File size: 3,479 Bytes
dd19378 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE99612"
# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE99612"
# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE99612.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE99612.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE99612.csv"
json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)
# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")
# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
print(f"Feature: {feature}")
print(f"Values: {values}\n")
# 1. Gene Expression Data - Not a miRNA or methylation study
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# This is cell line data, not human subject data
trait_row = None
def convert_trait(x):
return None
age_row = None
def convert_age(x):
return None
gender_row = None
def convert_gender(x):
return None
# 3. Save metadata
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. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(gene_data.index[:20])
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)
# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 1. Identify relevant columns for gene mapping
# 'ID' in gene annotation matches identifiers in gene expression data
# 'gene_assignment' contains gene symbol information
# 2. Extract gene mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
# 3. Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping)
# 1. Normalize gene symbols and save gene data
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. Create minimal linked data structure
linked_data = pd.DataFrame(index=gene_data.columns)
# 3-4. Skip missing value handling since data is not usable
# Mark as biased since we have no trait data
is_biased = True
# 5. Final validation and save metadata
validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=is_biased,
df=linked_data,
note="This is a cell line experiment, not a human subject study. Contains no trait data."
)
# 6. Skip saving linked data since it's not usable |