+ # These are standard HGNC gene symbols, so no mapping is needed
134
+ requires_gene_mapping = False
135
+ # 1. Normalize gene symbols
136
+ gene_data = normalize_gene_symbols_in_index(gene_data)
137
+ gene_data.to_csv(out_gene_data_file)
138
+
139
+ # 2. Load clinical data and link with genetic data
140
+ clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
141
+ linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
142
+
143
+ # 3. Handle missing values
144
+ linked_data = handle_missing_values(linked_data, trait)
145
+
146
+ # 4. Evaluate bias
147
+ is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
148
+
149
+ # 5. Validate and save cohort info
150
+ is_usable = validate_and_save_cohort_info(
151
+ is_final=True,
152
+ cohort=cohort,
153
+ info_path=json_path,
154
+ is_gene_available=True,
155
+ is_trait_available=True,
156
+ is_biased=is_biased,
157
+ df=linked_data,
158
+ note="Study examining transcriptome profiles from peripheral blood of older adults, including some with stroke history."
159
+ )
160
+
161
+ # 6. Save linked data if usable
162
+ if is_usable:
163
+ linked_data.to_csv(out_data_file)
p3/preprocess/Stroke/gene_data/GSE125771.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Stroke/gene_data/GSE161533.csv ADDED
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p3/preprocess/Stroke/gene_data/GSE273225.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Stroke/gene_data/GSE37587.csv ADDED
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p3/preprocess/Stroke/gene_data/GSE47727.csv ADDED
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p3/preprocess/Stroke/gene_data/GSE58294.csv ADDED
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p3/preprocess/Substance_Use_Disorder/GSE116833.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Substance_Use_Disorder/GSE125681.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Substance_Use_Disorder/GSE138297.csv ADDED
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p3/preprocess/Substance_Use_Disorder/GSE159676.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Substance_Use_Disorder/GSE161986.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Substance_Use_Disorder/GSE161999.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Substance_Use_Disorder/GSE94399.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Substance_Use_Disorder/clinical_data/GSE103580.csv ADDED
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p3/preprocess/Substance_Use_Disorder/clinical_data/GSE116833.csv ADDED
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p3/preprocess/Substance_Use_Disorder/clinical_data/GSE138297.csv ADDED
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p3/preprocess/Substance_Use_Disorder/clinical_data/GSE159676.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ ,GSM4837490,GSM4837491,GSM4837492,GSM4837493,GSM4837494,GSM4837495,GSM4837496,GSM4837497,GSM4837498,GSM4837499,GSM4837500,GSM4837501,GSM4837502,GSM4837503,GSM4837504,GSM4837505,GSM4837506,GSM4837507,GSM4837508,GSM4837509,GSM4837510,GSM4837511,GSM4837512,GSM4837513,GSM4837514,GSM4837515,GSM4837516,GSM4837517,GSM4837518,GSM4837519,GSM4837520,GSM4837521,GSM4837522
2
+ Substance_Use_Disorder,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
