File size: 4,693 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE222124"
# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222124"
# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE222124.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE222124.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE222124.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 availability
is_gene_available = True # This is expression data from cell lines, not miRNA/methylation
# 2.1 Data availability
# Row 0 shows leukemia cell lines, can be used for trait
trait_row = 0
# Age and gender not provided for cell lines
age_row = None
gender_row = None
# 2.2 Data type conversion functions
def convert_trait(value):
if not value or ':' not in value:
return None
cell_type = value.split(': ')[1].lower()
if 'monocytic leukemia' in cell_type:
return 1 # Acute monocytic leukemia
elif 't cell leukemia' in cell_type:
return 0 # Other leukemia types
elif 'natural killer cell leukemia' in cell_type:
return 0
return None
def convert_age(value):
pass # Not used since age_row is None
def convert_gender(value):
pass # Not used since gender_row is 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. Clinical feature extraction
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
)
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))
clinical_features.to_csv(out_clinical_data_file)
# 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))
# Extract probe-to-gene mapping from annotation
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, gene_mapping)
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate data quality and save metadata
# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined
# based on cell subtypes (AMKL vs non-AMKL).
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="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |