output/preprocess/Depression/code/GSE201332.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE201332"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE201332"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE201332.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE201332.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE201332.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
import re
import os

# 1) Gene Expression Data Availability
is_gene_available = True  # "Transcriptional profiling" of whole blood for DEGs indicates mRNA expression data.

# 2) Variable Availability and Converters
trait_row = 1   # 'subject status: heathy controls' vs 'subject status: MDD patients'
age_row = 3     # 'age: 43y', etc.
gender_row = 2  # 'gender: male' / 'gender: female'

def _after_colon(val):
    if val is None:
        return None
    s = str(val).strip()
    if ':' in s:
        s = s.split(':', 1)[1].strip()
    return s

def convert_trait(val):
    s = _after_colon(val)
    if s is None or s == '':
        return None
    s_low = s.lower()
    # Map MDD/depression to 1, controls/healthy to 0
    if any(k in s_low for k in ['mdd', 'depress']):
        return 1
    if any(k in s_low for k in ['control', 'healthy', 'normal', 'hc']):
        return 0
    return None

def convert_age(val):
    s = _after_colon(val)
    if s is None or s == '':
        return None
    m = re.search(r'(\d+(\.\d+)?)', s)
    if m:
        num = float(m.group(1))
        return num
    return None

def convert_gender(val):
    s = _after_colon(val)
    if s is None or s == '':
        return None
    s_low = s.lower()
    if s_low in ['male', 'm']:
        return 1
    if s_low in ['female', 'f']:
        return 0
    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 (only if clinical data is available)
if trait_row is not None:
    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)
    print(preview)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# Step 3: Gene Data Extraction
# 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])

# Step 4: Gene Identifier Review
# The observed identifiers are numeric (e.g., '1', '2', ...), consistent with Entrez Gene IDs, not human gene symbols.
requires_gene_mapping = True
print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Gene Annotation
# 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 6: Gene Identifier Mapping
# Determine the probe ID column and candidate gene symbol columns
probe_col = 'ID' if 'ID' in gene_annotation.columns else None
if probe_col is None:
    raise ValueError("Probe ID column 'ID' was not found in the gene annotation dataframe.")

# Candidate columns that may contain gene symbols or descriptions from which symbols can be extracted
candidate_gene_cols = [
    'GENE_SYMBOL', 'Gene Symbol', 'Symbol', 'SYMBOL', 'Gene', 'GENE',
    'GENE_NAME', 'Gene Name', 'GENE_TITLE', 'GENE TITLE', 'GENE_SYMBOLS',
    'DESCRIPTION', 'DEFINITION', 'Product', 'PRODUCT', 'RefSeq', 'REFSEQ',
    'ENTREZ_GENE_ID', 'ENTREZID', 'GB_ACC', 'SEQ_ACC', 'ORF', 'ACCNUM',
    'SPOT_ID', 'NAME', 'SEQUENCE', 'CHROMOSOMAL_LOCATION'
]
present_gene_cols = [c for c in candidate_gene_cols if c in gene_annotation.columns]

if not present_gene_cols:
    # Fallback: try any non-ID textual columns
    present_gene_cols = [c for c in gene_annotation.columns if c != probe_col]

# Score candidate columns by how many rows yield at least one human gene symbol
best_col = None
best_count = -1
for c in present_gene_cols:
    try:
        tmp_map = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=c)
    except Exception:
        continue
    if tmp_map.empty:
        continue
    # Restrict to probes present in the expression data
    tmp_map = tmp_map[tmp_map['ID'].isin(gene_data.index)]
    if tmp_map.empty:
        continue
    # Count rows with at least one extracted human gene symbol
    count_nonempty = tmp_map['Gene'].apply(extract_human_gene_symbols).apply(lambda x: len(x) if isinstance(x, list) else 0).gt(0).sum()
    if count_nonempty > best_count:
        best_count = count_nonempty
        best_col = c

if best_col is None or best_count <= 0:
    # As a last resort, use 'NAME' if available, otherwise raise an error
    if 'NAME' in gene_annotation.columns:
        best_col = 'NAME'
    else:
        raise ValueError("Could not identify a suitable annotation column containing gene symbols.")

# Build final mapping using the selected column
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=best_col)

# Convert probe-level data to gene-level expression using the mapping
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data (fix variable name)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Bias assessment and removal of biased demographics
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata
is_usable = validate_and_save_cohort_info(
    True,
    cohort,
    json_path,
    True,
    True,
    is_trait_biased,
    unbiased_linked_data,
    note="INFO: Probes mapped to symbols via annotation; symbols normalized using NCBI synonyms."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)

# Step 8: Gene Identifier Mapping
import json
import re

# Reload raw expression data to ensure probe IDs
raw_expression_df = get_genetic_data(matrix_file)

# 1) Identify identifier column in annotation
probe_col = 'ID' if 'ID' in gene_annotation.columns else None
if probe_col is None:
    raise ValueError("Probe ID column 'ID' was not found in the gene annotation dataframe.")

# Probes present in expression data
expr_probe_ids = set(raw_expression_df.index.astype(str))

# Exclude known control probes if CONTROL_TYPE is present
if 'CONTROL_TYPE' in gene_annotation.columns:
    control_flags = gene_annotation['CONTROL_TYPE'].astype(str).str.lower()
    non_control_mask = ~control_flags.isin(['pos', 'neg', 'control', 'empty', 'ignore'])
    non_control_ids = set(gene_annotation.loc[non_control_mask, probe_col].astype(str))
else:
    non_control_ids = set(gene_annotation[probe_col].astype(str))

valid_probe_ids = expr_probe_ids.intersection(non_control_ids)

# 2) Select the best annotation column containing gene symbols/descriptors
candidate_gene_cols = [
    'GENE_SYMBOL', 'Gene Symbol', 'Symbol', 'SYMBOL', 'Gene', 'GENE',
    'GENE_NAME', 'Gene Name', 'GENE_TITLE', 'GENE TITLE', 'GENE_SYMBOLS',
    'DESCRIPTION', 'DEFINITION', 'Product', 'PRODUCT', 'RefSeq', 'REFSEQ',
    'ENTREZ_GENE_ID', 'ENTREZID', 'GB_ACC', 'SEQ_ACC', 'ORF', 'ACCNUM',
    'NAME', 'SEQUENCE', 'SPOT_ID', 'CHROMOSOMAL_LOCATION'
]
present_gene_cols = [c for c in candidate_gene_cols if c in gene_annotation.columns]
if not present_gene_cols:
    present_gene_cols = [c for c in gene_annotation.columns if c != probe_col]

# Load synonym dictionary to score columns
with open("./metadata/gene_synonym.json", "r") as f:
    synonym_dict = json.load(f)
synonym_keys = set(synonym_dict.keys())

# Token exclusion patterns (spike-ins, controls, generic RNA placeholders)
exclude_exact = {"GE_BRIGHTCORNER", "DARKCORNER", "EMPTY", "CONTROL", "NEG", "POS"}
exclude_regex = [
    re.compile(r'^ERCC[\w-]*$', re.IGNORECASE),
    re.compile(r'^RNA\d+$', re.IGNORECASE),
    re.compile(r'^RNA\d+-\d+$', re.IGNORECASE),
    re.compile(r'^NEG[\w-]*$', re.IGNORECASE),
    re.compile(r'^POS[\w-]*$', re.IGNORECASE),
]

def filter_tokens(tokens):
    kept = []
    for t in tokens:
        if not isinstance(t, str):
            continue
        u = t.upper()
        if u in exclude_exact:
            continue
        if any(rx.match(u) for rx in exclude_regex):
            continue
        # Only keep tokens recognized by synonym dictionary
        if u in synonym_keys:
            kept.append(u)
    return kept

def score_column(col_name):
    tmp_map = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=col_name)
    if tmp_map.empty:
        return 0, set()
    tmp_map = tmp_map[tmp_map['ID'].astype(str).isin(valid_probe_ids)]
    if tmp_map.empty:
        return 0, set()
    extracted = tmp_map['Gene'].apply(extract_human_gene_symbols)
    # Filter symbols
    filtered_lists = extracted.apply(filter_tokens)
    # Count unique recognized symbols
    uniq_syms = set(sym for lst in filtered_lists if isinstance(lst, list) for sym in lst)
    return len(uniq_syms), uniq_syms

# First pass: score all present columns
scores = {}
uniq_syms_by_col = {}
for c in present_gene_cols:
    cnt, uniq = score_column(c)
    scores[c] = cnt
    uniq_syms_by_col[c] = uniq

# Choose the best column by recognized count
best_col = max(scores, key=lambda k: scores[k]) if scores else None
best_count = scores.get(best_col, 0) if best_col is not None else 0

# Enforce fallback strategy if no recognized symbols
if best_count <= 0:
    for fallback in ['NAME', 'SEQUENCE']:
        if fallback in gene_annotation.columns:
            cnt, uniq = score_column(fallback)
            if cnt > 0:
                best_col = fallback
                best_count = cnt
                uniq_syms_by_col[best_col] = uniq
                break

# As a safety, avoid SPOT_ID unless it yields recognized symbols
if (best_col is None) or (best_count <= 0) or (best_col == 'SPOT_ID' and best_count <= 0):
    raise ValueError("Could not identify an annotation column that yields recognized human gene symbols.")

print(f"Selected identifier column: {probe_col}")
print(f"Selected gene annotation column: {best_col} (recognized_symbols={best_count})")

# 3) Build mapping and apply to convert probes -> genes, with explicit filtering
mapping_df_raw = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=best_col)
mapping_df_raw = mapping_df_raw[mapping_df_raw['ID'].astype(str).isin(valid_probe_ids)].copy()

# Extract and filter tokens per row
extracted = mapping_df_raw['Gene'].apply(extract_human_gene_symbols)
filtered_tokens = extracted.apply(filter_tokens)

# Keep rows that have at least one recognized, non-excluded symbol
keep_mask = filtered_tokens.apply(lambda lst: isinstance(lst, list) and len(lst) > 0)
mapping_df_filtered = mapping_df_raw.loc[keep_mask, ['ID']].copy()
# Join tokens back to a single string so that apply_gene_mapping can re-extract correctly
mapping_df_filtered['Gene'] = filtered_tokens.loc[keep_mask].apply(lambda lst: ';'.join(lst))

print(f"Mapping dataframe shape after filtering: {mapping_df_filtered.shape}")

# Show a small sample of recognized symbols we will map
recognized_syms_sample = sorted(list(set(sym for lst in filtered_tokens.loc[keep_mask] for sym in lst)))[:15]
print(f"Sample of recognized symbols to be mapped: {recognized_syms_sample}")

if mapping_df_filtered.empty:
    raise ValueError("Derived mapping_df is empty after filtering; cannot map probes to gene symbols.")

gene_data = apply_gene_mapping(expression_df=raw_expression_df, mapping_df=mapping_df_filtered)

print(f"Gene-level expression shape: {gene_data.shape}")
print(f"First 10 genes mapped: {list(gene_data.index[:10])}")
if gene_data.empty:
    raise ValueError("Resulting gene_data is empty after applying mapping.")