p3/preprocess/Substance_Use_Disorder/clinical_data/GSE161986.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ ,GSM4929029,GSM4929030,GSM4929031,GSM4929032,GSM4929033,GSM4929034,GSM4929035,GSM4929036,GSM4929037,GSM4929038,GSM4929039,GSM4929040,GSM4929041,GSM4929042,GSM4929043,GSM4929044,GSM4929045,GSM4929046,GSM4929047,GSM4929048,GSM4929049,GSM4929050,GSM4929051,GSM4929052,GSM4929053,GSM4929054,GSM4929055,GSM4929056,GSM4929057,GSM4929058,GSM4929059,GSM4929060,GSM4929061,GSM4929062,GSM4929063
2
+ Substance_Use_Disorder,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0
3
+ Age,61.0,44.0,62.0,56.0,63.0,42.0,46.0,56.0,52.0,43.0,59.0,56.0,54.0,46.0,39.0,73.0,56.0,50.0,63.0,50.0,50.0,51.0,64.0,55.0,55.0,47.0,50.0,55.0,53.0,82.0,64.0,73.0,73.0,57.0,59.0
p3/preprocess/Substance_Use_Disorder/clinical_data/GSE161999.csv ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ ,GSM4929029,GSM4929030,GSM4929031,GSM4929032,GSM4929033,GSM4929034,GSM4929035,GSM4929036,GSM4929037,GSM4929038,GSM4929039,GSM4929040,GSM4929041,GSM4929042,GSM4929043,GSM4929044,GSM4929045,GSM4929046,GSM4929047,GSM4929048,GSM4929049,GSM4929050,GSM4929051,GSM4929052,GSM4929053,GSM4929054,GSM4929055,GSM4929056,GSM4929057,GSM4929058,GSM4929059,GSM4929060,GSM4929061,GSM4929062,GSM4929063
2
+ Substance_Use_Disorder,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0
3
+ Age,61.0,44.0,62.0,56.0,63.0,42.0,46.0,56.0,52.0,43.0,59.0,56.0,54.0,46.0,39.0,73.0,56.0,50.0,63.0,50.0,50.0,51.0,64.0,55.0,55.0,47.0,50.0,55.0,53.0,82.0,64.0,73.0,73.0,57.0,59.0
4
+ Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
p3/preprocess/Substance_Use_Disorder/clinical_data/GSE273630.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ ,GSM8434091,GSM8434092,GSM8434093,GSM8434094,GSM8434095,GSM8434096,GSM8434097,GSM8434098,GSM8434099,GSM8434100,GSM8434101,GSM8434102,GSM8434103,GSM8434104,GSM8434105,GSM8434106,GSM8434107,GSM8434108,GSM8434109,GSM8434110,GSM8434111,GSM8434112,GSM8434113,GSM8434114,GSM8434115,GSM8434116,GSM8434117,GSM8434118,GSM8434119,GSM8434120,GSM8434121,GSM8434122,GSM8434123,GSM8434124,GSM8434125,GSM8434126,GSM8434127,GSM8434128,GSM8434129,GSM8434130,GSM8434131,GSM8434132,GSM8434133,GSM8434134,GSM8434135,GSM8434136,GSM8434137,GSM8434138,GSM8434139,GSM8434140,GSM8434141,GSM8434142,GSM8434143,GSM8434144,GSM8434145,GSM8434146,GSM8434147,GSM8434148,GSM8434149,GSM8434150,GSM8434151,GSM8434152,GSM8434153,GSM8434154,GSM8434155,GSM8434156,GSM8434157,GSM8434158,GSM8434159,GSM8434160,GSM8434161,GSM8434162,GSM8434163,GSM8434164,GSM8434165,GSM8434166,GSM8434167,GSM8434168,GSM8434169,GSM8434170,GSM8434171,GSM8434172,GSM8434173,GSM8434174,GSM8434175,GSM8434176,GSM8434177,GSM8434178,GSM8434179,GSM8434180,GSM8434181,GSM8434182,GSM8434183,GSM8434184,GSM8434185,GSM8434186,GSM8434187,GSM8434188,GSM8434189
2
+ Substance_Use_Disorder,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
p3/preprocess/Substance_Use_Disorder/clinical_data/GSE94399.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ ,GSM2474751,GSM2474752,GSM2474753,GSM2474754,GSM2474755,GSM2474756,GSM2474757,GSM2474758,GSM2474759,GSM2474760,GSM2474761,GSM2474762,GSM2474763,GSM2474764,GSM2474765,GSM2474766,GSM2474767,GSM2474768,GSM2474769,GSM2474770,GSM2474771,GSM2474772,GSM2474773,GSM2474774,GSM2474775,GSM2474776,GSM2474777,GSM2474778,GSM2474779,GSM2474780,GSM2474781,GSM2474782,GSM2474783,GSM2474784,GSM2474785,GSM2474786,GSM2474787,GSM2474788
2
+ Substance_Use_Disorder,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0
p3/preprocess/Substance_Use_Disorder/code/GSE103580.py ADDED
@@ -0,0 +1,174 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Substance_Use_Disorder"
6
+ cohort = "GSE103580"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE103580"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Substance_Use_Disorder/GSE103580.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Substance_Use_Disorder/gene_data/GSE103580.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Substance_Use_Disorder/clinical_data/GSE103580.csv"
16
+ json_path = "./output/preprocess/3/Substance_Use_Disorder/cohort_info.json"
17
+
18
+ # Get file paths
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+ print("Background Information:")
24
+ print(background_info)
25
+ print("\nSample Characteristics:")
26
+
27
+ # Get dictionary of unique values per row
28
+ unique_values_dict = get_unique_values_by_row(clinical_data)
29
+ for row, values in unique_values_dict.items():
30
+ print(f"\n{row}:")
31
+ print(values)
32
+ # 1. Gene Expression Data Availability
33
+ # Based on the background information, this is a transcriptome profiling study
34
+ # examining gene expression in liver tissues using NanoString platform
35
+ is_gene_available = True
36
+
37
+ # 2. Variable Analysis
38
+ # 2.1 Data Availability
39
+
40
+ # Trait (alcoholic liver disease stage) is available in row 0
41
+ trait_row = 0
42
+
43
+ # Age and gender data not available in sample characteristics
44
+ age_row = None
45
+ gender_row = None
46
+
47
+ # 2.2 Data Type Conversion Functions
48
+ def convert_trait(value: str) -> int:
49
+ """Convert alcoholic liver disease stage to binary:
50
+ 0 for mild cases (steatosis, mild hepatitis)
51
+ 1 for severe cases (cirrhosis)"""
52
+ if not value or ':' not in value:
53
+ return None
54
+ value = value.split(':')[1].strip().lower()
55
+ if 'cirrhosis' in value:
56
+ return 1
57
+ elif 'steatosis' in value or 'mild' in value:
58
+ return 0
59
+ return None
60
+
61
+ # No age conversion needed
62
+ convert_age = None
63
+
64
+ # No gender conversion needed
65
+ convert_gender = None
66
+
67
+ # 3. Save Metadata
68
+ is_trait_available = trait_row is not None
69
+ validate_and_save_cohort_info(is_final=False,
70
+ cohort=cohort,
71
+ info_path=json_path,
72
+ is_gene_available=is_gene_available,
73
+ is_trait_available=is_trait_available)
74
+
75
+ # 4. Clinical Feature Extraction
76
+ if trait_row is not None:
77
+ selected_clinical_df = geo_select_clinical_features(
78
+ clinical_df=clinical_data,
79
+ trait=trait,
80
+ trait_row=trait_row,
81
+ convert_trait=convert_trait,
82
+ age_row=age_row,
83
+ convert_age=convert_age,
84
+ gender_row=gender_row,
85
+ convert_gender=convert_gender
86
+ )
87
+
88
+ # Preview the extracted features
89
+ preview = preview_df(selected_clinical_df)
90
+ print("Preview of extracted clinical features:")
91
+ print(preview)
92
+
93
+ # Save to CSV
94
+ os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
95
+ selected_clinical_df.to_csv(out_clinical_data_file)
96
+ # Get gene expression data from matrix file
97
+ genetic_data = get_genetic_data(matrix_file_path)
98
+
99
+ # Examine data structure
100
+ print("Data structure and head:")
101
+ print(genetic_data.head())
102
+
103
+ print("\nShape:", genetic_data.shape)
104
+
105
+ print("\nFirst 20 row IDs (gene/probe identifiers):")
106
+ print(list(genetic_data.index)[:20])
107
+
108
+ # Get a few column names to verify sample IDs
109
+ print("\nFirst 5 column names:")
110
+ print(list(genetic_data.columns)[:5])
111
+ # The identifiers like "11715100_at" are probe IDs from Affymetrix microarrays
112
+ # They need to be mapped to human gene symbols for analysis
113
+ requires_gene_mapping = True
114
+ # Extract gene annotation from SOFT file
115
+ gene_annotation = get_gene_annotation(soft_file_path)
116
+
117
+ # Preview annotation structure
118
+ preview = preview_df(gene_annotation)
119
+ print("Gene annotation preview:")
120
+ print(preview)
121
+ # Get gene mapping data by extracting ID and Gene Symbol columns
122
+ # 'ID' contains probe IDs matching gene expression data, 'Gene Symbol' contains gene symbols
123
+ gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
124
+
125
+ # Map probe data to gene expression data
126
+ gene_data = apply_gene_mapping(expression_df=genetic_data, mapping_df=gene_mapping)
127
+
128
+ # Preview the mapped gene data
129
+ print("Gene data shape:", gene_data.shape)
130
+ preview = preview_df(gene_data)
131
+ print("\nGene data preview:")
132
+ print(preview)
133
+
134
+ # Save gene data
135
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
136
+ gene_data.to_csv(out_gene_data_file)
137
+ # 1. Normalize gene symbols in gene expression data
138
+ gene_data = normalize_gene_symbols_in_index(gene_data)
139
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
140
+ gene_data.to_csv(out_gene_data_file)
141
+ print("\nGene data shape (normalized gene-level):", gene_data.shape)
142
+
143
+ # Load clinical data previously processed
144
+ selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
145
+ print("\nClinical data shape:", selected_clinical_df.shape)
146
+
147
+ # 2. Link clinical and genetic data using normalized gene-level data
148
+ linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
149
+ print("\nLinked data shape:", linked_data.shape)
150
+
151
+ # 3. Handle missing values systematically
152
+ if trait in linked_data.columns:
153
+ linked_data = handle_missing_values(linked_data, trait)
154
+
155
+ # 4. Check for bias in trait and demographic features
156
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
157
+
158
+ # 5. Final validation and information saving
159
+ note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
160
+ is_usable = validate_and_save_cohort_info(
161
+ is_final=True,
162
+ cohort=cohort,
163
+ info_path=json_path,
164
+ is_gene_available=True,
165
+ is_trait_available=True,
166
+ is_biased=trait_biased,
167
+ df=linked_data,
168
+ note=note
169
+ )
170
+
171
+ # 6. Save linked data only if usable and not biased
172
+ if is_usable and not trait_biased:
173
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
174
+ linked_data.to_csv(out_data_file)
p3/preprocess/Substance_Use_Disorder/code/GSE116833.py ADDED
@@ -0,0 +1,189 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Substance_Use_Disorder"
6
+ cohort = "GSE116833"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE116833"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Substance_Use_Disorder/GSE116833.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Substance_Use_Disorder/gene_data/GSE116833.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Substance_Use_Disorder/clinical_data/GSE116833.csv"
16
+ json_path = "./output/preprocess/3/Substance_Use_Disorder/cohort_info.json"
17
+
18
+ # Get file paths
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+ print("Background Information:")
24
+ print(background_info)
25
+ print("\nSample Characteristics:")
26
+
27
+ # Get dictionary of unique values per row
28
+ unique_values_dict = get_unique_values_by_row(clinical_data)
29
+ for row, values in unique_values_dict.items():
30
+ print(f"\n{row}:")
31
+ print(values)
32
+ # 1. Gene Expression Data Availability
33
+ is_gene_available = True # The background info shows it used HumanHT-12 v4.0 Gene Expression BeadChip
34
+
35
+ # 2.1 Data Availability
36
+ # trait info can be found in key 0 showing anhedonia levels
37
+ trait_row = 0
38
+ # age info found in key 2
39
+ age_row = 2
40
+ # gender info found in key 1
41
+ gender_row = 1
42
+
43
+ # 2.2 Data Type Conversion
44
+ def convert_trait(x):
45
+ """Convert anhedonia level to binary: high=1, low=0"""
46
+ if not isinstance(x, str):
47
+ return None
48
+ val = x.split(': ')[1].lower() if ': ' in x else x.lower()
49
+ if 'high' in val:
50
+ return 1
51
+ elif 'low' in val:
52
+ return 0
53
+ return None
54
+
55
+ def convert_age(x):
56
+ """Convert age to continuous numeric value"""
57
+ if not isinstance(x, str):
58
+ return None
59
+ try:
60
+ return float(x.split(': ')[1])
61
+ except:
62
+ return None
63
+
64
+ def convert_gender(x):
65
+ """Convert gender to binary: female=0, male=1"""
66
+ if not isinstance(x, str):
67
+ return None
68
+ val = x.split(': ')[1].lower() if ': ' in x else x.lower()
69
+ if 'female' in val:
70
+ return 0
71
+ elif 'male' in val:
72
+ return 1
73
+ return None
74
+
75
+ # 3. Save Metadata
76
+ # Initial filtering based on trait and gene data availability
77
+ is_trait_available = trait_row is not None
78
+ validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
79
+ is_gene_available=is_gene_available,
80
+ is_trait_available=is_trait_available)
81
+
82
+ # 4. Clinical Feature Extraction
83
+ # Since trait_row is not None, we extract clinical features
84
+ clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
85
+ age_row, convert_age,
86
+ gender_row, convert_gender)
87
+
88
+ # Preview the extracted features
89
+ preview_result = preview_df(clinical_df)
90
+
91
+ # Save clinical features to CSV
92
+ clinical_df.to_csv(out_clinical_data_file)
93
+ # Get gene expression data from matrix file
94
+ genetic_data = get_genetic_data(matrix_file_path)
95
+
96
+ # Examine data structure
97
+ print("Data structure and head:")
98
+ print(genetic_data.head())
99
+
100
+ print("\nShape:", genetic_data.shape)
101
+
102
+ print("\nFirst 20 row IDs (gene/probe identifiers):")
103
+ print(list(genetic_data.index)[:20])
104
+
105
+ # Get a few column names to verify sample IDs
106
+ print("\nFirst 5 column names:")
107
+ print(list(genetic_data.columns)[:5])
108
+ # ILMN_ prefix indicates these are Illumina array probe IDs, not gene symbols
109
+ # These need to be mapped to human gene symbols for analysis
110
+ requires_gene_mapping = True
111
+ # First inspect where platform data begins
112
+ print("Looking for platform data section:")
113
+ with gzip.open(soft_file_path, 'rt') as f:
114
+ for i, line in enumerate(f):
115
+ if "!Platform_table_begin" in line or "^PLATFORM" in line:
116
+ print(f"Found platform marker at line {i}:")
117
+ print(line.strip())
118
+ # Print next few lines to see format
119
+ for _ in range(5):
120
+ print(next(f).strip())
121
+ break
122
+
123
+ print("\nExtracting gene annotations...")
