File size: 5,466 Bytes
187fbda |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Asthma"
cohort = "GSE270312"
# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE270312"
# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE270312.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE270312.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE270312.csv"
json_path = "./output/preprocess/1/Asthma/cohort_info.json"
# STEP 1
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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# Step 1: Gene Expression Data Availability
# Based on the background stating "RNA transcriptome responses" were measured, we consider it gene expression data.
is_gene_available = True
# Step 2: Variable Availability and Conversion
# 2.1 Identify rows for trait, age, and gender
# From the sample characteristics dictionary, 'asthma status' = row 3, 'gender' = row 2.
# No age information is provided.
trait_row = 3
age_row = None
gender_row = 2
# 2.2 Define data conversion functions
def convert_trait(value: str):
# Example: "asthma status: Yes"
# Split by colon, then strip extra spaces
parts = value.split(":")
if len(parts) < 2:
return None
val = parts[1].strip().lower()
if val == "yes":
return 1
elif val == "no":
return 0
return None
def convert_age(value: str):
# No age data available, so return None
return None
def convert_gender(value: str):
# Example: "gender: Female"
parts = value.split(":")
if len(parts) < 2:
return None
val = parts[1].strip().lower()
if val == "female":
return 0
elif val == "male":
return 1
return None
# Step 3: Save Metadata (initial filtering)
# Trait data is considered available if we have a valid row for it
is_trait_available = (trait_row is not None)
filter_pass = 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
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 the selected clinical features
preview_clinical = preview_df(selected_clinical_df)
# (You could print the preview or store it if needed; omitted here for brevity.)
# Save the clinical data
selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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])
# Based on the observed gene identifiers such as ABCF1, ACE, ACKR2, etc.,
# these appear to be valid human gene symbols and do not require additional mapping.
print("These genes are human gene symbols.")
# Conclusion
print("\nrequires_gene_mapping = False")
# STEP 7: Data Normalization and Linking
# 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)
print(f"Saved normalized gene data to {out_gene_data_file}")
# 2. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait_col=trait)
# 4. Determine whether the trait/demographic features are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
# 5. Conduct final quality validation and save metadata
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="Trait data and gene data successfully linked."
)
# 6. If the dataset is deemed usable, save the final linked data as a CSV file
if is_usable:
linked_data.to_csv(out_data_file)
print(f"Saved final linked data to {out_data_file}")
else:
print("Dataset was not deemed usable; final linked data not saved.") |