File size: 5,911 Bytes
a3c6344 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Allergies"
cohort = "GSE84046"
# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE84046"
# Output paths
out_data_file = "./output/preprocess/1/Allergies/GSE84046.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE84046.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE84046.csv"
json_path = "./output/preprocess/1/Allergies/cohort_info.json"
# STEP1
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)
# 1. Gene Expression Data Availability
is_gene_available = True # The study clearly mentions "whole genome gene expression" in adipose tissue.
# 2. Variable Availability
# Trait (Allergies): Not found in the sample characteristics.
trait_row = None
# Age: We can infer from date of birth, which is stored under key=5 "date of birth".
age_row = 5
# Gender: Found under key=4 "sexe: Male/Female".
gender_row = 4
# 2.2 Data Type Conversion Functions
def convert_trait(value: str):
# No trait data, so return None.
return None
def convert_age(value: str):
# Example format: "date of birth (dd-mm-yyyy): 1952-06-17"
# We parse out '1952-06-17' and convert it to approximate age.
try:
date_str = value.split(':', 1)[1].strip() # e.g. "1952-06-17"
birth_year = int(date_str.split('-')[0])
# Approximate age by subtracting from current year
approx_age = 2023 - birth_year
if approx_age < 0 or approx_age > 120:
return None
return float(approx_age)
except:
return None
def convert_gender(value: str):
# Example format: "sexe: Male" or "sexe: Female"
try:
gender_str = value.split(':', 1)[1].strip().lower()
if gender_str == "male":
return 1
elif gender_str == "female":
return 0
else:
return None
except:
return None
# 3. Save Metadata (Initial Filtering)
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
)
# 4. Clinical Feature Extraction
# Since trait_row is None, we skip extracting clinical features for the trait "Allergies".
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)
# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# The gene identifiers in the provided index are numeric, suggesting they are not standard human gene symbols.
# These likely need to be mapped to gene symbols.
requires_gene_mapping = True
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)
# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping
# 1. Decide which columns in 'gene_annotation' match the probe IDs in 'gene_data' (i.e., "ID")
# and which column contains text that can lead to actual gene symbols (i.e., "gene_assignment").
prob_col = "ID"
gene_col = "gene_assignment"
# 2. Get a gene mapping dataframe using these two columns.
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
# 3. Apply the gene mapping to convert probe-level data to gene-level data.
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
# STEP 7: Data Normalization and Linking
# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file, index=True)
# 2. Since trait data is missing, skip linking clinical and genetic data,
# skip missing-value handling and bias detection for the trait.
# 3. Conduct final validation and record info.
# Since trait data is unavailable, set is_trait_available=False,
# pass a dummy/empty DataFrame and is_biased=False (it won't be used).
dummy_df = pd.DataFrame()
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=False,
df=dummy_df,
note="No trait data found; skipped clinical-linking steps."
)
# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
if is_usable:
dummy_df.to_csv(out_data_file, index=True) |