124
+ # Use library function to extract annotations
125
+ gene_annotation = get_gene_annotation(soft_file_path)
126
+
127
+ # Extract ID and Symbol columns which we need for mapping
128
+ mapping_df = gene_annotation[['ID', 'Symbol']].copy()
129
+
130
+ print("\nMapping data preview:")
131
+ preview = preview_df(mapping_df)
132
+ print(preview)
133
+
134
+ print("\nShape of mapping data:", mapping_df.shape)
135
+ print("Number of non-null Symbols:", mapping_df['Symbol'].count())
136
+ # Prepare mapping dataframe with correct column names
137
+ mapping_df = mapping_df.rename(columns={'Symbol': 'Gene'})
138
+
139
+ # Map probe IDs to gene symbols using predefined function
140
+ gene_data = apply_gene_mapping(genetic_data, mapping_df)
141
+
142
+ # Normalize gene symbols to their official HGNC names and combine rows with same gene symbol
143
+ gene_data = normalize_gene_symbols_in_index(gene_data)
144
+
145
+ # Save gene expression data
146
+ gene_data.to_csv(out_gene_data_file)
147
+
148
+ # Print shape and preview after mapping
149
+ print("Shape after mapping:", gene_data.shape)
150
+ print("\nPreview of first few rows after mapping:")
151
+ print(preview_df(gene_data))
152
+ # 1. Normalize gene symbols in gene expression data
153
+ gene_data = normalize_gene_symbols_in_index(gene_data)
154
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
155
+ gene_data.to_csv(out_gene_data_file)
156
+ print("\nGene data shape (normalized gene-level):", gene_data.shape)
157
+
158
+ # Load clinical data previously processed
159
+ selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
160
+ print("\nClinical data shape:", selected_clinical_df.shape)
161
+
162
+ # 2. Link clinical and genetic data using normalized gene-level data
163
+ linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
164
+ print("\nLinked data shape:", linked_data.shape)
165
+
166
+ # 3. Handle missing values systematically
167
+ if trait in linked_data.columns:
168
+ linked_data = handle_missing_values(linked_data, trait)
169
+
170
+ # 4. Check for bias in trait and demographic features
171
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
172
+
173
+ # 5. Final validation and information saving
174
+ note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
175
+ is_usable = validate_and_save_cohort_info(
176
+ is_final=True,
177
+ cohort=cohort,
178
+ info_path=json_path,
179
+ is_gene_available=True,
180
+ is_trait_available=True,
181
+ is_biased=trait_biased,
182
+ df=linked_data,
183
+ note=note
184
+ )
185
+
186
+ # 6. Save linked data only if usable and not biased
187
+ if is_usable and not trait_biased:
188
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
189
+ linked_data.to_csv(out_data_file)
p3/preprocess/Substance_Use_Disorder/code/GSE125681.py ADDED
@@ -0,0 +1,165 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Substance_Use_Disorder"
6
+ cohort = "GSE125681"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE125681"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Substance_Use_Disorder/GSE125681.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Substance_Use_Disorder/gene_data/GSE125681.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Substance_Use_Disorder/clinical_data/GSE125681.csv"
16
+ json_path = "./output/preprocess/3/Substance_Use_Disorder/cohort_info.json"
17
+
18
+ # Get file paths
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+ print("Background Information:")
24
+ print(background_info)
25
+ print("\nSample Characteristics:")
26
+
27
+ # Get dictionary of unique values per row
28
+ unique_values_dict = get_unique_values_by_row(clinical_data)
29
+ for row, values in unique_values_dict.items():
30
+ print(f"\n{row}:")
31
+ print(values)
32
+ # 1. Gene Expression Data Availability
33
+ is_gene_available = True # Based on Series summary mentioning "gene expression profile"
34
+
35
+ # 2.1 Data Keys for Clinical Features
36
+ trait_row = 3 # 'sud' contains trait information
37
+ age_row = 0 # Age information available
38
+ gender_row = 1 # Gender information available
39
+
40
+ # 2.2 Data Type Conversion Functions
41
+ def convert_trait(x: str) -> Optional[int]:
42
+ if not isinstance(x, str):
43
+ return None
44
+ val = x.split(': ')[-1].strip().upper()
