Hugging Face's logo

Datasets:
Liu-Hy
/
GenoTEX

Tasks:
Text Generation
Table Question Answering
Tabular Regression
Languages:
English
Size:
100B<n<1T
ArXiv:
Tags:
ai4science
agent
llm
genomics
biology
License:
Dataset card Files Community
GenoTEX
File size: 6,332 Bytes 
ee5a411
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
 
# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Heart_rate"
cohort = "GSE117070"

# Input paths
in_trait_dir = "../DATA/GEO/Heart_rate"
in_cohort_dir = "../DATA/GEO/Heart_rate/GSE117070"

# Output paths
out_data_file = "./output/preprocess/3/Heart_rate/GSE117070.csv"
out_gene_data_file = "./output/preprocess/3/Heart_rate/gene_data/GSE117070.csv"
out_clinical_data_file = "./output/preprocess/3/Heart_rate/clinical_data/GSE117070.csv"
json_path = "./output/preprocess/3/Heart_rate/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# Get gene expression data from matrix file
gene_data = get_genetic_data(matrix_file_path)
is_gene_available = len(gene_data.columns) > 1
is_trait_available = False  # Since we found no heart rate measurements in step 1

# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data together
# Since trait data is not available, we have no clinical data to link with
clinical_data = pd.DataFrame()
if not clinical_data.empty:
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
else:
    linked_data = pd.DataFrame()

# 3. Handle missing values if we have linked data
if not linked_data.empty and trait in linked_data.columns:
    linked_data = handle_missing_values(linked_data, trait)

# 4. Judge whether features are biased and remove biased demographic features  
is_biased = True  # No trait data means it's biased by default
if not linked_data.empty and trait in linked_data.columns:
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "This dataset lacks heart rate measurements. The study focused on gene expression changes in PBMCs before and after physical activity, but did not include heart rate as a measured variable."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# 6. Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print information about the data structure
print("First few rows of the genetic data:")
print(genetic_data.head())
print("\nShape of genetic data:", genetic_data.shape)
print("\nColumn names:", genetic_data.columns.tolist())
# Review the gene identifiers in the genetic data
# The IDs shown (e.g. 1007_s_at, 1053_at) appear to be probe IDs from an Affymetrix microarray platform
# These are not human gene symbols and will need to be mapped to gene symbols

requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Get gene mapping from annotation data
# 'ID' column matches probe IDs in expression data, 'Gene Symbol' contains gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')

# Apply gene mapping to convert probe expression to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Print information about the mapping results
print("Shape of probe-level data:", genetic_data.shape)
print("Shape of gene-level data:", gene_data.shape)
print("\nPreview of mapped gene expression data:")
print(gene_data.head())
# Get probe-level data from previous step
genetic_data = get_genetic_data(matrix_file_path)

# Get mapping data from previous step 
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')

# First apply gene mapping to convert probe data to gene data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Then normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Check data availability
is_gene_available = len(gene_data.columns) > 1
is_trait_available = False  # Since we found no heart rate measurements in step 1

# Link clinical and genetic data together
# Since trait data is not available, we have no clinical data to link with
clinical_data = pd.DataFrame()
if not clinical_data.empty:
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
else:
    linked_data = pd.DataFrame()

# Handle missing values if we have linked data
if not linked_data.empty and trait in linked_data.columns:
    linked_data = handle_missing_values(linked_data, trait)

# Judge whether features are biased and remove biased demographic features  
is_biased = True  # No trait data means it's biased by default
if not linked_data.empty and trait in linked_data.columns:
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and save metadata
note = "This dataset lacks heart rate measurements. The study focused on gene expression changes before and after physical activity, but did not include heart rate as a measured variable."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)