File size: 6,863 Bytes
0a0878d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE40914"
# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE40914"
# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE40914.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE40914.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE40914.csv"
json_path = "./output/preprocess/3/Kidney_Chromophobe/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))
# 1. Gene Expression Data Availability
# Based on background info, this is a microarray dataset studying gene expression
# including lncRNAs in RCC, so gene data should be available
is_gene_available = True
# 2. Data Availability and Type Conversion
# Sample characteristics show limited data - disease, tissue, and sample type
# 2.1 Data Availability
# trait_row - Can infer case/control from "tissue" field (row 1)
trait_row = 1
# age_row - Not available
age_row = None
# gender_row - Not available
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert tissue field to binary trait"""
if not isinstance(value, str):
return None
# Extract value after colon
if ':' in value:
value = value.split(':', 1)[1].strip()
# Map tissue type to binary - tumor (1) vs non-tumor (0)
if 'tumor' in value.lower():
return 1
return 0
# No age/gender conversion functions needed since data not available
convert_age = None
convert_gender = None
# 3. Save Metadata
# Initial validation based on gene and trait availability
is_validated = validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=trait_row is not None
)
# 4. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
if trait_row is not None:
clinical_features = geo_select_clinical_features(
clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
age_row=age_row,
convert_age=convert_age,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the extracted features
preview = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)
# Save clinical features
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
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())
# Look at general data statistics
print("\nData shape:", gene_metadata.shape)
# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# 1. In gene expression data, row IDs are simple numbers like "1", "2", etc.
# From annotation preview, 'ID' contains the same identifiers
# 'GENE_SYMBOL' contains the target gene symbols we want to map to
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'
# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
# Convert expression values to numeric type, handling 'Null' values
genetic_data = genetic_data.replace(['Null', 'null', 'NULL'], pd.NA)
numeric_cols = genetic_data.columns
genetic_data[numeric_cols] = genetic_data[numeric_cols].astype(float)
# 3. Convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Print dimensions of output
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
# 1. In gene expression data, row IDs are simple numbers like "1", "2", etc.
# From annotation preview, 'ID' contains the same identifiers
# 'GENE_SYMBOL' contains the target gene symbols we want to map to
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'
# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
# Convert expression values to numeric type
numeric_cols = genetic_data.columns
genetic_data[numeric_cols] = genetic_data[numeric_cols].apply(pd.to_numeric, errors='coerce')
# 3. Convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Print dimensions of output
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Get clinical features
clinical_features = geo_select_clinical_features(
clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
age_row=age_row,
convert_age=convert_age,
gender_row=gender_row,
convert_gender=convert_gender
)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
is_biased = True
linked_data = None
else:
# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples from various tissue types including kidney, lung, stomach and other organs, with both tumor and normal tissues."
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=True,
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) |