45
+ if val == 'SUD':
46
+ return 1
47
+ elif val == 'WITHOUT SUD':
48
+ return 0
49
+ return None
50
+
51
+ def convert_age(x: str) -> Optional[float]:
52
+ if not isinstance(x, str):
53
+ return None
54
+ try:
55
+ return float(x.split(': ')[-1])
56
+ except:
57
+ return None
58
+
59
+ def convert_gender(x: str) -> Optional[int]:
60
+ if not isinstance(x, str):
61
+ return None
62
+ val = x.split(': ')[-1].strip().upper()
63
+ if val == 'MALE':
64
+ return 1
65
+ elif val == 'FEMALE':
66
+ return 0
67
+ return None
68
+
69
+ # 3. Save Metadata
70
+ validate_and_save_cohort_info(is_final=False,
71
+ cohort=cohort,
72
+ info_path=json_path,
73
+ is_gene_available=is_gene_available,
74
+ is_trait_available=trait_row is not None)
75
+
76
+ # 4. Clinical Feature Extraction
77
+ if trait_row is not None:
78
+ selected_clinical_df = geo_select_clinical_features(
79
+ clinical_df=clinical_data,
80
+ trait=trait,
81
+ trait_row=trait_row,
82
+ convert_trait=convert_trait,
83
+ age_row=age_row,
84
+ convert_age=convert_age,
85
+ gender_row=gender_row,
86
+ convert_gender=convert_gender
87
+ )
88
+
89
+ print("Preview of selected clinical features:")
90
+ print(preview_df(selected_clinical_df))
91
+
92
+ os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
93
+ selected_clinical_df.to_csv(out_clinical_data_file)
94
+ # Get gene expression data from matrix file
95
+ genetic_data = get_genetic_data(matrix_file_path)
96
+
97
+ # Examine data structure
98
+ print("Data structure and head:")
99
+ print(genetic_data.head())
100
+
101
+ print("\nShape:", genetic_data.shape)
102
+
103
+ print("\nFirst 20 row IDs (gene/probe identifiers):")
104
+ print(list(genetic_data.index)[:20])
105
+
106
+ # Get a few column names to verify sample IDs
107
+ print("\nFirst 5 column names:")
108
+ print(list(genetic_data.columns)[:5])
109
+ requires_gene_mapping = True
110
+ # Extract gene annotation from SOFT file
111
+ gene_annotation = get_gene_annotation(soft_file_path)
112
+
113
+ # Preview annotation structure
114
+ preview = preview_df(gene_annotation)
115
+ print("Gene annotation preview:")
116
+ print(preview)
117
+ # 1. The gene expression data uses IDs that match with 'ID' column in annotation
118
+ # The gene symbols are in the 'Symbol' column
119
+
120
+ # 2. Get gene mapping dataframe with relevant columns
121
+ gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
122
+
123
+ # 3. Apply gene mapping to convert probe-level data to gene expression data
124
+ gene_data = apply_gene_mapping(genetic_data, gene_mapping)
125
+ print("\nGene data shape after mapping:", gene_data.shape)
126
+ print("\nPreview of first few genes and their expression values:")
127
+ print(preview_df(gene_data))
128
+ # 1. Normalize gene symbols in gene expression data
129
+ gene_data = normalize_gene_symbols_in_index(gene_data)
130
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
131
+ gene_data.to_csv(out_gene_data_file)
132
+ print("\nGene data shape (normalized gene-level):", gene_data.shape)
133
+
134
+ # Load clinical data previously processed
135
+ selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
136
+ print("\nClinical data shape:", selected_clinical_df.shape)
137
+
138
+ # 2. Link clinical and genetic data using normalized gene-level data
139
+ linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
140
+ print("\nLinked data shape:", linked_data.shape)
141
+
142
+ # 3. Handle missing values systematically
143
+ if trait in linked_data.columns:
144
+ linked_data = handle_missing_values(linked_data, trait)
145
+
146
+ # 4. Check for bias in trait and demographic features
147
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
148
+
149
+ # 5. Final validation and information saving
150
+ note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
151
+ is_usable = validate_and_save_cohort_info(
152
+ is_final=True,
153
+ cohort=cohort,
154
+ info_path=json_path,
155
+ is_gene_available=True,
156
+ is_trait_available=True,
157
+ is_biased=trait_biased,
158
+ df=linked_data,
159
+ note=note
160
+ )
161
+
162
+ # 6. Save linked data only if usable and not biased
163
+ if is_usable and not trait_biased:
164
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
165
+ linked_data.to_csv(out_data_file)