File size: 4,076 Bytes
6b8ee1b | 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 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Depression"
cohort = "GSE138297"
# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE138297"
# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE138297.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE138297.csv"
json_path = "./output/z2/preprocess/Depression/cohort_info.json"
# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)
# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# Step 2: Dataset Analysis and Clinical Feature Extraction
# Step 1: Determine gene expression availability
is_gene_available = True # Microarray analysis on sigmoid biopsies indicates gene expression data
# Step 2: Identify rows and define converters
trait_row = None # No Depression-related data available in this cohort
age_row = 3
gender_row = 1
def convert_trait(x):
# Trait (Depression) not available in this dataset
return None
def convert_age(x):
try:
# Extract value after colon
val = str(x).split(":", 1)[1].strip()
except Exception:
val = str(x).strip()
# Handle missing/unknown
if val in {"", "NA", "N/A", "nan", "NaN", None}:
return None
# Convert to float
try:
return float(val)
except Exception:
return None
def convert_gender(x):
s = str(x)
# Extract value after colon, but keep header for potential mapping hints
parts = s.split(":", 1)
header = parts[0].lower() if parts else ""
val = parts[1].strip() if len(parts) > 1 else s.strip()
vlow = val.lower()
# Direct string mapping
if any(k in vlow for k in ["female", "f"]):
return 0
if any(k in vlow for k in ["male", "m"]):
return 1
# Numeric mapping with hint in header (female=1, male=0)
if "female=1" in header and "male=0" in header:
if val == "1":
return 0 # female -> 0
if val == "0":
return 1 # male -> 1
# Fallback: try common encodings
if val in {"0", "1"}:
# Without reliable header, assume 0=male, 1=female then convert to required scheme female=0, male=1
# But given this dataset includes header, this path is unlikely.
return 1 if val == "0" else 0
return None
# Step 3: Initial filtering and save metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available
)
# Step 4: Clinical feature extraction (skip because trait_row is None)
# If in future trait_row becomes available, the following pattern should be used:
# selected_clinical_df = 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 = preview_df(selected_clinical_df)
# selected_clinical_df.to_csv(out_clinical_data_file) |