File size: 6,373 Bytes
6f366b0 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Substance_Use_Disorder"
cohort = "GSE138297"
# Input paths
in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE138297"
# Output paths
out_data_file = "./output/preprocess/3/Substance_Use_Disorder/GSE138297.csv"
out_gene_data_file = "./output/preprocess/3/Substance_Use_Disorder/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/preprocess/3/Substance_Use_Disorder/clinical_data/GSE138297.csv"
json_path = "./output/preprocess/3/Substance_Use_Disorder/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)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene Expression Data Availability
# Based on background info, this is a microarray study of gene expression in colon biopsies
is_gene_available = True
# 2.1 Data Availability
# Key identification:
# Trait - experimental condition (row 6) can be used to define case/control
trait_row = 6
# Age - age in years available (row 3)
age_row = 3
# Gender - sex data available (row 1)
gender_row = 1
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if not value or ':' not in value:
return None
condition = value.split(': ')[1].strip()
# Autologous FMT (self) as control (0), Allogenic FMT (from donor) as case (1)
if 'Autologous' in condition:
return 0
elif 'Allogenic' in condition:
return 1
return None
def convert_age(value):
if not value or ':' not in value:
return None
try:
return float(value.split(': ')[1])
except:
return None
def convert_gender(value):
if not value or ':' not in value:
return None
try:
# Data already coded as female=1, male=0
# Need to reverse to match our convention (female=0, male=1)
gender_val = int(value.split(': ')[1])
return 1 - gender_val # Reverse the coding
except:
return None
# 3. Save initial 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. Extract and save clinical features
if trait_row is not None:
selected_clinical = 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(selected_clinical)
print("Preview of selected clinical features:", preview)
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The IDs in the data (e.g. 16650001, 16650003 etc.) appear to be probe/array IDs
# and not standard human gene symbols like BRCA1, TP53 etc.
# This indicates we need to map these IDs to their corresponding gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)
# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. Identify the probe ID column and gene symbol column
# From the preview, 'ID' matches the gene expression data identifiers
# The gene symbols are in 'gene_assignment' column
# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# 3. Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# 4. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
# Preview results
print("Shape after mapping:", gene_data.shape)
print("\nFirst few genes and samples:")
print(gene_data.iloc[:5, :5])
# 1. Normalize gene symbols in gene expression 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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)
# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)
# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)
# 3. Handle missing values systematically
if trait in linked_data.columns:
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and information saving
note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
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=trait_biased,
df=linked_data,
note=note
)
# 6. Save linked data only if usable and not biased
if is_usable and not trait_biased:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |